Electric vehicle carbon footprint quantification calculation method and device, computer equipment and medium

Abstract

This application relates to a method, apparatus, computer equipment, and medium for quantifying the carbon footprint of electric vehicles. The method includes: acquiring distribution network energy data from charging stations and user behavior characteristic data of electric vehicles; based on the distribution network energy data, weighted fusion of carbon emission factors from multiple nodes to construct a real-time carbon status analysis model and output a dynamic curve of carbon density changing over time; time-series matching of charging periods in the user behavior characteristic data with the dynamic curve of carbon density to establish a correlation between charging periods and corresponding carbon densities, and calculating the quantified carbon footprint value throughout the charging process based on the correlation and charging power data; creating a user carbon account based on the quantified carbon footprint value, determining the optimal low-carbon charging period according to the dynamic curve of carbon density, and adjusting the incentive coefficient associated with user rights. This method enables high-granularity carbon density monitoring and deep coupling with user incentives to improve measurement accuracy.

Description

Technical Field

[0001] This application relates to the field of electric vehicle technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for quantifying the carbon footprint of electric vehicles. Background Technology

[0002] With the continued deepening of the global "dual carbon" strategic goals, electric vehicles have developed into the core carrier of the new energy transportation system. Currently, the industry mainly relies on the macro-regional average carbon emission factor method to calculate the carbon footprint of electric vehicle charging. That is, by querying the regional annual or monthly average carbon emission factor published by the power grid and combining it with the amount of electricity charged by the electric vehicle, the carbon emissions generated by a single charging behavior are estimated.

[0003] However, traditional technical solutions suffer from the following technical shortcomings: First, the granularity of carbon footprint measurement is too coarse, lacking dynamic perception capabilities with high spatiotemporal resolution. This fails to distinguish the differences in carbon density of power sources at different charging stations due to varying grid connection nodes, and also fails to reflect real-time carbon density fluctuations at the same station within a day caused by changes in grid load and renewable energy penetration. This leads to distorted carbon footprint quantification results, making it difficult to accurately reflect the environmental impact of charging behavior. Second, there is a lack of user-level carbon footprint correlation and low-carbon behavior guidance mechanisms. Because it is impossible to establish dedicated carbon footprint accounts for users, and carbon footprint information is not transformed into perceptible and beneficial incentive signals, users lack the intrinsic motivation to optimize charging times and choose low-carbon charging stations, thus preventing the effective activation and release of the enormous emission reduction potential of electric vehicles in the charging process.

[0004] Therefore, there is an urgent need to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for quantifying the carbon footprint of electric vehicles, which can achieve high-granularity carbon density monitoring and be deeply coupled with user incentives to improve the accuracy of the calculation. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for quantifying the carbon footprint of electric vehicles, which can achieve high-granularity carbon density monitoring and be deeply coupled with user incentives to improve the accuracy of the calculation, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for quantifying the carbon footprint of electric vehicles, including:

[0007] Acquire power distribution network energy data and electric vehicle user behavior data for charging stations; the power distribution network energy data includes at least the real-time power and carbon emission factor of each node in the power distribution network.

[0008] Based on the energy data of the distribution network, a dynamic weighting algorithm is used to weight and fuse the carbon emission factors of multiple nodes to construct a real-time carbon status analysis model and output a dynamic carbon density curve that characterizes the change of carbon density of the charging station over time.

[0009] The charging time period in the user behavior feature data is matched with the carbon density dynamic curve in time series to establish the correlation between the charging time period and the corresponding carbon density, and the carbon footprint quantification value of the electric vehicle in the whole charging process is calculated based on the correlation and charging power data.

[0010] Based on the quantified carbon footprint value, a user carbon account is created, the optimal low-carbon charging period is determined according to the dynamic carbon density curve, and the incentive coefficient associated with user rights is dynamically adjusted according to the degree to which the user's charging behavior conforms to the optimal low-carbon charging period.

[0011] In one embodiment, acquiring the power distribution network energy data of the charging station and the user behavior characteristic data of the electric vehicle includes:

[0012] By deploying edge computing nodes at charging stations, the real-time power and carbon emission factor of each node in the distribution network are collected at a preset sampling frequency.

[0013] User behavior data is collected synchronously through the vehicle information system and charging pile network. The user behavior data includes user charging time, driving mileage and energy consumption per unit mileage under different driving conditions.

[0014] Outlier cleaning is performed on the collected distribution network energy data and user behavior characteristic data to remove data points that deviate from the statistical distribution threshold.

[0015] In one embodiment, based on the distribution network energy data, a dynamic weighted algorithm is used to weight and fuse the carbon emission factors of multiple nodes to construct a real-time carbon state analysis model, and output a dynamic carbon density curve characterizing the change of carbon density of the charging station over time, including:

[0016] Based on the energy data of the distribution network, calculate the energy contribution rate of each node of the distribution network to the total power supply of the charging station;

[0017] The energy contribution rate is normalized and used as the dynamic weighting coefficient of the carbon emission factor at each node.

[0018] The real-time power and carbon emission factor of each node in the distribution network are weighted and accumulated using the dynamic weighting coefficient to obtain the total carbon emissions of the distribution network supplying power to the charging station.

[0019] Divide the total carbon emissions by the total charging power of the charging station to obtain the real-time carbon density corresponding to the unit charging energy, and generate a dynamic carbon density curve with time as the horizontal axis and carbon density as the vertical axis.

[0020] In one embodiment, the step of performing time-series matching between the charging periods in the user behavior feature data and the carbon density dynamic curve to establish a correlation between the charging periods and the corresponding carbon densities, and calculating the quantified carbon footprint of the electric vehicle throughout the charging process based on the correlation and charging power data, includes:

[0021] Extract the time series of user charging periods from the user behavior feature data;

[0022] The time series matching algorithm is invoked to align the time series with the carbon density dynamic curve on the time axis;

[0023] Within the aligned time interval, obtain the carbon density value and real-time charging power of the charging pile at each moment;

[0024] Based on the real-time charging power of the charging pile, the charging energy for each minute period is calculated, multiplied by the corresponding carbon density value, and then accumulated to obtain the quantified value of the user's carbon footprint throughout the charging process.

[0025] In one embodiment, the step of calculating the charging energy for each minute time period based on the real-time charging power of the charging pile, multiplying it by the corresponding carbon density value, and accumulating the results to obtain the quantified carbon footprint value of the user throughout the charging process includes:

[0026] Based on the changing nodes of the carbon density dynamic curve, the user charging period is divided into multiple consecutive sub-periods;

[0027] The average carbon density, charging power, and duration of each sub-period are obtained, and the carbon emissions are calculated based on the average carbon density, the charging power, and the duration.

[0028] The carbon emissions of all sub-periods are summed up to output the quantified carbon footprint value of a single charging behavior.

[0029] In one embodiment, the step of creating a user carbon account based on the quantified carbon footprint value, determining the optimal low-carbon charging period according to the dynamic carbon density curve, and dynamically adjusting the incentive coefficient associated with user rights based on the degree to which the user's charging behavior conforms to the optimal low-carbon charging period includes:

[0030] Assign a unique identifier to the user's carbon account and bind it to vehicle information and charging records;

[0031] The optimal low-carbon charging period is determined based on the carbon density dynamic curve, and the user's actual charging period is monitored.

[0032] Based on the number of times the user's actual charging period and the optimal low-carbon charging period are matched, and based on the deviation between the carbon footprint quantification value and the preset industry average, the incentive coefficient is adjusted upward, downward, or cleared.

[0033] The adjusted incentive coefficients are mapped to user benefits, which include a charging fee reduction ratio or a carbon credit redemption limit.

[0034] In one embodiment, the adjustment rule for the excitation coefficient includes:

[0035] Set the baseline and upper limit values ​​for the incentive coefficient;

[0036] If the carbon footprint quantification value of a single charge is lower than the first preset industry average, the incentive coefficient will be increased by a first adjustment range.

[0037] When the optimal low-carbon charging period is selected multiple times in a row, the excitation coefficient is superimposed with a second adjustment magnitude;

[0038] If the quantified carbon footprint of a single charge is higher than the second threshold of the industry average, the incentive coefficient will be reduced by a third adjustment.

[0039] If the optimal low-carbon charging period is not selected multiple times in a row, the superimposed second adjustment range will be reset to zero.

[0040] Secondly, this application also provides a device for quantifying the carbon footprint of electric vehicles, comprising:

[0041] The acquisition module is used to acquire the distribution network energy data of the charging station and the user behavior characteristic data of electric vehicles; the distribution network energy data includes at least the real-time power and carbon emission factor of each node in the distribution network;

[0042] The model building module is used to construct a real-time carbon status analysis model based on the distribution network energy data, by using a dynamic weighting algorithm to weight and fuse the carbon emission factors of multiple nodes, and output a dynamic carbon density curve characterizing the change of carbon density of the charging station over time.

[0043] The module for establishing and calculating is used to perform time series matching between the charging period in the user behavior feature data and the carbon density dynamic curve, establish the correlation between the charging period and the corresponding carbon density, and calculate the quantified value of the carbon footprint of the electric vehicle in the entire charging process based on the correlation and the charging power data.

[0044] The adjustment module is used to create a user carbon account based on the carbon footprint quantification value, determine the optimal low-carbon charging period according to the carbon density dynamic curve, and dynamically adjust the incentive coefficient associated with user rights based on the degree of conformity between the user's charging behavior and the optimal low-carbon charging period.

[0045] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0046] Acquire power distribution network energy data and electric vehicle user behavior data for charging stations; the power distribution network energy data includes at least the real-time power and carbon emission factor of each node in the power distribution network.

[0047] Based on the energy data of the distribution network, a dynamic weighting algorithm is used to weight and fuse the carbon emission factors of multiple nodes to construct a real-time carbon status analysis model and output a dynamic carbon density curve that characterizes the change of carbon density of the charging station over time.

[0048] The charging time period in the user behavior feature data is matched with the carbon density dynamic curve in time series to establish the correlation between the charging time period and the corresponding carbon density, and the carbon footprint quantification value of the electric vehicle in the whole charging process is calculated based on the correlation and charging power data.

[0049] Based on the quantified carbon footprint value, a user carbon account is created, the optimal low-carbon charging period is determined according to the dynamic carbon density curve, and the incentive coefficient associated with user rights is dynamically adjusted according to the degree to which the user's charging behavior conforms to the optimal low-carbon charging period.

[0050] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0051] Acquire power distribution network energy data and electric vehicle user behavior data for charging stations; the power distribution network energy data includes at least the real-time power and carbon emission factor of each node in the power distribution network.

[0052] Based on the energy data of the distribution network, a dynamic weighting algorithm is used to weight and fuse the carbon emission factors of multiple nodes to construct a real-time carbon status analysis model and output a dynamic carbon density curve that characterizes the change of carbon density of the charging station over time.

[0053] The charging time period in the user behavior feature data is matched with the carbon density dynamic curve in time series to establish the correlation between the charging time period and the corresponding carbon density, and the carbon footprint quantification value of the electric vehicle in the whole charging process is calculated based on the correlation and charging power data.

[0054] Based on the quantified carbon footprint value, a user carbon account is created, the optimal low-carbon charging period is determined according to the dynamic carbon density curve, and the incentive coefficient associated with user rights is dynamically adjusted according to the degree to which the user's charging behavior conforms to the optimal low-carbon charging period.

[0055] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0056] Acquire power distribution network energy data and electric vehicle user behavior data for charging stations; the power distribution network energy data includes at least the real-time power and carbon emission factor of each node in the power distribution network.

[0057] Based on the energy data of the distribution network, a dynamic weighting algorithm is used to weight and fuse the carbon emission factors of multiple nodes to construct a real-time carbon status analysis model and output a dynamic carbon density curve that characterizes the change of carbon density of the charging station over time.

[0058] The charging time period in the user behavior feature data is matched with the carbon density dynamic curve in time series to establish the correlation between the charging time period and the corresponding carbon density, and the carbon footprint quantification value of the electric vehicle in the whole charging process is calculated based on the correlation and charging power data.

[0059] Based on the quantified carbon footprint value, a user carbon account is created, the optimal low-carbon charging period is determined according to the dynamic carbon density curve, and the incentive coefficient associated with user rights is dynamically adjusted according to the degree to which the user's charging behavior conforms to the optimal low-carbon charging period.

[0060] The aforementioned electric vehicle carbon footprint quantification method, device, computer equipment, computer-readable storage medium, and computer program product, by acquiring real-time power and carbon emission factors at each node of the distribution network and employing a dynamic weighted algorithm to spatiotemporally fuse the carbon emission factors of multiple nodes, can construct a real-time carbon state analysis model reflecting the continuous evolution of carbon emission intensity at charging stations over time. This model outputs a curve characterizing the dynamic fluctuations in carbon density, overcoming the technical shortcomings of traditional methods that use fixed average carbon emission factors and cannot characterize the spatiotemporal heterogeneity and volatility of distribution network carbon intensity. By time-series matching of electric vehicle user charging period characteristic data with this dynamic carbon density curve, a precise correlation between charging behavior and real-time carbon intensity can be established. Furthermore, based on charging power data, the carbon footprint of the entire charging lifecycle can be quantitatively calculated for each time period, thus solving the problems of existing carbon accounting methods that struggle to trace the specific charging process of users and lack refined measurement methods. By creating personalized carbon accounts for users based on quantified carbon footprints and dynamically identifying optimal low-carbon charging periods according to carbon density curves, and then dynamically adjusting incentive coefficients linked to benefits based on the degree to which users' actual charging behavior matches these low-carbon periods, a closed-loop, transparent, and dynamically responsive carbon incentive feedback mechanism can be constructed. This mechanism not only makes users' low-carbon behavior quantifiable, recordable, and incentivized, significantly enhancing users' initiative in participating in green electricity consumption and demand-side response, but also provides the distribution network with a flexible adjustment tool based on price signals. This effectively guides charging loads to actively match peak renewable energy output, promotes carbon emission reduction in the distribution network and local consumption of renewable energy, and achieves synergistic effects between user-side carbon reduction and grid-side operational optimization. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a diagram illustrating the application environment of a method for quantifying the carbon footprint of electric vehicles in one embodiment.

[0063] Figure 2 This is a flowchart illustrating a method for quantifying the carbon footprint of electric vehicles in one embodiment.

[0064] Figure 3 This is a flowchart illustrating the method for quantifying the carbon footprint of electric vehicles in another embodiment;

[0065] Figure 4 This is a structural block diagram of an electric vehicle carbon footprint quantification and calculation device in one embodiment;

[0066] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0068] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0069] The method for quantifying the carbon footprint of electric vehicles provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0070] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle systems, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0071] In one exemplary embodiment, such as Figure 2 As shown, a method for quantifying the carbon footprint of electric vehicles is provided, and this method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S202 to S208. Wherein:

[0072] Step S202: Obtain the power distribution network energy data of the charging station and the user behavior characteristic data of electric vehicles; the power distribution network energy data shall include at least the real-time power and carbon emission factor of each node of the power distribution network.

[0073] Specifically, the first step is to acquire real-time operational data of the power distribution network where the charging stations are located, as well as charging behavior data of electric vehicle users. Specifically, acquiring power distribution network energy data relies on intelligent measurement terminals or data acquisition and monitoring control systems deployed at key nodes of the power distribution network (such as feeder outlets, low-voltage sides of transformers, and charging station access points). This data is collected through real-time communication networks to gather active and reactive power data from each node, while simultaneously obtaining carbon emission factor information at the corresponding time. The carbon emission factor refers to the amount of carbon dioxide emitted per unit of electricity consumption. The value is derived from real-time carbon emission intensity data released by the upper-level power grid or predicted values ​​based on unit combination and scheduling plans, reflecting the greenness of electricity consumption at different times and nodes. User behavior characteristic data is obtained through charging operation management platforms or vehicle-to-everything (V2X) platforms, mainly including users' historical charging start and end times, charging duration, and charging power curves. This data forms the basis for analyzing the temporal correlation between user electricity consumption patterns and carbon emissions.

[0074] Step S204: Based on the distribution network energy data, a dynamic weighting algorithm is used to weight and fuse the carbon emission factors of multiple nodes to construct a real-time carbon state analysis model and output a dynamic carbon density curve characterizing the change of carbon density of charging stations over time.

[0075] Specifically, since the power supply of charging stations often involves multiple nodes in the distribution network, and the load levels and power structure of different nodes vary at different times, their carbon emission factors exhibit significant spatiotemporal heterogeneity. To accurately characterize the overall carbon emission intensity of charging stations, a dynamic weighted algorithm is used to fuse the carbon emission factors of multiple related nodes. The weights are not fixed but dynamically determined based on the real-time contribution of each node to the power supply of the charging station. In practice, it is first necessary to identify several distribution network nodes closely connected to the charging station electrically and obtain the real-time power data of these nodes at each moment (including the power component flowing to the charging station or the total load power of the node) and the corresponding carbon emission factors. The dynamic weighted algorithm dynamically allocates weights based on the proportion of each node's real-time power value to the total power supply; that is, the node with the larger power supply, the higher the weight of its carbon emission factor in the fusion calculation at the current moment. Through this dynamic weighted fusion calculation, the comprehensive carbon emission intensity of the charging station at each moment, i.e., carbon density, can be obtained. By connecting the calculation results from all moments in chronological order, a dynamic curve describing the continuous change of carbon density at charging stations over time can be generated. This curve visually reflects the fluctuation pattern of electricity cleanliness at charging stations over a day or longer period, providing a quantitative basis for accurately matching user charging behavior with low-carbon periods.

[0076] Step S206: Time series matching of charging time periods in user behavior feature data with carbon density dynamic curves is performed to establish the correlation between charging time periods and corresponding carbon densities, and the carbon footprint quantification value of electric vehicles in the entire charging process is calculated based on the correlation and charging power data.

[0077] Specifically, matching user behavior data with the carbon density dynamic curve and quantifying the carbon footprint involves several steps. First, the start and end times of a single charging session are extracted from the acquired user behavior data to form a complete charging period. Simultaneously, detailed data on the charging power variation over time within this period is obtained, typically presented as a power sequence at regular time intervals (e.g., every minute or every 15 minutes). Then, this charging period is aligned and matched with the dynamic curve generated in the previous step, which characterizes the continuous change of carbon density at the charging station over time. Specifically, each point in time within the charging period needs to be mapped to the corresponding carbon density value on the carbon density dynamic curve, thereby establishing a precise correlation between the charging power at each moment in the charging process and the real-time carbon density at that moment. Based on this time-by-time correlation, the carbon footprint quantification value is calculated by integration or summation: the charging power (in kW) at each time moment is multiplied by the carbon density (in kg CO2 / kWh) at that time moment to obtain the instantaneous carbon emission rate at that time moment; then the instantaneous carbon emission rates at all times are integrated or summed over the entire charging period to finally obtain the total carbon emissions generated by a single charging process, i.e., the carbon footprint quantification value.

[0078] Step S208: Based on the carbon footprint quantification value, create a user carbon account, determine the optimal low-carbon charging period according to the carbon density dynamic curve, and dynamically adjust the incentive coefficient associated with user rights based on the degree of conformity between the user's charging behavior and the optimal low-carbon charging period.

[0079] Specifically, based on quantified carbon footprint values, user carbon accounts are created, and a dynamic incentive mechanism is built. First, based on the quantified carbon footprint value of each user's charging behavior calculated in the previous step, a unique carbon account is established for each electric vehicle user. The core function of this account is to record, accumulate, and manage the carbon footprint data generated by the user in all their charging behaviors, forming a traceable and quantifiable personal carbon emission history archive. Second, based on the constructed dynamic carbon density curve, by analyzing the curve's variation patterns over a day or longer period, one or more consecutive time periods with low carbon density are identified, and these time periods are determined and marked as optimal low-carbon charging periods. Charging during times of low carbon density means that the unit carbon emission corresponding to the user's electricity consumption is minimized, making it the charging choice with the best environmental benefits. Then, the user's actual charging behavior is evaluated. Specifically, the start and end times of each user's charging are compared with the determined optimal low-carbon charging periods to calculate the degree of conformity between their charging behavior and these periods. For example, this can be measured by statistically analyzing the proportion of charging time falling within the optimal low-carbon periods. Finally, based on this degree of conformity, the incentive coefficients associated with user rights are dynamically adjusted. This incentive coefficient is a variable multiplier or addend used to influence the benefits users can enjoy in subsequent charging services, such as points redemption rates, service fee discounts, or priority access. The higher the compliance level, the greater the increase in the incentive coefficient, thus providing users with more positive benefits; conversely, the incentive coefficient may remain at the basic level or be reduced.

[0080] The aforementioned method for quantifying the carbon footprint of electric vehicles (EVs) acquires real-time power and carbon emission factors at each node of the distribution network and uses a dynamic weighted algorithm to perform spatiotemporal fusion of carbon emission factors from multiple nodes. This enables the construction of a real-time carbon state analysis model that reflects the continuous evolution of carbon emission intensity at charging stations over time, outputting a curve characterizing the dynamic fluctuations in carbon density. This overcomes the technical shortcomings of traditional methods that use fixed average carbon emission factors and cannot characterize the spatiotemporal heterogeneity and volatility of distribution network carbon intensity. By matching the charging time period characteristic data of EV users with this dynamic carbon density curve over time, a precise correlation between charging behavior and real-time carbon intensity can be established. Furthermore, based on charging power data, the carbon footprint of the entire charging lifecycle can be quantified and calculated time-by-time, solving the problems of existing carbon accounting methods that struggle to trace the specific charging process of users and lack refined measurement methods. By creating personalized carbon accounts for users based on the quantified carbon footprint and dynamically identifying the optimal low-carbon charging period according to the carbon density curve, and then dynamically adjusting the incentive coefficient linked to benefits based on the degree of conformity between the user's actual charging behavior and the low-carbon period, a closed-loop, transparent, and dynamically responsive carbon incentive feedback mechanism can be constructed. This mechanism not only makes users' low-carbon behavior quantifiable, recordable, and incentivized, significantly enhancing users' initiative in participating in green electricity consumption and demand-side response, but also provides the distribution network with a flexible adjustment mechanism based on price signals, effectively guiding charging loads to actively match peak output of new energy, promoting carbon emission reduction in the distribution network and local consumption of new energy, and achieving synergistic effects of carbon reduction on the user side and operation optimization on the grid side.

[0081] In one exemplary embodiment, acquiring power distribution network energy data of charging stations and user behavior characteristic data of electric vehicles includes:

[0082] By deploying edge computing nodes at charging stations, real-time power and carbon emission factors of each node in the distribution network are collected at a preset sampling frequency.

[0083] User behavior data is collected synchronously through the vehicle information system and charging pile network. The user behavior data includes user charging time, driving mileage and energy consumption per unit mileage under different driving conditions.

[0084] Outlier cleaning is performed on the collected distribution network energy data and user behavior characteristic data to remove data points that deviate from the statistical distribution threshold.

[0085] Specifically, firstly, regarding the collection of distribution network energy data, edge computing nodes deployed at charging stations are used as the core units for data acquisition and processing. These edge computing nodes establish real-time communication with intelligent measurement terminals or data acquisition systems at key nodes in the distribution network, periodically collecting real-time active power, reactive power, and corresponding carbon emission factor data from each node according to a preset sampling frequency (e.g., once per minute or once per minute). The advantage of using edge computing nodes is that real-time processing can be performed close to the data source, reducing network transmission latency and cloud computing pressure, providing low-latency, high-timeliness data support for subsequent real-time carbon status analysis.

[0086] Secondly, regarding the collection of user behavior data, two parallel data collection channels are used: one is through the vehicle's own operating data, including mileage and energy consumption per unit mileage under different driving conditions (such as energy consumption per 100 kilometers in urban and highway conditions), obtained through the vehicle information system (such as T-Box or vehicle diagnostic interface); the other is through the charging pile network to obtain user charging behavior data, including the start time, end time, charging duration, and charging power curve of each charge. These two types of data are linked through user identifiers to form a complete user behavior profile.

[0087] Finally, in the data preprocessing stage, outlier cleaning is performed on all collected raw data to ensure the accuracy and reliability of subsequent analysis. For each type of data indicator (such as power value, carbon emission factor, charging time, energy consumption per unit mile, etc.), a statistical model based on historical data distribution is established in advance to determine its reasonable numerical range or fluctuation threshold. Data points that exceed the preset threshold (e.g., more than three standard deviations) or clearly do not conform to physical laws (e.g., negative power value, charging time exceeding 24 hours, etc.) are identified as outliers and removed or marked for correction.

[0088] In this embodiment, edge computing nodes are deployed to collect power and carbon emission factors of distribution network nodes in real time at a preset frequency. Combined with the vehicle information system and charging pile network, user charging time, driving mileage and energy consumption data are obtained synchronously. On this basis, outlier cleaning is performed on the collected data to remove data points that deviate from the statistical distribution threshold. This can effectively eliminate noise introduced by sensor failure or communication interference and ensure the accuracy, integrity and consistency of the input data.

[0089] In an exemplary embodiment, based on distribution network energy data, a dynamic weighting algorithm is used to weight and fuse the carbon emission factors of multiple nodes to construct a real-time carbon state analysis model, and output a dynamic carbon density curve characterizing the change of carbon density of charging stations over time, including:

[0090] Based on the distribution network energy data, calculate the energy contribution rate of each node in the distribution network to the total power supply of the charging station.

[0091] The energy contribution rate was normalized and used as the dynamic weighting coefficient of the carbon emission factor at each node.

[0092] The real-time power and carbon emission factor of each node in the distribution network are weighted and accumulated using dynamic weighting coefficients to obtain the total carbon emissions of the distribution network supplying power to the charging station.

[0093] The total carbon emissions are divided by the total charging power of the charging station to obtain the real-time carbon density corresponding to the unit charging energy, and a dynamic carbon density curve is generated with time as the horizontal axis and carbon density as the vertical axis.

[0094] Specifically, firstly, it is necessary to clarify that the power supply to charging stations does not come from a single node, but is jointly supplied by multiple nodes in the distribution network. Therefore, it is essential to quantify the actual contribution of each node to the power supply of the charging station. Based on the real-time power data collected from each node in the distribution network, the power component flowing to the charging station from each node can be determined using power flow tracing algorithms or calculation methods based on power allocation factors. This allows for the calculation of the energy contribution rate of that node to the total power supply of the charging station. This contribution rate reflects the proportion of power supplied by that node to the total power consumption of the station at a given moment. Based on this, the energy contribution rates of all nodes are normalized to ensure that their sum is 1, and the normalized value is used as the dynamic weighting coefficient of the node's carbon emission factor. The core characteristic of this weighting is its dynamism; it automatically adjusts with changes in the real-time power of each node, thus accurately reflecting the actual position of each node in the power supply structure at different times. Subsequently, using this dynamic weighting coefficient, the real-time power data of each node is weighted and accumulated with the corresponding carbon emission factor: First, the power of each node is multiplied by its carbon emission factor to obtain the carbon emissions corresponding to the power supply of that node; then, the carbon emissions of all nodes are accumulated to obtain the total carbon emissions generated by the distribution network supplying power to the charging station at the current moment. Based on this, the calculated total carbon emissions are divided by the total charging power of the charging station at that moment to obtain the real-time carbon density corresponding to the unit charging energy. Finally, the carbon density values ​​calculated at all moments are connected in chronological order, and a continuously changing dynamic carbon density curve is plotted with time as the horizontal axis and carbon density as the vertical axis. This curve visually depicts the fluctuation pattern of the cleanliness of electricity use at the charging station over a daily or longer time scale.

[0095] In this embodiment, a dynamic weighted fusion algorithm based on energy contribution rate can be used to track the changes in the contribution of each node in the distribution network to the power supply of the charging station in real time, and use this as a weight to dynamically synthesize the carbon emission factor of each node, thereby overcoming the defect that traditional fixed weight or average carbon emission factor cannot reflect the spatiotemporal heterogeneity of carbon intensity in the distribution network.

[0096] In one exemplary embodiment, such as Figure 3 As shown, the charging periods in user behavior characteristic data are matched with the carbon density dynamic curve over time to establish a correlation between charging periods and corresponding carbon densities. Based on the correlation and charging power data, the carbon footprint quantification value of electric vehicles throughout the charging process is calculated, including:

[0097] Step S302: Extract the time series of user charging periods from user behavior feature data;

[0098] Step S304: Call the time series matching algorithm to align the time series with the carbon density dynamic curve on the time axis;

[0099] Step S306: Within the aligned time interval, obtain the carbon density value and the real-time charging power of the charging pile at each moment.

[0100] Step S308: Calculate the charging energy for each minute period based on the real-time charging power of the charging pile, multiply it by the corresponding carbon density value, and sum them up to obtain the quantified value of the user's carbon footprint throughout the charging process.

[0101] Specifically, firstly, a complete time series of a single charging process is extracted from the acquired user behavior feature data. This series includes the charging start time, end time, and charging duration, forming a continuous time interval. Simultaneously, the charging power data corresponding to each moment during the charging process is organized into a rate sequence in chronological order. Secondly, a time series matching algorithm is used to precisely align the user's charging time series with the constructed carbon density dynamic curve on the time axis. This ensures that every moment during the charging period can find a corresponding carbon density value on the carbon density dynamic curve, establishing a correspondence between charging time and real-time carbon intensity. Specifically, if the time resolution of the carbon density dynamic curve is at the minute level, and the timestamp of the user charging data is also at the minute level, point-to-point matching is performed directly; if the time resolutions are inconsistent, interpolation or resampling methods are used to unify the time base before alignment. Then, within the aligned time interval, for each time point or each small time interval, two key data points are simultaneously acquired: the real-time charging power value recorded by the charging pile at that moment, and the corresponding carbon density value. Finally, the carbon footprint quantification value is accumulated and calculated. The specific calculation method is as follows: multiply the duration of each small time period (e.g., 1 minute or 15 minutes) by the average charging power within that time period to obtain the charging energy value within that time period; then multiply this charging energy value by the carbon density value corresponding to that time period to obtain the carbon emissions generated during that time period; and finally, sum up the carbon emissions of all small time periods to obtain the total carbon footprint quantification value generated by the user during the entire charging process.

[0102] In this embodiment, by precisely aligning the time series of the user's charging period with the dynamic curve of carbon density, and acquiring the charging power and corresponding carbon density value at each moment, a precise correlation between charging behavior and real-time carbon intensity can be established. Based on this, by multiplying the charging energy of each minute time period by the corresponding carbon density value and summing them, a refined integral measurement of carbon emissions throughout the entire electric vehicle charging process is achieved. This overcomes the shortcomings of traditional methods that use fixed average carbon emission factors for coarse calculations, and can truly reflect the actual environmental impact of users charging during different carbon intensity periods.

[0103] In one exemplary embodiment, the charging energy for each minute time period is calculated based on the real-time charging power of the charging pile, multiplied by the corresponding carbon density value, and accumulated to obtain the quantified value of the user's carbon footprint throughout the charging process, including:

[0104] Based on the changing nodes of the carbon density dynamic curve, the user's charging period is divided into multiple consecutive sub-periods;

[0105] The average carbon density, charging power, and duration for each sub-period are obtained, and the carbon emissions are calculated based on the average carbon density, charging power, and duration.

[0106] The carbon emissions of all sub-periods are summed up to output the quantified carbon footprint value of a single charging behavior.

[0107] Specifically, the process of refining the carbon footprint calculation based on the piecewise integral approach requires starting from the changing characteristics of the carbon density dynamic curve and dividing the charging period into multiple sub-intervals with approximately constant carbon intensity for piecewise accumulation. First, it is necessary to identify and extract the changing nodes of the carbon density dynamic curve. These nodes refer to the points where the carbon density value changes significantly, such as inflection points, step transition points, or boundary points determined according to a preset change threshold. By using these nodes as the segmentation criteria, the continuous charging period for the user is divided into multiple continuous and non-overlapping sub-periods. The carbon density value within each sub-period is relatively stable or changes gradually, so the average carbon density of that sub-period can be used to approximately characterize its overall carbon intensity level.

[0108] Secondly, for each sub-time period, three key parameters are obtained: the average carbon density within that sub-time period (obtained by taking the arithmetic mean of the carbon density values ​​at each moment within that sub-time period), the average charging power recorded by the charging station within that sub-time period, and the duration of that sub-time period. Based on these three parameters, the carbon emissions for that sub-time period can be calculated using the following formula: Carbon emissions = Average charging power × Duration × Average carbon density. This calculation method simplifies the original process of point-by-point integration into piecewise linear accumulation, reducing computational complexity while maintaining calculation accuracy. Finally, by summing the carbon emissions from all sub-time periods, the quantified carbon footprint of the user's entire charging activity from start to end can be obtained.

[0109] For example, the charging time is divided into n consecutive sub-periods based on the nodes of carbon density change, denoted as [t1,t2), [t2,t3), ..., [tn-1,tn]. The real-time carbon density of the corresponding sub-period is denoted as ρ1, ρ2, ..., ρn. The real-time charging power Pi of the charging pile in each sub-period is obtained. Combined with the duration of the sub-period Δti = ti+1-ti, the charging energy Ei = Pi × Δti of each sub-period is calculated. Based on the real-time carbon density and charging energy of the sub-period, the carbon emission Ci = ρi × Ei of a single sub-period is calculated. The carbon emissions of all sub-periods are accumulated to obtain the quantified result C of the user's carbon footprint throughout the entire charging process. 总 .

[0110] In this embodiment, the user's charging period is adaptively divided into multiple continuous sub-periods based on the changing nodes of the carbon density dynamic curve. The average carbon density, charging power, and duration of each sub-period are calculated separately. This effectively reduces the computational complexity caused by high-frequency fluctuations in carbon density while ensuring the accuracy of carbon footprint quantification. This piecewise integration method achieves a balance between computational efficiency and measurement accuracy, simplifying the continuous calculation process that originally required point-by-point integration into piecewise linear accumulation, ultimately outputting the quantified carbon footprint value of a single charging behavior.

[0111] In one exemplary embodiment, a user carbon account is created based on a quantified carbon footprint value. The optimal low-carbon charging period is determined according to a dynamic carbon density curve. Furthermore, the incentive coefficient associated with user benefits is dynamically adjusted based on the degree to which the user's charging behavior aligns with the optimal low-carbon charging period. This includes:

[0112] Assign a unique identifier to the user's carbon account and link it to vehicle information and charging records;

[0113] The optimal low-carbon charging period is determined based on the carbon density dynamic curve, and the user's actual charging period is monitored.

[0114] Based on the number of times the user's actual charging time period matches the optimal low-carbon charging time period, and based on the deviation between the carbon footprint quantification value and the preset industry average, the incentive coefficient is adjusted upward, downward, or reset to zero.

[0115] The adjusted incentive coefficient will be mapped to user benefits, which include a reduction in charging fees or a carbon credit redemption limit.

[0116] Specifically, firstly, a globally unique identifier is assigned to each user's carbon account. This identifier is linked to the vehicle identification number, license plate number, or user's mobile phone number and associated with the user's charging history, forming a traceable and accumulative personal carbon asset management file. Secondly, based on the generated dynamic carbon density curve, by analyzing the curve's variation patterns over a day or longer period, continuous periods with consistently low carbon density are identified and marked as optimal low-carbon charging periods. Simultaneously, the actual start and end times of each user's charge are monitored in real time, creating a record of the user's actual charging periods. Then, the incentive coefficient is dynamically adjusted based on two core evaluation dimensions: firstly, the number of times or the proportion of matching between the user's actual charging periods and the optimal low-carbon charging periods on the time axis; and secondly, the relative deviation between the user's quantified carbon footprint value for a single charge and the preset industry average carbon footprint value (a benchmark value under the same vehicle type and mileage conditions). Based on the comprehensive evaluation results of these two dimensions, the incentive coefficient is dynamically adjusted: if a user frequently charges during low-carbon periods and their carbon footprint is significantly better than the industry average, their incentive coefficient is increased; if a user's charging behavior deviates significantly from low-carbon periods and their carbon footprint is higher than the industry average, their incentive coefficient is decreased; if abnormal charging behavior or suspected carbon footprint data falsification is detected, the incentive coefficient can be reset to zero as a penalty. Finally, the adjusted incentive coefficient is mapped to specific user benefits, such as calculating the reduction ratio of charging service fees based on the incentive coefficient, or redeeming carbon credits at a certain rate for subsequent consumption.

[0117] In this embodiment, a traceable and accumulative personal carbon asset management file is established by assigning a unique identifier to each user's carbon account and binding it to vehicle information and charging records. The optimal low-carbon charging period is accurately identified based on the dynamic carbon density curve, and the incentive coefficient is dynamically adjusted according to the degree of matching between the user's actual charging period and that period, as well as the deviation of the quantified carbon footprint value from the industry average. This mechanism directly links users' low-carbon charging behavior with actual benefits such as charging fee reductions and carbon credit redemption, forming a closed-loop incentive system. This effectively guides users to actively shift their charging behavior to low-carbon periods, enhances users' enthusiasm for participating in green electricity consumption, and provides the distribution network with flexible adjustment capabilities based on market signals.

[0118] In one exemplary embodiment, the adjustment rule for the excitation coefficient includes:

[0119] Set the baseline and upper limit values ​​for the incentive coefficient;

[0120] If the carbon footprint quantification value of a single charge is lower than the first preset industry average, the incentive coefficient will be increased by the first adjustment range.

[0121] When the optimal low-carbon charging period is selected multiple times in a row, the incentive coefficient is superimposed with the second adjustment range;

[0122] If the quantified carbon footprint of a single charge is higher than the second threshold of the industry average, the incentive coefficient will be reduced by the third adjustment level.

[0123] If the optimal low-carbon charging period is not selected multiple times in a row, the second adjustment range that has been accumulated will be reset.

[0124] Specifically, a baseline value for the incentive coefficient is first preset as the initial starting point for all users, and an upper limit is set as the capping threshold for the incentive coefficient to prevent excessive incentives. Based on this, user behavior is evaluated from two dimensions: positive incentives and negative constraints. Regarding positive incentives, when a user's carbon footprint quantification value for a single charge is detected to be lower than the first preset industry average, it indicates that the charging behavior is more low-carbon and environmentally friendly compared to similar users. In this case, the incentive coefficient is increased by the first adjustment level to reward the user's green choice. If a user not only performs well in a single instance but also actively chooses to charge during the optimal low-carbon charging period multiple times consecutively, it indicates that they have formed a stable low-carbon behavior habit. In this case, a second adjustment level is added to the existing incentive to further increase the incentive intensity, reflecting a progressive reward for continuous green behavior. Regarding negative constraints, if a user's carbon footprint quantification value for a single charge is higher than the second threshold of the industry average, it indicates that the environmental impact of the charging behavior is significant. In this case, the incentive coefficient is decreased by the third adjustment level, forming a moderate penalty for high-carbon behavior. In addition, if a user fails to select the optimal low-carbon charging period multiple times in a row, the system will perform a reset operation. However, this reset does not reduce the entire incentive coefficient to zero, but only removes the second adjustment range that is superimposed due to the continuous selection of low-carbon periods. The baseline value is still retained, thus giving users a chance to accumulate green behavior again while punishing bad behavior.

[0125] For example, using carbon footprint quantification results as input, a benchmark and upper limit for the incentive coefficient can be set. If a user's charging carbon footprint is 10% lower than the industry average, the incentive coefficient will increase by 20%. If a user chooses the optimal low-carbon period for charging three times in a row, an additional 15% incentive coefficient will be added. If a user's carbon footprint for a single charge is 20% higher than the industry average, the incentive coefficient will decrease by 10%. If a user does not choose the optimal low-carbon period for charging twice in a row, the additional incentive coefficient will be cleared. The incentive coefficient is directly related to user benefits, including charging fee reductions and carbon credit redemption. When the incentive coefficient reaches the upper limit, it will no longer increase.

[0126] In this embodiment, by setting a baseline and upper limit for the incentive coefficient, and based on multiple dimensions such as the deviation of the user's carbon footprint from the industry average and whether low-carbon charging periods are selected, the incentive coefficient is dynamically adjusted in tiers, including increasing, adding, decreasing, and partially resetting. This rule enables refined quantitative evaluation and differentiated incentives for users' low-carbon behavior. This mechanism can accurately guide users to proactively optimize their charging habits, forming a virtuous cycle and effectively improving the system's incentive efficiency and guidance effect on users' low-carbon behavior.

[0127] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0128] Based on the same inventive concept, this application also provides an electric vehicle carbon footprint quantification device for implementing the above-described electric vehicle carbon footprint quantification method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the electric vehicle carbon footprint quantification device provided below can be found in the limitations of the electric vehicle carbon footprint quantification method described above, and will not be repeated here.

[0129] In one exemplary embodiment, such as Figure 4 As shown, a device for quantifying the carbon footprint of electric vehicles is provided, comprising:

[0130] The acquisition module 402 is used to acquire the distribution network energy data of the charging station and the user behavior characteristic data of electric vehicles; the distribution network energy data includes at least the real-time power and carbon emission factor of each node of the distribution network;

[0131] The model building module 404 is used to construct a real-time carbon status analysis model based on distribution network energy data, using a dynamic weighting algorithm to weight and fuse the carbon emission factors of multiple nodes, and output a dynamic carbon density curve characterizing the change of carbon density of charging stations over time.

[0132] The establishment and calculation module 406 is used to perform time series matching between the charging period in the user behavior feature data and the carbon density dynamic curve, establish the correlation between the charging period and the corresponding carbon density, and calculate the carbon footprint quantification value of the electric vehicle in the whole charging process based on the correlation and charging power data.

[0133] The adjustment module 408 is used to create a user carbon account based on the carbon footprint quantification value, determine the optimal low-carbon charging period according to the carbon density dynamic curve, and dynamically adjust the incentive coefficient associated with user rights based on the degree of conformity between the user's charging behavior and the optimal low-carbon charging period.

[0134] In an exemplary embodiment, the acquisition module 402 is specifically used to collect real-time power and carbon emission factors of each node in the distribution network at a preset sampling frequency through edge computing nodes deployed at the charging station; synchronously collect user behavior feature data through the vehicle information system and the charging pile network, including user charging time, driving mileage and energy consumption per unit mileage under different driving conditions; and perform outlier cleaning on the collected distribution network energy data and user behavior feature data to remove data points that deviate from the statistical distribution threshold.

[0135] In an exemplary embodiment, the model building module 404 is specifically used to calculate the energy contribution rate of each node in the distribution network to the total power supply of the charging station based on the distribution network energy data; normalize the energy contribution rate and use it as the dynamic weighting coefficient of the carbon emission factor of each node; use the dynamic weighting coefficient to weight and accumulate the real-time power and carbon emission factor of each node in the distribution network to obtain the total carbon emission of the distribution network supplying power to the charging station; divide the total carbon emission by the total charging power of the charging station to obtain the real-time carbon density corresponding to the unit charging energy, and generate a dynamic carbon density curve with time as the horizontal axis and carbon density as the vertical axis.

[0136] In an exemplary embodiment, the establishment and calculation module 406 is specifically used to extract the time series of user charging periods from user behavior feature data; call a time series matching algorithm to align the time series with the carbon density dynamic curve on the time axis; within the aligned time interval, obtain the carbon density value and real-time charging power of the charging pile corresponding to each moment; calculate the charging energy of each small time period based on the real-time charging power of the charging pile, multiply it by the corresponding carbon density value, and accumulate it to obtain the quantified value of the user's carbon footprint throughout the entire charging process.

[0137] In an exemplary embodiment, the establishment and calculation module 406 is further configured to divide the user's charging period into multiple consecutive sub-periods based on the change nodes of the carbon density dynamic curve; obtain the average carbon density, charging power and duration of each sub-period, and calculate the carbon emissions based on the average carbon density, charging power and duration; and sum up the carbon emissions of all sub-periods to output the carbon footprint quantification value of a single charging behavior.

[0138] In an exemplary embodiment, the adjustment module 408 is specifically used for: assigning a unique identifier to the user's carbon account and binding vehicle information and charging records; determining the optimal low-carbon charging period based on the carbon density dynamic curve and monitoring the user's actual charging period; adjusting, lowering, or resetting the incentive coefficient based on the number of times the user's actual charging period matches the optimal low-carbon charging period and the deviation between the carbon footprint quantification value and the preset industry average; and mapping the adjusted incentive coefficient to user rights, which include a charging fee reduction ratio or carbon credit redemption quota.

[0139] In an exemplary embodiment, the adjustment rules for the incentive coefficient include: setting a baseline value and an upper limit value for the incentive coefficient; increasing the incentive coefficient by a first adjustment magnitude when the quantified carbon footprint value of a single charge is lower than a first preset industry average; adding a second adjustment magnitude to the incentive coefficient when the optimal low-carbon charging period is selected multiple times consecutively; decreasing the incentive coefficient by a third adjustment magnitude when the quantified carbon footprint value of a single charge is higher than a second threshold of the industry average; and resetting the added second adjustment magnitude to zero when the optimal low-carbon charging period is not selected multiple times consecutively.

[0140] The modules in the aforementioned electric vehicle carbon footprint quantification and calculation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0141] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores power distribution network energy data for charging stations and user behavior data for electric vehicles. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for quantifying the carbon footprint of electric vehicles.

[0142] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0143] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0144] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0145] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0146] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0147] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0148] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0149] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for quantifying the carbon footprint of electric vehicles, characterized in that, The method includes: Acquire power distribution network energy data and electric vehicle user behavior data for charging stations; the power distribution network energy data includes at least the real-time power and carbon emission factor of each node in the power distribution network. Based on the energy data of the distribution network, a dynamic weighting algorithm is used to weight and fuse the carbon emission factors of multiple nodes to construct a real-time carbon status analysis model and output a dynamic carbon density curve that characterizes the change of carbon density of the charging station over time. The charging time period in the user behavior feature data is matched with the carbon density dynamic curve in time series to establish the correlation between the charging time period and the corresponding carbon density, and the carbon footprint quantification value of the electric vehicle in the whole charging process is calculated based on the correlation and charging power data. Based on the quantified carbon footprint value, a user carbon account is created, the optimal low-carbon charging period is determined according to the dynamic carbon density curve, and the incentive coefficient associated with user rights is dynamically adjusted according to the degree to which the user's charging behavior conforms to the optimal low-carbon charging period.

2. The method according to claim 1, characterized in that, The acquisition of power grid energy data for charging stations and user behavior data of electric vehicles includes: By deploying edge computing nodes at charging stations, the real-time power and carbon emission factor of each node in the distribution network are collected at a preset sampling frequency. User behavior data is collected synchronously through the vehicle information system and charging pile network. The user behavior data includes user charging time, driving mileage and energy consumption per unit mileage under different driving conditions. Outlier cleaning is performed on the collected distribution network energy data and user behavior characteristic data to remove data points that deviate from the statistical distribution threshold.

3. The method according to claim 1, characterized in that, Based on the distribution network energy data, a dynamic weighted algorithm is used to weight and fuse the carbon emission factors of multiple nodes to construct a real-time carbon state analysis model, and output a dynamic carbon density curve characterizing the change of carbon density of the charging station over time, including: Based on the energy data of the distribution network, calculate the energy contribution rate of each node of the distribution network to the total power supply of the charging station; The energy contribution rate is normalized and used as the dynamic weighting coefficient of the carbon emission factor at each node. The real-time power and carbon emission factor of each node in the distribution network are weighted and accumulated using the dynamic weighting coefficient to obtain the total carbon emissions of the distribution network supplying power to the charging station. Divide the total carbon emissions by the total charging power of the charging station to obtain the real-time carbon density corresponding to the unit charging energy, and generate a dynamic carbon density curve with time as the horizontal axis and carbon density as the vertical axis.

4. The method according to claim 1, characterized in that, The step of performing time-series matching between the charging periods in the user behavior feature data and the carbon density dynamic curve to establish a correlation between the charging periods and the corresponding carbon densities, and calculating the quantified carbon footprint of the electric vehicle throughout the charging process based on the correlation and charging power data, includes: Extract the time series of user charging periods from the user behavior feature data; The time series matching algorithm is invoked to align the time series with the carbon density dynamic curve on the time axis; Within the aligned time interval, obtain the carbon density value and real-time charging power of the charging pile at each moment; Based on the real-time charging power of the charging pile, the charging energy for each minute period is calculated, multiplied by the corresponding carbon density value, and then accumulated to obtain the quantified value of the user's carbon footprint throughout the charging process.

5. The method according to claim 4, characterized in that, The process involves calculating the charging energy for each minute period based on the real-time charging power of the charging pile, multiplying it by the corresponding carbon density value, and summing the results to obtain the quantified carbon footprint of the user throughout the charging process. This includes: Based on the changing nodes of the carbon density dynamic curve, the user charging period is divided into multiple consecutive sub-periods; The average carbon density, charging power, and duration of each sub-period are obtained, and the carbon emissions are calculated based on the average carbon density, the charging power, and the duration. The carbon emissions of all sub-periods are summed up to output the quantified carbon footprint value of a single charging behavior.

6. The method according to claim 1, characterized in that, Based on the quantified carbon footprint value, a user carbon account is created; the optimal low-carbon charging period is determined according to the dynamic carbon density curve; and the incentive coefficient associated with user benefits is dynamically adjusted according to the degree to which the user's charging behavior conforms to the optimal low-carbon charging period, including: Assign a unique identifier to the user's carbon account and bind it to vehicle information and charging records; The optimal low-carbon charging period is determined based on the carbon density dynamic curve, and the user's actual charging period is monitored. Based on the number of times the user's actual charging period and the optimal low-carbon charging period are matched, and based on the deviation between the carbon footprint quantification value and the preset industry average, the incentive coefficient is adjusted upward, downward, or cleared. The adjusted incentive coefficients are mapped to user benefits, which include a charging fee reduction ratio or a carbon credit redemption limit.

7. The method according to claim 6, characterized in that, The adjustment rules for the incentive coefficient include: Set the baseline and upper limit values ​​for the incentive coefficient; If the carbon footprint quantification value of a single charge is lower than the first preset industry average, the incentive coefficient will be increased by a first adjustment range. When the optimal low-carbon charging period is selected multiple times in a row, the excitation coefficient is superimposed with a second adjustment magnitude; If the quantified carbon footprint of a single charge is higher than the second threshold of the industry average, the incentive coefficient will be reduced by a third adjustment. If the optimal low-carbon charging period is not selected multiple times in a row, the superimposed second adjustment range will be reset to zero.

8. A device for quantifying and calculating the carbon footprint of electric vehicles, characterized in that, The device includes: The acquisition module is used to acquire the distribution network energy data of the charging station and the user behavior characteristic data of electric vehicles; the distribution network energy data includes at least the real-time power and carbon emission factor of each node in the distribution network; The model building module is used to construct a real-time carbon status analysis model based on the distribution network energy data, by using a dynamic weighting algorithm to weight and fuse the carbon emission factors of multiple nodes, and output a dynamic carbon density curve characterizing the change of carbon density of the charging station over time. The module for establishing and calculating is used to perform time series matching between the charging period in the user behavior feature data and the carbon density dynamic curve, establish the correlation between the charging period and the corresponding carbon density, and calculate the quantified value of the carbon footprint of the electric vehicle in the entire charging process based on the correlation and the charging power data. The adjustment module is used to create a user carbon account based on the carbon footprint quantification value, determine the optimal low-carbon charging period according to the carbon density dynamic curve, and dynamically adjust the incentive coefficient associated with user rights based on the degree of conformity between the user's charging behavior and the optimal low-carbon charging period.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, 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 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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