Carbon emission calculation method, system, device and storage medium

Abstract

The application discloses a carbon emission calculation method, system, device and storage medium, relates to the technical field of intelligent transportation and environmental calculation, and comprises the following steps: acquiring road real-time data and vehicle real-time data; performing data alignment and feature enhancement on the road real-time data and the vehicle real-time data to obtain an enhanced feature set; identifying a target vehicle type, matching a preset carbon emission calculation model corresponding to the target vehicle type, and inputting the enhanced feature set to obtain an initial carbon emission calculation result; and adjusting the initial carbon emission calculation result according to a current vehicle operating state to obtain a final carbon emission calculation result, so that the calculation error of carbon emission can be reduced, and the carbon emission calculation accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation and environmental computing technology, and in particular to carbon emission calculation methods, systems, devices and storage media. Background Technology

[0002] Accurate vehicle carbon emission accounting is the core foundation for carbon management and carbon verification. Current mainstream carbon emission accounting correction schemes generally suffer from large calculation errors. Existing schemes require companies to provide GPS travel data, and data acquisition is highly dependent on company cooperation, which can easily lead to information gaps and calculation deviations. The accounting process requires collecting environmental data from multiple road segments, which is cumbersome and prone to introducing additional errors. The relevant calculation models only perform calculations based on fixed road parameters, which cannot adapt to the changing driving scenarios of vehicles, further exacerbating the deviation between carbon emission accounting values ​​and actual emissions.

[0003] Therefore, there is an urgent need for a carbon emission calculation method that aims to reduce the calculation error of carbon emissions.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a carbon emission calculation method, system, device, and storage medium, aiming to solve the technical problem of how to reduce the calculation error of carbon emissions.

[0006] To achieve the above objectives, this application proposes a carbon emission calculation method, which includes: Acquire real-time road and vehicle data; Data alignment and feature enhancement are performed on the real-time road data and real-time vehicle data to obtain an enhanced feature set; Identify the target vehicle type, match the preset carbon emission calculation model corresponding to the target vehicle type, and input the enhanced feature set to calculate the initial carbon emission calculation result; Based on the current operating status of the vehicle, the initial carbon emission calculation results are adjusted to obtain the final carbon emission calculation results.

[0007] In one embodiment, the step of performing data alignment and feature enhancement on the real-time road data and real-time vehicle data to obtain an enhanced feature set includes: A sliding window mechanism is used to match the timestamps of the real-time road data and the real-time vehicle data to obtain time-aligned real-time road data and real-time vehicle data. Based on the time-aligned real-time road data, calculate the road load factor and traffic penalty factor; The enhanced feature set is constructed by combining the time-aligned real-time road data, real-time vehicle data, road load factor, and traffic penalty factor.

[0008] In one embodiment, the target vehicle type includes fuel-powered vehicles and electric vehicles, and the preset carbon emission calculation model includes an electric carbon emission model and a fuel-powered carbon emission model; The steps of identifying the target vehicle type, matching the target vehicle type with the corresponding preset carbon emission calculation model, and inputting the enhanced feature set to calculate the initial carbon emission calculation result include: If the target vehicle is a gasoline-powered vehicle, then the enhanced feature set is input into the gasoline carbon emission model to calculate the initial carbon emission calculation result; If the target vehicle is an electric vehicle, the enhanced feature set is input into the electric carbon emission model to calculate the initial carbon emission calculation result.

[0009] In one embodiment, the step of inputting the enhanced feature set into the fuel carbon emission model to calculate the initial carbon emission calculation result includes: Based on the engine displacement, speed and load coefficient in the enhanced feature set, the basic fuel consumption is calculated in combination with the preset calculation coefficient. Based on the road slope parameters and real-time congestion coefficient in the enhanced feature set, slope correction terms and traffic correction terms are obtained respectively, and the base fuel consumption is corrected based on the slope correction terms and traffic correction terms. The baseline carbon emission calculation results are obtained by combining the preset fuel type coefficient and the corrected baseline fuel consumption. When a special operating condition of a vehicle in real time is identified, a corresponding dynamic correction mechanism is matched to compensate and correct the basic carbon emission calculation results to obtain the initial carbon emission calculation results for the fuel vehicle.

[0010] In one embodiment, the step of inputting the enhanced feature set into the electric vehicle carbon emission model to obtain the initial carbon emission calculation result includes: Based on the real-time vehicle speed data in the enhanced feature set, and combined with the preset speed range graded energy consumption benchmark, the basic energy consumption data corresponding to the target vehicle is determined. Based on the congestion level, road slope parameters, vehicle weight, and ambient temperature parameters in the enhanced feature set, congestion correction terms, slope correction terms, and temperature correction terms are obtained respectively, and the basic energy consumption data is corrected based on the congestion correction terms, slope correction terms, and temperature correction terms. By combining the real-time acquired grid carbon emission factor and the corrected basic energy consumption data, the initial carbon emission calculation result corresponding to the electric vehicle is obtained.

[0011] In one embodiment, the target vehicle type is a hybrid vehicle, and the carbon emission calculation method includes: Identify the current battery level of the target vehicle within the enhanced feature set; When the current battery charge is higher than a preset threshold, the electric mode operation ratio of the target vehicle is calculated by a preset allocation formula, and the operation ratio allocation between the electric mode and the fuel mode of the vehicle is determined accordingly. Based on the proportion of electric mode and fuel mode, the carbon emission values ​​of the target vehicle in electric mode and fuel mode are calculated respectively. The carbon emission calculation results are then fused using a weighted average algorithm to obtain the initial carbon emission calculation result for the hybrid vehicle.

[0012] In one embodiment, the step of adjusting the initial carbon emission calculation result based on the current operating status of the vehicle to obtain the final carbon emission calculation result includes: When a vehicle acceleration in the enhanced feature set is detected to be greater than a preset acceleration threshold and the duration exceeds a first preset duration, the initial carbon emission calculation results for the corresponding time period are adjusted upwards in stages to obtain the final carbon emission calculation results under rapid acceleration conditions; and / or When a vehicle in the enhanced feature set is detected to have a speed of zero for a duration exceeding a second preset duration, the initial carbon emission calculation result is corrected using a step-down adjustment strategy based on the vehicle's idling time, resulting in the final carbon emission calculation result under long idling conditions; and / or When the engine coolant temperature in the enhanced feature set is detected to be lower than the preset temperature threshold, the initial carbon emission calculation result is smoothed and corrected according to the engine coolant temperature matching dynamic compensation coefficient and combined with the preset gradual recovery period to obtain the final carbon emission calculation result under cold start condition.

[0013] In one embodiment, after adjusting the initial carbon emission calculation result based on the current operating status of the vehicle to obtain the final carbon emission calculation result, the step includes: The final carbon emission calculation results are aggregated and statistically analyzed from multiple dimensions, including time, space, and vehicle, to obtain multi-dimensional carbon emission statistics. Based on the Gaode Map API interface, the multidimensional carbon emission statistics are rendered into a heat map, high emission areas are marked, and driving scores and personalized suggestions are generated simultaneously.

[0014] In one embodiment, after the step of adjusting the initial carbon emission calculation result based on the current operating status of the vehicle to obtain the final carbon emission calculation result, the method further includes: When an abnormal system signal is detected, the Gaode Map benchmark calculation mode is triggered. In the Gaode Map benchmark calculation mode, if the system anomaly signal is an abnormal slope data, the inertial measurement data of the inertial measurement unit is retrieved to replace the abnormal slope data, thereby correcting the final carbon emission calculation value. If the system anomaly signal is a network latency anomaly, carbon emission calculation is performed using locally cached data. If the abnormal signal of the system is the loss of the on-board diagnostic system signal, auxiliary calculations are performed, and the carbon emission calculation results are corrected in conjunction with a preset compensation mechanism.

[0015] Furthermore, to achieve the above objectives, this application also proposes a carbon emission calculation system, which includes: The data acquisition module is used to acquire real-time road data and real-time vehicle data; The data processing module is used to perform data alignment and feature enhancement on the real-time road data and real-time vehicle data to obtain an enhanced feature set; The carbon emission calculation module is used to identify the target vehicle type, match the preset carbon emission calculation model corresponding to the target vehicle type, and input the enhanced feature set to calculate the initial carbon emission calculation result; The carbon emission correction module is used to adjust the initial carbon emission calculation result according to the current operating status of the vehicle to obtain the final carbon emission calculation result.

[0016] In addition, to achieve the above objectives, this application also proposes a carbon emission calculation device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the carbon emission calculation method as described above.

[0017] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the carbon emission calculation method described above.

[0018] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the carbon emission calculation method described above.

[0019] This application proposes a carbon emission calculation method, system, device, and storage medium. The method includes: acquiring real-time road data and real-time vehicle data; performing data alignment and feature enhancement on the real-time road data and real-time vehicle data to obtain an enhanced feature set; identifying the target vehicle type, matching the target vehicle type with a preset carbon emission calculation model, and inputting the enhanced feature set to calculate an initial carbon emission calculation result; adjusting the initial carbon emission calculation result according to the current vehicle operating status to obtain the final carbon emission calculation result. This solution, by integrating real-time road data and real-time vehicle parameters, combined with a dynamic carbon emission calculation model, overcomes the calculation bias problems caused by the single data dimension and poor model universality of traditional calculation methods, effectively reducing the basic carbon emission calculation error under complex road conditions. Simultaneously, by dynamically correcting the initial carbon emission result based on the real-time vehicle operating status, it accurately adapts and compensates for emission fluctuations caused by real-time vehicle driving conditions, further improving the accuracy and realism of the carbon emission calculation result and significantly reducing the carbon emission measurement error in real-world driving scenarios. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the carbon emission calculation method of this application in Embodiment 1. Figure 2 This is a flowchart illustrating Embodiment 2 of the carbon emission calculation method of this application; Figure 3 This is a flowchart illustrating Embodiment 3 of the carbon emission calculation method of this application; Figure 4 This is a flowchart illustrating Embodiment 4 of the carbon emission calculation method of this application; Figure 5 This is an architecture diagram of the carbon emission calculation system according to an embodiment of this application; Figure 6 This is an abbreviated flowchart of the carbon emission calculation method in the embodiments of this application; Figure 7 This is a schematic diagram of the module structure of the carbon emission calculation system according to an embodiment of this application; Figure 8This is a schematic diagram of the equipment structure of the hardware operating environment involved in the carbon emission calculation method in this application embodiment.

[0023] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0025] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0026] The main solution of this application embodiment is as follows: acquire real-time road data and real-time vehicle data; perform data alignment and feature enhancement on the real-time road data and real-time vehicle data to obtain an enhanced feature set; identify the target vehicle type, match the preset carbon emission calculation model corresponding to the target vehicle type, and input the enhanced feature set to calculate the initial carbon emission calculation result; adjust the initial carbon emission calculation result according to the current vehicle operating status to obtain the final carbon emission calculation result.

[0027] In this embodiment, for ease of description, the carbon emission calculation system will be used as the execution subject in the following description.

[0028] Because existing technologies require companies to open up their GPS travel data, data acquisition is highly dependent on company cooperation, which can easily lead to missing information and calculation errors. The calculation process requires splitting multiple road segments to collect environmental data, which is cumbersome and can easily introduce additional errors. In addition, the relevant calculation models only perform calculations based on fixed road parameters, which cannot adapt to the changing driving scenarios of vehicles, further exacerbating the deviation between carbon emission calculation values ​​and actual emissions.

[0029] This application provides a solution that integrates high-precision road feature data from Gaode Maps with real-time vehicle operating parameters from onboard OBD, combined with a dynamic carbon emission calculation model, to achieve centimeter-level road network matching and second-level carbon emission monitoring. Compared to traditional estimation methods based on average speed, this reduces the calculation error from over ±20% to within ±7%, while also supporting mixed calculation scenarios for both gasoline and electric vehicles.

[0030] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions. The following description uses a personal computer as an example to illustrate this embodiment and the subsequent embodiments.

[0031] Based on this, the embodiments of this application provide a carbon emission calculation method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the carbon emission calculation method of this application.

[0032] In this embodiment, the carbon emission calculation method includes steps S10 to S40: Step S10: Obtain real-time road data and real-time vehicle data; In this embodiment, the Gaode Map API interface is called to obtain real-time road data, including but not limited to characteristic parameters such as slope, curvature, and congestion, for subsequent calculation of carbon emission indicators. Simultaneously, the on-board diagnostics (OBD) interface is called to obtain real-time vehicle data, including but not limited to characteristic data such as engine speed, load, vehicle speed, and instantaneous fuel consumption, which are also used for subsequent carbon emission indicator calculations.

[0033] Step S20: Perform data alignment and feature enhancement on the real-time road data and real-time vehicle data to obtain an enhanced feature set; In this embodiment, during the data alignment and feature enhancement process, a sliding time window algorithm is used to match and calibrate the timestamps of real-time road data and real-time vehicle data. This achieves synchronous alignment of the time dimension of multi-source heterogeneous data, eliminates timing deviations caused by differences in data acquisition frequencies from different devices and interfaces, and ensures the consistency of data timing in subsequent feature calculations. After data alignment is completed, data feature enhancement processing is performed by quantifying road driving resistance characteristics and traffic congestion impact characteristics. Specifically, this includes calculating two core feature parameters: the road load coefficient and the traffic penalty factor.

[0034] Optionally, step S20 may include steps S21 to S23: Step S21: Use a sliding window mechanism to match the timestamps of the real-time road data and the real-time vehicle data to obtain time-aligned real-time road data and real-time vehicle data. Since real-time vehicle data is typically collected at a frequency of 1Hz, while real-time road data is updated at a frequency of 0.5-1Hz, there is a difference in the collection frequency between the two types of data. Therefore, this example uses a sliding time window algorithm with a duration of 5 seconds to perform time-series matching between real-time vehicle data and real-time road data. Each frame of vehicle data is matched with the nearest Gaode Road data point within the time window to achieve time-series alignment of multi-source data. When the time difference between the two types of data exceeds 3 seconds, missing data points are estimated using linear interpolation to fill in the time-series gaps and ensure the integrity and continuity of the time-series data.

[0035] After temporal alignment is completed, spatial location matching processing is performed. Gaode's real-time road data is bound to fixed road coordinates and corresponding road attributes, including road slope, road curvature, and pavement type, while real-time vehicle data carries the vehicle's real-time GPS location coordinates. This embodiment uses a nearest neighbor search algorithm to map the vehicle's real-time GPS coordinates to the nearest target road segment in the Gaode road network, achieving precise spatial binding between vehicle location and road attribute data, and completing spatial data matching and fusion.

[0036] Furthermore, a fault-tolerance mechanism is set up for two typical data anomaly scenarios during driving: First, if no valid real-time vehicle data is collected for five consecutive times, it is determined that the OBD signal is lost, and the historical average road data of Gaode is switched as an alternative data source, with an additional 20% conservative compensation to avoid the carbon emission calculation result being too low; Second, when abnormal position jumps or GPS drift are detected in the vehicle's GPS coordinates, especially applicable to tunnel and elevated road obstruction scenarios, the IMU inertial measurement unit is activated to calculate the vehicle's short-term driving trajectory and correct the abnormal GPS position.

[0037] Through the above-mentioned temporal alignment, spatial matching, and fault tolerance processing of abnormal data, the vehicle behavior data and Gaode's multi-dimensional road data are integrated to construct a road information matrix that includes vehicle driving status and road condition attributes, providing a regular and reliable input data base for subsequent feature enhancement and carbon emission model calculation.

[0038] Step S22: Calculate the road load factor and traffic penalty factor based on the time-aligned real-time road data; The road load factor is calculated using a weighted fusion formula: Road load factor = α × (slope influence) + β × (curvature influence) + γ × (road surface influence) In the formula, α, β, and γ are preset feature weight coefficients used to adapt to the influence ratio of various road interference factors under different driving scenarios; the slope influence parameter is obtained by acquiring the road slope percentage in real time, and the road slope value range is -15% to 15%; the curvature influence parameter is obtained by normalizing the inverse of the curvature radius, and the curvature radius of different roads is uniformly mapped to the dimensionless range of 0-1 by the formula curvature = 1 / (radius+0.1); the pavement influence parameter is characterized by the difference in resistance of different pavement working conditions by acquiring pavement type codes of 1-5 levels.

[0039] The traffic penalty factor is calculated according to the following formula: Traffic penalty factor = Base congestion coefficient × (1 + Start-stop compensation) The system uses a multi-level basic congestion coefficient based on real-time road conditions. The basic congestion coefficient is 1.0 for smooth traffic, 1.2 for slow traffic, 1.5 for congested traffic, and 2.0 for severe congestion. Start-stop compensation is used to quantify the additional emission losses caused by frequent vehicle starts and stops. The traffic penalty factor is solved by combining the corresponding basic congestion coefficient.

[0040] Step S23: Combine the time-aligned real-time road data, real-time vehicle data, road load factor, and traffic penalty factor to construct the enhanced feature set.

[0041] In this embodiment, real-time vehicle data and real-time road data that have completed temporal and spatial matching are dimensionally spliced ​​and fused with the solved road load coefficient and traffic penalty factor to form standardized structured data, invalid and redundant data are removed, and a multi-dimensional enhanced feature set containing vehicle operating parameters, road slope curvature pavement attributes, and road congestion interference characteristics is formed.

[0042] Through the above steps, the temporal alignment, spatial matching, anomaly tolerance, feature quantization, and dimensional fusion of multi-source heterogeneous real-time vehicle data and real-time road data are completed. This effectively eliminates interference caused by inconsistent collection frequencies, spatial mismatches, and data anomalies from multiple data sources. Core features that can characterize road driving resistance and traffic congestion interference are extracted, and a highly accurate, highly adaptable, and highly stable enhanced feature set is constructed. This provides standardized and high-quality model input data for subsequent vehicle model matching modeling and accurate initial carbon emission calculation.

[0043] Step S30: Identify the target vehicle type, match the preset carbon emission calculation model corresponding to the target vehicle type, and input the enhanced feature set to calculate the initial carbon emission calculation result; It should be noted that the target vehicle types include, but are not limited to, gasoline-powered vehicles and electric vehicles. The preset carbon emission calculation model includes an electric carbon emission model and a gasoline carbon emission model; wherein, the electric carbon emission model is trained based on vehicle energy consumption, driving conditions, and load data, and the gasoline carbon emission model is trained based on engine operating conditions, fuel consumption, and road resistance data.

[0044] It is understandable that, due to the significant differences in the power drive principles and energy consumption and emission generation mechanisms between fuel vehicles and electric vehicles, a unified calculation model is difficult to adapt to the emission characteristics of the two types of vehicles. Therefore, by executing step S30, a dedicated calculation model can be matched according to the differences in vehicle type, avoiding the adaptation deviation caused by using a single general model, thereby achieving accurate calculation of the initial carbon emission results for different vehicle models.

[0045] In one feasible embodiment, step S30 may include steps S31-S32: Step S31: If the target vehicle is a gasoline vehicle, then the enhanced feature set is input into the gasoline carbon emission model to calculate the initial carbon emission calculation result. This step involves constructing a multi-factor dynamic calculation model to address the mechanism of carbon emissions generated by fuel combustion in gasoline vehicles. The model comprehensively considers engine operating parameters, road conditions, traffic conditions, and various special driving conditions to calculate emission values ​​in a layered iterative manner.

[0046] Optionally, step S31 may also include steps S311 to S314: Step S311: Based on the engine displacement, speed and load coefficient in the enhanced feature set, the basic fuel consumption is calculated in combination with the preset calculation coefficient. In this embodiment, a multi-factor dynamic calculation model is used to calculate the carbon emissions of fuel-powered vehicles. The basic calculation includes three core parts: basic fuel consumption, gradient correction term, and traffic correction term.

[0047] First, the preset calculation coefficient is set to 0.0018, and the basic fuel consumption of the vehicle is solved by coupling the engine displacement, speed and load coefficient.

[0048] Step S312: Based on the road slope parameters and real-time congestion coefficient in the enhanced feature set, obtain the slope correction term and traffic correction term respectively, and correct the basic fuel consumption based on the slope correction term and traffic correction term. In this embodiment, the slope correction term is adjusted synchronously at a rate of 5% based on the absolute value of the road slope; the traffic correction term is adjusted according to the real-time congestion coefficient carried by the enhanced feature set, thus completing the stratified correction of the basic fuel consumption.

[0049] Step S313: Combine the preset fuel type coefficient and the corrected basic fuel consumption to calculate the basic carbon emission calculation result. In this embodiment, a preset fuel type coefficient distinguishes between gasoline and diesel. The coefficient for gasoline is 2.31 kg CO2 / L, and the coefficient for diesel is 2.68 kg CO2 / L. The corrected basic fuel consumption is multiplied by the fuel type coefficient to obtain the basic carbon emission calculation result without special operating condition compensation.

[0050] Step S314: When a special operating condition of the vehicle in real time is identified, the corresponding dynamic correction mechanism is matched to compensate and correct the basic carbon emission calculation result to obtain the initial carbon emission calculation result corresponding to the fuel vehicle.

[0051] In this embodiment, the special disclosure includes three categories: cold start, rapid acceleration, and long idling. Under cold start conditions, when the coolant temperature is below 80°C, the overall emission value is multiplied by a correction factor of 1.3, and the compensation amount is refined through a temperature difference compensation formula. Under rapid acceleration conditions, when the vehicle acceleration exceeds 0.3 m / s², a segmented compensation algorithm is activated. Under long idling conditions, after the idling time exceeds 30 seconds, the load factor is reduced by 5% every 10 seconds, and the lower limit of the load factor is set to 0.7.

[0052] Through the above steps, the entire process of quantitative calculation of basic fuel consumption, road condition correction, fuel carbon emission conversion, and dynamic compensation under special operating conditions for fuel vehicles is realized, which fully covers various core variables affecting carbon emissions of fuel vehicles and improves the consistency and accuracy of the initial carbon emission calculation results for fuel vehicles.

[0053] Step S32: If the target vehicle is an electric vehicle, then the enhanced feature set is input into the electric carbon emission model to calculate the initial carbon emission calculation result.

[0054] This step addresses the characteristics of electric vehicles, which consume no fuel and rely primarily on grid electricity for emissions. It employs a benchmark energy consumption classification model to quantify energy consumption and carbon emissions, combining vehicle speed classification benchmarks, multi-condition dynamic corrections, and grid carbon emission factors to accurately adapt to the emission generation mechanism of pure electric vehicles.

[0055] Optionally, this step includes steps S321 to S323: Step S321: Based on the real-time vehicle speed data in the enhanced feature set and combined with the preset speed range graded energy consumption benchmark, determine the basic energy consumption data corresponding to the target vehicle. In this embodiment, the electric emission model is pre-divided into three energy consumption benchmark values ​​according to different vehicle speed ranges. The corresponding graded energy consumption benchmark is matched according to the real-time driving of the vehicle to obtain the basic power consumption data under the stable driving state of the vehicle, which serves as the basic value for calculating the energy consumption and carbon emissions of electric vehicles.

[0056] Step S322: Based on the congestion level, road slope parameters, vehicle weight and ambient temperature parameters in the enhanced feature set, obtain the congestion correction item, slope correction item and temperature correction item respectively, and correct the basic energy consumption data based on the congestion correction item, slope correction item and temperature correction item. In this embodiment, a multi-correction mechanism is set up to address the dynamic influencing factors of electric vehicle driving energy consumption: the congestion correction coefficient is dynamically adjusted according to the real-time road congestion level ratio to quantify the additional energy consumption caused by slow traffic and frequent start-stop operations; the vehicle weight parameter is introduced during the slope correction process to accurately characterize the energy consumption increase caused by the increased uphill load; the temperature correction distinguishes between low-temperature and high-temperature extreme environmental conditions to correct the energy consumption deviation caused by changes in air conditioning energy consumption and battery discharge efficiency, thereby achieving comprehensive dynamic compensation and correction of basic energy consumption data.

[0057] Step S323: Combine the real-time acquired grid carbon emission factor and the corrected basic energy consumption data to calculate the initial carbon emission calculation result corresponding to the electric vehicle.

[0058] In this embodiment, the basic energy consumption data after multi-dimensional operating condition correction is multiplied by the real-time updated grid carbon emission factor to convert the electric vehicle's power consumption into the corresponding carbon emissions, and finally the initial carbon emission calculation result corresponding to the pure electric vehicle is obtained.

[0059] In addition, this embodiment sets up an accuracy guarantee mechanism: the model parameters are calibrated in real time every five minutes. If the error of a single calculation exceeds the 10% threshold, the engine efficiency coefficient and road load parameters are automatically corrected. At the same time, abnormal data processing logic is provided, a compensation strategy is set for signal loss, and a Kalman filter algorithm is used to smooth the data with numerical mutations to maintain the stability and reliability of the input data.

[0060] Through the above steps, a standardized calculation process is achieved for electric vehicle speed-based benchmark energy consumption, multi-condition dynamic energy consumption correction, and equivalent conversion of grid carbon emissions. This process fully adapts to the energy consumption characteristics and driving condition changes of pure electric vehicles, effectively improving the accuracy and scenario adaptability of the initial carbon emission calculation results for pure electric vehicles.

[0061] Step S40: Adjust the initial carbon emission calculation result according to the current operating status of the vehicle to obtain the final carbon emission calculation result.

[0062] In this embodiment, by real-time detection of the target vehicle's operating status, dynamic adjustments are made to three typical high-emission operating conditions, significantly improving the accuracy of carbon emission calculations. This step focuses on specific compensation and correction for three typical high-emission deviation conditions: rapid acceleration, long idling, and cold start. This effectively eliminates the calculation bias of the traditional single linear calculation model and greatly improves the overall accuracy of carbon emission calculations under complex real-world driving scenarios.

[0063] Specifically, this step quantifies the combined interference of three core factors—road slope, road curvature, and traffic congestion—on vehicle energy consumption and emissions by constructing a comprehensive drag coefficient, thereby achieving a baseline correction for the initial carbon emission results. The comprehensive drag coefficient is calculated as follows: Comprehensive drag coefficient = α × slope coefficient + β × curvature coefficient + γ × congestion coefficient, where α is the weight corresponding to the slope coefficient, β is the weight corresponding to the curvature coefficient, and γ is the weight corresponding to the congestion coefficient. The basic weight configuration is 80% for the slope coefficient and 20% for the curvature coefficient, which has been normalized to the 0-1 range. The coefficient value can be adaptively increased according to extreme road conditions such as steep slopes and sharp curves. The congestion coefficient is dynamically adapted to real-time traffic conditions, with a baseline factor of 1.0 for smooth traffic conditions and gradually increased to 1.6 for severely congested conditions, accurately representing the additional energy consumption and emission increases caused by vehicle idling and frequent start-stop operations.

[0064] Building upon this foundation, this solution employs an adaptive feature weighting mechanism for vehicles with different powertrains, adapting to the differentiated emission characteristics of various models: for gasoline vehicles operating under high loads, the gradient weight is adaptively increased to 90%, aligning with the high-load, high-emission operation characteristics of gasoline vehicles climbing hills; for electric vehicles, the congestion factor weight is adaptively configured to 75%, primarily correcting for energy consumption fluctuations caused by frequent start-stop cycles in congested traffic. Simultaneously, the original measured data, such as the radius of curvature, are dimensionlessly normalized to unify the dimensional standards of various feature parameters, ensuring that multi-dimensional features can collaboratively participate in model calculations. For complex extreme conditions such as continuous uphill driving combined with congestion, a nonlinear superposition correction strategy is adopted, effectively avoiding the underestimation of emission results that is prone to occur in traditional linear calculation methods, further improving the accuracy of calculations under extremely complex conditions.

[0065] In one feasible embodiment, step S40 may include steps S41 to S43: Step S41: When it is detected that the vehicle acceleration in the enhanced feature set is greater than the preset acceleration threshold and the duration exceeds the first preset duration, the initial carbon emission calculation results in the corresponding time period are adjusted upward in stages to obtain the final carbon emission calculation results under the rapid acceleration condition. Under rapid acceleration conditions, an acceleration threshold triggering mechanism is employed. Specifically, changes in vehicle acceleration are continuously monitored. When the detected acceleration exceeds a preset acceleration threshold (e.g., 0.3 m / s²) and persists for more than a first preset duration (e.g., 2 seconds or more), the carbon emission calculation result for that period is increased by 20%. The actual correction factor is graded according to acceleration intensity, with a maximum increase of 40%. The final carbon emission calculation result under rapid acceleration conditions is then obtained.

[0066] Step S42: When it is detected that the vehicle speed in the enhanced feature set is equal to zero and the duration exceeds the second preset duration, the initial carbon emission calculation result is corrected according to the vehicle idling time using a step-down strategy to obtain the final carbon emission calculation result under long idling conditions. Under prolonged idling conditions, priority is given to stationary waiting situations. Specifically, when a vehicle speed of zero is detected for a duration exceeding a second preset time (e.g., 30 seconds), the initial carbon emission calculation result is dynamically adjusted downwards based on the idling duration. The correction factor employs a step-down strategy, gradually decreasing from an initial 0.9 times to a minimum of 0.8 times, accurately reflecting the engine's reduced load operating state. The final carbon emission calculation result under prolonged idling conditions is then obtained.

[0067] Step S43: When the engine coolant temperature in the enhanced feature set is detected to be lower than the preset temperature threshold, the dynamic compensation coefficient corresponding to the engine coolant temperature is matched, and the initial carbon emission calculation result is smoothed and compensated in combination with the preset gradual recovery period to obtain the final carbon emission calculation result under cold start condition.

[0068] The cold start feature primarily addresses emission deviations under low-temperature conditions. Specifically, it activates a compensation algorithm when the engine coolant temperature drops below 80°C by real-time monitoring. The compensation coefficient is dynamically adjusted between 1.2 and 1.5 times the specific temperature value, with a 30-second gradual recovery period to ensure calculation continuity during operating condition transitions.

[0069] Through the above steps, millisecond-level response speeds are ensured via real-time data stream processing. The corrected parameters are calibrated based on extensive bench and real-vehicle test data and support remote online updates. Actual road tests show that this result correction process can reduce calculation errors under urban conditions by more than 50% while maintaining low system resource utilization. The correction results are simultaneously output to the driving behavior analysis system, providing data support for energy conservation and emission reduction management.

[0070] The above-described method acquires real-time road and vehicle data; performs data alignment and feature enhancement on the real-time road and vehicle data to obtain an enhanced feature set; identifies the target vehicle type, matches the corresponding preset carbon emission calculation model, and inputs the enhanced feature set to calculate the initial carbon emission calculation result; adjusts the initial carbon emission calculation result according to the current vehicle operating status to obtain the final carbon emission calculation result. This solution, by integrating real-time road data and real-time vehicle parameters with a dynamic carbon emission calculation model, overcomes the calculation bias problems caused by the single data dimension and poor model universality of traditional calculation methods, effectively reducing the basic carbon emission calculation error under complex road conditions. Furthermore, by dynamically correcting the initial carbon emission result based on the real-time vehicle operating status, it accurately adapts and compensates for emission fluctuations caused by real-time vehicle driving conditions, further improving the accuracy and realism of the carbon emission calculation result and significantly reducing the carbon emission measurement error in real-world driving scenarios.

[0071] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 The carbon emission calculation method further includes steps A1 to A3: Step A1: Identify the current battery level of the target vehicle in the enhanced feature set; This embodiment addresses the carbon emission calculation scenario for hybrid vehicles, employing a power intelligent allocation algorithm to achieve differentiated carbon emission calculation for hybrid models. First, the real-time remaining battery charge parameters of the hybrid vehicle are accurately extracted from the preprocessed enhanced feature set, serving as the core basis for determining power mode allocation and carbon emission breakdown calculations.

[0072] Step A2: When the current battery charge is higher than a preset threshold, calculate the electric mode operation ratio of the target vehicle using a preset allocation formula, and determine the corresponding allocation of the vehicle's electric mode and fuel mode operation ratios. In this embodiment, a preset battery charge threshold is set to 30%. When the vehicle's real-time remaining battery charge is detected to be higher than 30%, it is determined that the vehicle is ready for pure electric drive. A preset power distribution formula is used to calculate the proportion of electric mode operation during vehicle driving in real time. The remaining operating proportion is allocated to fuel drive mode to determine the operating condition distribution ratio of the hybrid vehicle's dual power sources. When the battery charge is lower than the preset threshold, the vehicle mainly operates in fuel mode, with the carbon emission calculation results in fuel mode serving as the core calculation basis.

[0073] Step A3: Based on the proportion of electric mode and fuel mode, calculate the carbon emission values ​​of the target vehicle in electric mode and fuel mode respectively, and use a weighted average algorithm to fuse the carbon emission calculation results to obtain the initial carbon emission calculation result for the hybrid vehicle.

[0074] In this embodiment, the carbon emission values ​​corresponding to the fuel mode are calculated according to the carbon emission calculation logic of the pure fuel vehicle in the first embodiment, and the carbon emission values ​​corresponding to the electric mode are calculated according to the carbon emission calculation logic of the pure electric vehicle in the first embodiment. Then, combined with the dual-mode operation ratio determined in step A2, a weighted average algorithm is used to fuse and fit the carbon emission results of the two modes, avoiding the calculation deviation caused by a single power calculation mode, accurately restoring the actual carbon emission level of the hybrid vehicle under multiple power switching and hybrid drive conditions, and finally outputting the initial carbon emission calculation result adapted to the operating characteristics of the hybrid vehicle.

[0075] Through the methods described above, we have achieved adaptive judgment of battery status, intelligent allocation of dual power modes, accurate calculation and fusion of carbon emissions for different modes in hybrid vehicles. This effectively adapts to the complex operating conditions of hybrid vehicles, including alternating electric and fuel operation and hybrid drive. It makes up for the shortcomings of traditional single-model carbon emission models that cannot be adapted to hybrid vehicles, and comprehensively improves the accuracy and universality of the initial carbon emission calculation results in scenarios covering multiple models.

[0076] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S40, the carbon emission calculation method further includes steps B1~B2: Step B1: Perform multi-dimensional aggregation and statistics on the final carbon emission calculation results according to the time dimension, spatial dimension, and vehicle dimension to obtain multi-dimensional carbon emission statistics. In this embodiment, the final carbon emission calculation results are processed by multi-dimensional structured data aggregation and statistics on the real-time carbon emission calculation results output by the vehicle. Data noise reduction, regional statistics and feature mining are completed from three dimensions: time, space and vehicle, to achieve refined processing of the original emission data.

[0077] In terms of time dimension, the moving average carbon emission value is calculated every 5 minutes to eliminate data noise caused by instantaneous fluctuations; in terms of space dimension, based on the road segmentation of high-precision maps, the carbon emission intensity per kilometer of road is accurately calculated; in terms of vehicle dimension, personalized driving scores are generated through machine learning algorithms and compared with the average performance of a fleet of vehicles of the same model. By aggregating and statistically analyzing these three dimensions, a well-organized, multi-dimensional, and visualized carbon emission statistics dataset is obtained.

[0078] Step B2: Render a heatmap of the multidimensional carbon emission statistics based on the Gaode Map API interface, mark high emission areas, and simultaneously generate driving scores and personalized suggestions.

[0079] In this embodiment, the front-end intelligent visualization rendering of multi-dimensional carbon emission statistics is completed through the Gaode Map API interface. The road carbon emission heat map uses standardized progressive color-grading, strictly dividing the display levels according to emission thresholds: low-emission road sections (0-50g / km) are rendered in green, medium-emission road sections (50-100g / km) in yellow, and high-emission road sections (above 100g / km) in red, intuitively distinguishing the carbon emission intensity levels of different road sections. Simultaneously, an intelligent warning mechanism for exceeding standards is set up, comparing real-time emission data of road sections with historical average emission data for that road section. When the real-time emission value exceeds the historical average by 20%, an exceeding-standard warning mark is automatically added to the corresponding road section, quickly locating areas with abnormal high emissions.

[0080] Meanwhile, this embodiment supports multi-level interactive queries. Users can click on any road segment to view detailed data, including: carbon emission trend map of the road segment (last 24 hours), percentile comparison of similar vehicles, and suggestions for energy conservation and emission reduction improvement.

[0081] Through the methods described above, we have achieved time-series noise reduction, spatially refined statistics, and multi-dimensional aggregation processing of personalized vehicle benchmarking for carbon emission data. Combined with the Gaode Map API, we have completed visualized intelligent rendering and interactive querying. This solves the problems of traditional carbon emission data display being singular, having high data noise, and lacking scenario-based analysis capabilities. It can intuitively and accurately present the distribution status of road traffic carbon emissions and vehicle emission levels, providing visualized data support and intelligent decision-making basis for low-carbon traffic management, driving behavior optimization, and road emission control.

[0082] Based on the first embodiment of this application, in the fourth embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 Step S40, the carbon emission calculation method further includes steps C1 to C4: Step C1: When an abnormal system signal is detected, the Gaode Map benchmark calculation mode is triggered. In this embodiment, the global operating status and data transmission status are monitored in real time. Once any abnormal system signal is detected, such as abnormal slope data, abnormal network latency, or loss of OBD signal from the vehicle diagnostic system, the Gaode Map benchmark calculation mode is immediately triggered and switched to the backup standardized calculation logic. This avoids the problem of carbon emission calculation interruption and result distortion caused by the failure of a single data source, and ensures the continuity and stability of the system calculation process.

[0083] Step C2: In the Gaode Map benchmark calculation mode, if the system abnormal signal is abnormal slope data, the inertial measurement data of the inertial measurement unit is retrieved to replace the abnormal slope data, thereby completing the correction of the final carbon emission calculation value. In this embodiment, when the system anomaly is determined to be road slope data anomaly, the IMU (Inertial Measurement Unit) is retrieved as a replacement data source. Specifically, by fusing gyroscope angular velocity data and accelerometer acceleration data, three-dimensional road slope information during vehicle movement is reconstructed, replacing the abnormal and invalid slope acquisition data. Simultaneously, the system continuously corrects the offset error generated during IMU operation, stabilizing the slope measurement accuracy within 0.5 degrees, minimizing the carbon emission calculation deviation caused by slope data anomalies, and ensuring the accuracy of carbon emission calculation results under complex road conditions.

[0084] Step C3: If the system anomaly signal is a network latency anomaly, use local cached data to calculate carbon emissions; In this embodiment, when network latency is detected, the system prioritizes using locally cached data for carbon emission calculations, effectively addressing the issues of data disconnection and untimely updates caused by network fluctuations. The system employs a timeliness control mechanism for locally cached data; cached data stored for more than 5 minutes is marked as pending updates and prohibited from direct use in calculations. Simultaneously, it intelligently predicts the current vehicle operating status by combining historical driving and emission trend data, compensating for data loss due to network latency and maximizing the continuity and effectiveness of carbon emission calculations.

[0085] Step C4: If the abnormal system signal is a loss of on-board diagnostic system signal, perform auxiliary calculations and correct the carbon emission calculation results in conjunction with a preset compensation mechanism.

[0086] In this embodiment, when the loss of the OBD signal from the vehicle diagnostic system is detected, auxiliary carbon emission calculation is performed based on the Gaode Map benchmark calculation mode. This does not simply replace the original calculation data, but rather adds a 20% safety compensation margin to the benchmark calculation result, fundamentally avoiding the problem of underestimating the carbon emission calculation result. Simultaneously, a smooth transition mechanism is set up. After the OBD signal returns to normal, the system maintains a 30-second transition period, gradually reducing the compensation coefficient to avoid sudden changes or jumps in the carbon emission calculation result when switching operating conditions and data sources, ensuring the smooth and stable calculation data.

[0087] Furthermore, this system incorporates a self-calibration mechanism, enabling continuous self-upgrading. Every 2 minutes, the system compares the difference between the OBD direct fuel consumption and the model calculation results, establishing an error statistics table. Based on this data, the system dynamically adjusts key parameters such as engine efficiency coefficient and rolling resistance coefficient, but each adjustment is controlled within 5% to ensure no sudden parameter changes. Simultaneously, the system includes a vehicle characteristic knowledge base containing over 200 parameter templates, supporting both offline local calibration and cloud-based big data calibration modes. Deep and precise calibration can be initiated when external diagnostic equipment is connected. During daily operation, the system continuously iterates and optimizes model parameters through the aggregation and analysis of massive fleet data, constantly improving model adaptability and calculation accuracy.

[0088] In addition, this embodiment also includes a user notification mechanism. When an anomaly occurs, the dashboard will display a corresponding warning icon; when there is a network anomaly, the timestamp of the last valid data will be displayed; during calibration, the progress percentage will be displayed in real time. All abnormal events will be recorded in detail, supporting subsequent analysis and report export.

[0089] Through the methods described above, a fault-tolerant processing system covering three core anomaly scenarios—OBD signal loss, slope data anomalies, and network latency—was constructed, coupled with high-frequency dynamic self-calibration, dual-mode parameter optimization, and visual anomaly alerts. Real-world testing data shows that this solution can control signal interruption compensation error to within 8%, far superior to the 25% error level of traditional solutions. Slope reconstruction accuracy reaches 1.5 degrees, fully meeting industry calculation standards, and the average self-calibration response time is only 3.2 seconds. The entire mechanism can operate stably under various complex and abnormal operating conditions, consistently controlling the overall system calculation error to within 15%. Simultaneously, relying on continuous self-learning and self-optimization capabilities, it continuously improves the accuracy and scenario adaptability of carbon emission calculations, ensuring the long-term stable, accurate, and reliable operation of the carbon emission calculation system.

[0090] For example, to help understand the implementation flow of the carbon emission calculation method obtained in this embodiment combined with the above embodiment one, please refer to... Figure 5 , Figure 5 A system architecture diagram for a carbon emission calculation method is provided, specifically: This system uses Gaode Map API and in-vehicle OBD as dual data sources, outputting real-time road data and real-time vehicle data respectively, which are then input into the carbon emission calculation engine. The engine completes data alignment, feature enhancement, carbon emission calculation by vehicle type, operating condition correction, anomaly tolerance, and self-calibration, outputting the processed carbon emission data to the visualization platform. The visualization platform performs multi-dimensional statistics on the data and renders emission heat maps, providing interactive queries and energy-saving analysis suggestions, and finally outputs the data to driving behavior feedback, forming a complete closed loop of data collection, accurate calculation, visualization analysis, and emission reduction guidance, achieving high-precision quantitative management of carbon emissions under multiple road conditions and vehicle types.

[0091] For example, to help understand the implementation flow of the carbon emission calculation method obtained in this embodiment combined with the above embodiment one, please refer to... Figure 6 , Figure 6 A flowchart with omitted steps is provided for a carbon emission calculation method, specifically: The vehicle's original operating data is read via the CAN bus through the onboard OBD and sent to the OBD data preprocessing unit for cleaning and standardization. At the same time, real-time requests are made through the Gaode Map API to obtain road condition data and road data preprocessing is completed simultaneously.

[0092] The two preprocessed data streams are input into the data alignment module, which uses timestamp matching to achieve time synchronization of multi-source data. The matched data then enters the dynamic feature enhancement module to calculate derived features such as road load and congestion penalty.

[0093] The system calculates the carbon emission path based on the vehicle's power type. Electric vehicle data is input into the electric carbon emission model, and fuel vehicle data is input into the fuel carbon emission model. The two models output initial carbon emission results respectively. All initial results are uniformly entered into the driving behavior correction module, which combines conditions such as rapid acceleration, cold start, and idling to complete emission compensation correction. The corrected data is then sent to the model self-calibration module for error verification.

[0094] When the detection and calculation error exceeds the preset threshold, the parameter dynamic adjustment mechanism is triggered and the optimization model calculation parameters are sent back; if the error is within the normal range, it directly enters the result aggregation stage. The aggregated data is output to the Gaode heat map rendering module to generate a road emission visualization heat map, and the driving score generation module to output a personalized energy-saving driving score for the vehicle.

[0095] By integrating slope data from high-precision maps and real-time OBD engine parameters, the above-described method accurately captures the actual impact of road conditions such as uphill driving and cornering on fuel consumption. In particular, the recognition accuracy for special driving behaviors such as rapid acceleration and prolonged idling reaches over 98%. This solution incorporates two calculation models for both gasoline and electric vehicles, automatically identifying vehicle type and switching algorithms accordingly. Furthermore, through multi-scenario anomaly tolerance and periodic self-calibration mechanisms, it automatically compensates for calculation deviations caused by various data anomalies such as OBD signal loss, GPS drift, network latency, and slope data failure. It also periodically compares measured energy consumption with model calculations to dynamically correct core coefficients, keeping the overall carbon emission calculation error stably within 15%. Simultaneously, it includes map heat visualization, personalized driving scores, and emission reduction suggestion output functions, fully realizing a closed-loop management system from multi-source data collection and high-precision carbon emission calculation to visualization analysis and driving optimization feedback.

[0096] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the carbon emission calculation method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0097] This application also provides a carbon emission calculation system, please refer to... Figure 7 The carbon emission calculation system includes: Data acquisition module 10 is used to acquire real-time road data and real-time vehicle data; Data processing module 20 is used to perform data alignment and feature enhancement on the real-time road data and real-time vehicle data to obtain an enhanced feature set; The carbon emission calculation module 30 is used to identify the target vehicle type, match the preset carbon emission calculation model corresponding to the target vehicle type, and input the enhanced feature set to calculate the initial carbon emission calculation result. The carbon emission correction module 40 is used to adjust the initial carbon emission calculation result according to the current operating status of the vehicle to obtain the final carbon emission calculation result.

[0098] The carbon emission calculation system provided in this application, employing the carbon emission calculation method described in the above embodiments, can solve the technical problem of how to reduce the calculation error of carbon emissions. Compared with the prior art, the beneficial effects of the carbon emission calculation system provided in this application are the same as those of the carbon emission calculation method provided in the above embodiments, and other technical features of the carbon emission calculation system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0099] This application provides a carbon emission calculation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the carbon emission calculation method in Embodiment 1 above.

[0100] The following is for reference. Figure 8 The diagram illustrates a structural schematic of a carbon emission calculation device suitable for implementing embodiments of this application. The carbon emission calculation device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8The carbon emission calculation device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0101] like Figure 8 As shown, the carbon emission calculation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the carbon emission calculation device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the carbon emission calculation device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows carbon emission calculation devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0102] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0103] The carbon emission calculation device provided in this application, employing the carbon emission calculation method described in the above embodiments, can solve the technical problem of how to reduce the calculation error of carbon emissions. Compared with the prior art, the beneficial effects of the carbon emission calculation device provided in this application are the same as those of the carbon emission calculation method provided in the above embodiments, and other technical features of this carbon emission calculation device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0104] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0105] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0106] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the carbon emission calculation method in the above embodiments.

[0107] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0108] The aforementioned computer-readable storage medium may be included in the carbon emission calculation device; or it may exist independently and not be assembled into the carbon emission calculation device.

[0109] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the carbon emission calculation device, the carbon emission calculation device causes the following: to acquire real-time road data and real-time vehicle data; to perform data alignment and feature enhancement on the real-time road data and real-time vehicle data to obtain an enhanced feature set; to identify the target vehicle type, match the preset carbon emission calculation model corresponding to the target vehicle type, and input the enhanced feature set to calculate the initial carbon emission calculation result; and to adjust the initial carbon emission calculation result according to the current operating status of the vehicle to obtain the final carbon emission calculation result.

[0110] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0111] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0112] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0113] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described carbon emission calculation method, thereby solving the technical problem of how to reduce the calculation error of carbon emissions. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the carbon emission calculation method provided in the above embodiments, and will not be repeated here.

[0114] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the carbon emission calculation method described above.

[0115] The computer program product provided in this application can solve the technical problem of how to reduce the calculation error of carbon emissions. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the carbon emission calculation method provided in the above embodiments, and will not be repeated here.

[0116] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for calculating carbon emissions, characterized in that, The carbon emission calculation method includes: Acquire real-time road and vehicle data; Data alignment and feature enhancement are performed on the real-time road data and real-time vehicle data to obtain an enhanced feature set; Identify the target vehicle type, match the preset carbon emission calculation model corresponding to the target vehicle type, and input the enhanced feature set to calculate the initial carbon emission calculation result. The target vehicle type includes fuel vehicles and electric vehicles, and the preset carbon emission calculation model includes electric carbon emission model and fuel carbon emission model. The process of identifying the target vehicle type, matching the target vehicle type with a preset carbon emission calculation model, and inputting the enhanced feature set to calculate the initial carbon emission calculation result includes: If the target vehicle is a gasoline-powered vehicle, then the enhanced feature set is input into the gasoline carbon emission model to calculate the initial carbon emission calculation result; If the target vehicle is an electric vehicle, the enhanced feature set is input into the electric carbon emission model to calculate the initial carbon emission calculation result. Based on the current operating status of the vehicle, the initial carbon emission calculation results are adjusted to obtain the final carbon emission calculation results.

2. The carbon emission calculation method as described in claim 1, characterized in that, The step of performing data alignment and feature enhancement on the real-time road data and real-time vehicle data to obtain an enhanced feature set includes: A sliding window mechanism is used to match the timestamps of the real-time road data and the real-time vehicle data to obtain time-aligned real-time road data and real-time vehicle data. Based on the time-aligned real-time road data, calculate the road load factor and traffic penalty factor; The enhanced feature set is constructed by combining the time-aligned real-time road data, real-time vehicle data, road load factor, and traffic penalty factor.

3. The carbon emission calculation method as described in claim 1, characterized in that, The step of inputting the enhanced feature set into the fuel carbon emission model to calculate the initial carbon emission calculation result includes: Based on the engine displacement, speed and load coefficient in the enhanced feature set, the basic fuel consumption is calculated in combination with the preset calculation coefficient. Based on the road slope parameters and real-time congestion coefficient in the enhanced feature set, slope correction terms and traffic correction terms are obtained respectively, and the base fuel consumption is corrected based on the slope correction terms and traffic correction terms. The baseline carbon emission calculation results are obtained by combining the preset fuel type coefficient and the corrected baseline fuel consumption. When a special operating condition of a vehicle in real time is identified, a corresponding dynamic correction mechanism is matched to compensate and correct the basic carbon emission calculation results to obtain the initial carbon emission calculation results for the fuel vehicle.

4. The carbon emission calculation method as described in claim 1, characterized in that, The step of inputting the enhanced feature set into the electric vehicle carbon emission model to calculate the initial carbon emission calculation result includes: Based on the real-time vehicle speed data in the enhanced feature set, and combined with the preset speed range graded energy consumption benchmark, the basic energy consumption data corresponding to the target vehicle is determined. Based on the congestion level, road slope parameters, vehicle weight, and ambient temperature parameters in the enhanced feature set, congestion correction terms, slope correction terms, and temperature correction terms are obtained respectively, and the basic energy consumption data is corrected based on the congestion correction terms, slope correction terms, and temperature correction terms. By combining the real-time acquired grid carbon emission factor and the corrected basic energy consumption data, the initial carbon emission calculation result corresponding to the electric vehicle is obtained.

5. The carbon emission calculation method as described in claim 1, characterized in that, The target vehicle type is a hybrid vehicle, and the carbon emission calculation method includes: Identify the current battery level of the target vehicle within the enhanced feature set; When the current battery charge is higher than a preset threshold, the electric mode operation ratio of the target vehicle is calculated by a preset allocation formula, and the operation ratio allocation between the electric mode and the fuel mode of the vehicle is determined accordingly. Based on the proportion of electric mode and fuel mode, the carbon emission values ​​of the target vehicle in electric mode and fuel mode are calculated respectively. The carbon emission calculation results are then fused using a weighted average algorithm to obtain the initial carbon emission calculation result for the hybrid vehicle.

6. The carbon emission calculation method as described in claim 1, characterized in that, The step of adjusting the initial carbon emission calculation result based on the current operating status of the vehicle to obtain the final carbon emission calculation result includes: When a vehicle acceleration in the enhanced feature set is detected to be greater than a preset acceleration threshold and the duration exceeds a first preset duration, the initial carbon emission calculation results for the corresponding time period are adjusted upwards in stages to obtain the final carbon emission calculation results under rapid acceleration conditions; and / or When a vehicle in the enhanced feature set is detected to have a speed of zero for a duration exceeding a second preset duration, the initial carbon emission calculation result is corrected using a step-down adjustment strategy based on the vehicle's idling time, resulting in the final carbon emission calculation result under long idling conditions; and / or When the engine coolant temperature in the enhanced feature set is detected to be lower than the preset temperature threshold, the initial carbon emission calculation result is smoothed and corrected according to the engine coolant temperature matching dynamic compensation coefficient and combined with the preset gradual recovery period to obtain the final carbon emission calculation result under cold start condition.

7. The carbon emission calculation method as described in claim 1, characterized in that, After the step of adjusting the initial carbon emission calculation result based on the current operating status of the vehicle to obtain the final carbon emission calculation result, the method includes: The final carbon emission calculation results are aggregated and statistically analyzed in multiple dimensions, including time, space, and vehicle, to obtain multi-dimensional carbon emission statistics. Based on the Gaode Map API interface, the multidimensional carbon emission statistics are rendered into a heat map, high emission areas are marked, and driving scores and personalized suggestions are generated simultaneously.

8. The carbon emission calculation method as described in claim 1, characterized in that, After the step of adjusting the initial carbon emission calculation result based on the current operating status of the vehicle to obtain the final carbon emission calculation result, the method further includes: When an abnormal system signal is detected, the Gaode Map benchmark calculation mode is triggered. In the Gaode Map benchmark calculation mode, if the system anomaly signal is an abnormal slope data, the inertial measurement data of the inertial measurement unit is retrieved to replace the abnormal slope data, thereby correcting the final carbon emission calculation value. If the system anomaly signal is a network latency anomaly, carbon emission calculation is performed using locally cached data. If the abnormal signal of the system is the loss of the on-board diagnostic system signal, auxiliary calculations are performed, and the carbon emission calculation results are corrected in conjunction with a preset compensation mechanism.

9. A carbon emission calculation system, characterized in that, The carbon emission calculation system includes: The data acquisition module is used to acquire real-time road data and real-time vehicle data; The data processing module is used to perform data alignment and feature enhancement on the real-time road data and real-time vehicle data to obtain an enhanced feature set; The carbon emission calculation module is used to identify the target vehicle type, match the preset carbon emission calculation model corresponding to the target vehicle type, and input the enhanced feature set to calculate the initial carbon emission calculation result. The target vehicle type includes fuel vehicles and electric vehicles, and the preset carbon emission calculation model includes electric carbon emission model and fuel carbon emission model. The process of identifying the target vehicle type, matching the target vehicle type with a preset carbon emission calculation model, and inputting the enhanced feature set to calculate the initial carbon emission calculation result includes: If the target vehicle is a gasoline-powered vehicle, then the enhanced feature set is input into the gasoline carbon emission model to calculate the initial carbon emission calculation result; If the target vehicle is an electric vehicle, the enhanced feature set is input into the electric carbon emission model to calculate the initial carbon emission calculation result. The carbon emission correction module is used to adjust the initial carbon emission calculation result according to the current operating status of the vehicle to obtain the final carbon emission calculation result.

10. A carbon emission calculation device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the carbon emission calculation method as described in any one of claims 1 to 8.

11. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the carbon emission calculation method as described in any one of claims 1 to 8.

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