Rental new energy automobile environmental protection benefit quantitative feedback system
By constructing a quantitative feedback system for the environmental benefits of leasing new energy vehicles, and using an environmental effort index and multi-objective optimization algorithm to generate personalized incentives, the system solves the problems of single quantification of environmental benefits and insufficient evaluation fairness in existing technologies, thereby improving user participation and incentive effects.
Patent Information
- Application Number
- CN202511685576.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-18
- Publication Date
- 2026-02-10
AI Technical Summary
Existing technologies lack a single method for quantifying the environmental benefits of leasing new energy vehicles, have insufficient fairness in the evaluation system, limited user incentive effects, and lack relative comparison and social interaction functions based on differences in user behavior.
A quantitative feedback system for the environmental benefits of leasing new energy vehicles is constructed, including a module for quantifying environmental benefits, a module for analyzing user behavior, a module for generating dynamic incentives, and a cross-platform interaction module. Through the environmental effort index, multi-objective optimization algorithms, and data from social platforms, personalized incentive factors and visual reports are generated.
This has ensured the scientific and fair evaluation of environmental contributions, enhanced user participation and engagement, and created a virtuous cycle of mechanisms to promote environmentally friendly behavior.
Smart Images
Figure CN121504576A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of big data analysis technology, specifically relating to a quantitative feedback system for the environmental benefits of leasing new energy vehicles. Background Technology
[0002] New energy vehicle leasing is an important area of deep integration between green transportation and the sharing economy. Its core lies in reducing carbon emissions from traditional fuel vehicles through vehicle sharing, thereby promoting the sustainable development of urban transportation. This model involves multiple technical aspects, including vehicle dispatching, user services, and environmental benefit assessment.
[0003] Among these, quantifying the environmental benefits of renting new energy vehicles and establishing a user feedback mechanism are key technological directions for enhancing user participation and promoting environmentally friendly behavior. This technology aims to accurately quantify and visualize the environmental benefits, such as carbon emission reductions, generated by users' car rental activities, thereby incentivizing users to continuously choose green travel options.
[0004] Existing technologies typically only display absolute emission reduction data based on users' individual car rental behavior, lacking relative comparisons and social interaction functions based on differences in user behavior. This results in a simplistic incentive approach and insufficient user engagement. Furthermore, existing evaluation systems often employ simplistic emission reduction rankings, failing to consider differences in objective factors such as vehicle models and user usage frequency. This fails to scientifically reflect users' actual environmental efforts, reducing the fairness and incentive effect of the evaluation.
[0005] Therefore, how to build a system that can scientifically quantify users' environmental contributions and provide effective feedback and incentives has become a pressing technical challenge in this field. Summary of the Invention
[0006] The purpose of this invention is to provide a quantitative feedback system for the environmental benefits of leasing new energy vehicles, so as to solve the technical contradictions of existing technologies, such as the single method of quantifying environmental benefits, insufficient fairness of the evaluation system, and limited user incentive effect.
[0007] This invention provides a system for quantifying the environmental benefits of rented new energy vehicles. The system includes an environmental benefit quantification module, a user behavior analysis module, a dynamic incentive generation module, and a cross-platform interaction module. The environmental benefit quantification module collects raw energy consumption data and geographic location information during the operation of rented new energy vehicles, and calculates the absolute carbon emission reduction generated by a single user rental based on a preset benchmark carbon emission model and real-time grid carbon intensity factor.
[0008] The user behavior analysis module receives absolute carbon emission reduction data from the environmental benefit quantification module and combines it with the user's historical car rental records, the technical parameters of the selected vehicle model, and the average rental duration in the same region during the same period to build an assessment model of the user's environmental efforts.
[0009] This model generates a standardized environmental effort index by calculating the percentage of a user's actual emission reductions relative to their theoretical maximum emission reduction potential.
[0010] The dynamic incentive generation module receives the environmental effort index output by the user behavior analysis module and integrates user relationship graph data from third-party social platforms. This module incorporates a multi-objective optimization algorithm to calculate personalized dynamic incentive factors based on the environmental effort index and the user's social influence.
[0011] The cross-platform interaction module integrates incentive factors from the dynamic incentive generation module with raw data from the environmental benefit quantification module to generate a multi-dimensional, visualized environmental report, which is then pushed to user terminals and designated social platforms via an application programming interface.
[0012] Furthermore, the specific calculation process of the environmental benefit quantification module is as follows: First, the vehicle's mileage, energy consumption data, and charging pile identifiers are collected in real time through the vehicle-mounted remote information processing unit.
[0013] Subsequently, the State Grid Corporation of China's real-time regional power grid carbon emission factor database was consulted to obtain the average carbon intensity of the power grid corresponding to the current charging event.
[0014] Next, the benchmark carbon emission model is invoked, which stores the average fuel consumption and carbon emission coefficient of mainstream fuel vehicles under the same mileage.
[0015] Finally, the absolute carbon emission reduction of this car rental activity is obtained by subtracting the indirect carbon emissions from the grid corresponding to the actual electricity consumption of the new energy vehicle from the baseline carbon emissions of the fuel vehicle. The result is measured in kgCO2e.
[0016] Furthermore, the construction process of the environmental effort assessment model in the user behavior analysis module is as follows: First, based on the battery capacity and energy consumption per 100 kilometers of the vehicle model selected by the user, combined with the average driving range of this model under typical urban road conditions, the theoretical maximum driving distance of the user's current rental is calculated. Then, based on the theoretical maximum driving distance and the benchmark carbon emission model, the theoretical maximum emission reduction potential of the user's current rental is calculated. Finally, the actual absolute carbon emission reduction obtained by the user is divided by the theoretical maximum emission reduction potential, and then multiplied by 100% to obtain an environmental effort index value between 0 and 100.
[0017] Furthermore, the multi-objective optimization algorithm of the dynamic incentive generation module operates as follows: the algorithm optimizes two objective functions simultaneously. The first objective function is to maximize the overall environmental benefits of the platform, and the second objective function is to balance user participation.
[0018] The algorithm inputs include the current user's environmental effort index, the user's centrality index within their social network, and the recent distribution of effort indices across all users on the platform. A linear weighted average transforms the multi-objective problem into a single-objective problem, with the weighting coefficients dynamically adjusted based on the platform's operational strategy. The final output is a dynamic incentive factor for each user, a scaling factor between 0.5 and 2.0, which will be directly used in the subsequent calculation of incentive integrals.
[0019] Furthermore, the dynamic incentive generation module is also connected to an incentive points database. This module calculates the incentive points a user should receive based on the environmental effort index and the dynamic incentive factor, according to a preset points conversion rule. The points conversion rule is defined as the environmental effort index multiplied by the dynamic incentive factor, and then multiplied by a base points coefficient, where the base points coefficient is set by the platform operator based on the activity cycle.
[0020] Furthermore, the visualized environmental report generated by the cross-platform interactive module comprises three core parts: Part 1 uses a pie chart to display the absolute carbon emission reduction of the user's car rental and its equivalent environmental contribution (equivalent to the number of trees planted). Part 2 uses a radar chart to display the user's scores across multiple dimensions, including environmental effort, rental frequency, and the greenness of the vehicle model chosen. Part 3 displays the trend of the user's environmental ranking within their social circle and details of the incentive points earned.
[0021] Furthermore, the cross-platform interaction module also includes a social challenge engine. Based on historical data from the user behavior analysis module, this engine regularly generates personalized environmental challenges, such as continuous car rental challenges and high-effort maintenance challenges. Challenge progress and completion status are updated in real time to a visual environmental report and synchronized to social platforms via an application programming interface.
[0022] Furthermore, the overall system architecture adopts a microservice design. The environmental benefit quantification module, user behavior analysis module, dynamic incentive generation module, and cross-platform interaction module are deployed as independent microservices, exchanging data through a representative state transmission application programming interface. All data transmission between modules is encrypted, and key data such as user environmental effort index and incentive points are recorded on a distributed ledger to ensure immutability.
[0023] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention eliminates the bias in the evaluation of environmental contribution caused by objective factors such as differences in vehicle model and usage frequency by introducing a standardized evaluation index for environmental effort. This makes the environmental behavior of different users comparable and improves the scientificity and fairness of the evaluation system.
[0024] By applying a multi-objective optimization algorithm in the dynamic incentive generation module, the system can comprehensively consider users' personal environmental performance and social influence to generate personalized incentive factors. This achieves the optimal allocation of incentive resources between promoting overall environmental benefits and maintaining user participation, thereby building a more sustainable mechanism for promoting environmental behavior at the platform level.
[0025] The cross-platform interactive module provides multi-dimensional visualization reports and social challenge functions, which transform personal environmental behavior data into visual content with social dissemination value. This not only enhances users' intuitive perception of their own environmental contributions, but also stimulates users' enthusiasm and stickiness through social comparison and challenge mechanisms, ultimately forming a virtuous cycle ecosystem from quantification to feedback to incentives. Attached Figure Description
[0026] Figure 1 This is a schematic diagram of the overall technical solution architecture of the environmental benefit quantification feedback system for leasing new energy vehicles proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the environmental protection effort assessment model in this invention; Figure 3 This is a schematic diagram of the multi-objective optimization algorithm operation mechanism of the dynamic stimulus generation module in this invention; Figure 4 This is a data interaction flowchart between the environmental benefit quantification module and the user behavior analysis module in this invention; Figure 5 This is a schematic diagram illustrating the multi-dimensional display principle of the cross-platform interaction module in this invention for generating visualized environmental protection reports; Detailed Implementation
[0027] The overall technical architecture of the environmental benefit quantification feedback system for leasing new energy vehicles is attached. Figure 1 As shown, the system consists of an environmental benefit quantification module, a user behavior analysis module, a dynamic incentive generation module, and a cross-platform interaction module. Each module is deployed independently based on a microservice architecture and exchanges data through a representative state transmission application programming interface. The system uses distributed ledger technology to store key data, ensuring the immutability of user environmental effort index and incentive points records.
[0028] The environmental benefit quantification module is responsible for collecting raw energy consumption data and geographical location information during the operation of leased new energy vehicles. This module obtains vehicle mileage, energy consumption data, and charging pile identifiers in real time through the onboard telematics unit.
[0029] Mileage data is obtained from the vehicle's mileage sensor, recording the total distance traveled in a single rental trip in km. Energy consumption data is collected through the battery management system, including total energy consumption and average energy consumption per 100 km, with a data accuracy of 0.1 kWh. The charging pile identifier is used to link to the State Grid's real-time regional power grid carbon emission factor database, which is updated every 15 minutes and stores the average carbon intensity value of the power grid in various regions across the country, in kgCO2e / kWh.
[0030] The environmental benefit quantification module calls the benchmark carbon emission model to calculate absolute carbon emission reductions. The benchmark carbon emission model pre-stores average fuel consumption data and carbon emission coefficients for mainstream gasoline vehicles under the same driving mileage. Fuel consumption data is based on the statistical values of fuel consumption per 100 kilometers published by the National Motor Vehicle Emission Monitoring Platform, and the carbon emission coefficient adopts the standard value of 2.31 kg CO2e per liter of gasoline from the Intergovernmental Panel on Climate Change (IPCC). The calculation first calculates the benchmark carbon emissions based on actual driving mileage and fuel consumption per 100 kilometers of gasoline vehicles, using the following formula: ; Subsequently, the actual carbon emissions are calculated based on the actual electricity consumption of new energy vehicles and the carbon intensity of the power grid. The final absolute carbon emission reduction is the difference between the baseline carbon emissions and the actual carbon emissions. The calculation results are stored in a distributed database in kgCO2e and synchronized to the user behavior analysis module.
[0031] The user behavior analysis module receives absolute carbon emission reduction data from the environmental benefit quantification module and combines it with users' historical car rental records, vehicle model technical parameters, and average rental duration in the region to construct an environmental effort assessment model. The core principle framework of this model is attached. Figure 2 As shown, the model first calculates the theoretical maximum driving distance based on the battery capacity and energy consumption per 100 kilometers of the vehicle model selected by the user.
[0032] Battery capacity data is extracted from the vehicle technical parameter database, and the energy consumption per 100 kilometers uses the certified value of this model under typical urban road conditions. The theoretical maximum driving distance is calculated by dividing the battery capacity by the energy consumption per 100 kilometers and then multiplying by 100.
[0033] The user behavior analysis module calculates the theoretical maximum emission reduction potential based on the theoretical maximum driving distance and the benchmark carbon emission model.
[0034] The calculation process for the theoretical maximum emission reduction potential refers to the benchmark carbon emission calculation method of the environmental benefit quantification module, replacing the actual driving mileage with the theoretical maximum driving distance.
[0035] The Environmental Effort Index is generated by dividing the user's actual absolute carbon emission reduction by the theoretical maximum emission reduction potential and then multiplying by 100.
[0036] The index is a standardized value between 0 and 100, with higher values indicating more efficient user environmental protection behaviors. The index calculation results are transmitted to the dynamic incentive generation module via an encrypted channel.
[0037] The dynamic incentive generation module receives the environmental protection effort index output by the user behavior analysis module and accesses a third-party social platform application interface to obtain user relationship graph data. The multi-objective optimization algorithm mechanism of this module is shown in the attached figure. Figure 3 As shown.
[0038] The algorithm simultaneously optimizes two objectives: maximizing the overall environmental benefits of the platform and balancing user participation.
[0039] Input parameters include the current user's environmental effort index, the user's social network centrality index, and the recent distribution of the platform's user effort index.
[0040] The social network centrality metric is obtained by calculating the eigenvector centrality of users in the relationship graph, and the data is updated once a day.
[0041] The multi-objective optimization algorithm transforms a dual-objective problem into a single-objective problem through linear weighting, with the weight coefficients dynamically adjusted according to the platform's operational strategy. These weight coefficients are stored in a strategy configuration library and can be set to different values based on the activity cycle.
[0042] The algorithm outputs a dynamic incentive factor, which is a floating-point number between 0.5 and 2.0. The calculation process uses a normalized weighted summation method. The dynamic incentive generation module connects to the incentive integral database and calculates the incentive integral value based on the environmental effort index and the dynamic incentive factor.
[0043] The points conversion rule is defined as the environmental protection effort index multiplied by the dynamic incentive factor and then multiplied by the basic points coefficient.
[0044] The base points coefficient is set by the platform operator, with a default value of 0.1 and an adjustment range of 0.05 to 0.2.
[0045] The cross-platform interactive module integrates the incentive factors from the dynamic incentive generation module with the raw data from the environmental benefit quantification module to generate a multi-dimensional, visualized environmental report. The report generation logic is shown in the attached diagram. Figure 5 As shown, it includes a pie chart, radar chart, and ranking trend. Figure 3 A core component.
[0046] The pie chart displays the absolute carbon emission reduction and equivalent tree planting amount of this car rental by the user. The equivalent conversion uses the fact that 1 kg of CO2e is equivalent to the annual carbon sequestration of 0.02 mature trees. The radar chart displays the user's ratings across five dimensions, including environmental effort, rental frequency, and the greenness of the vehicle model selected. The rating data comes from historical analysis results from the user behavior analysis module.
[0047] The cross-platform interaction module incorporates a social challenge engine that generates personalized environmental challenge tasks based on historical data from the user behavior analysis module. The challenge tasks include two types: a continuous car rental challenge and a high-effort maintenance challenge.
[0048] The consecutive car rental challenge requires users to complete more than 3 car rentals within 7 days, while the high effort challenge requires users to have an environmental effort index of more than 80 for each of the 3 consecutive car rentals.
[0049] Challenge progress data is updated in real time to a visualized environmental report and synchronized to social media platforms via an API. The report push frequency is linked to car rental activity, automatically triggering the report generation process after each rental period.
[0050] The data interaction process between the various modules of the system is shown in the appendix. Figure 4 As shown, the environmental benefit quantification module and the user behavior analysis module adopt an asynchronous communication mechanism, with a data transmission delay of less than 200ms. The dynamic incentive generation module and the cross-platform interaction module establish a two-way data channel, supporting real-time adjustment of incentive factors and updating of report content.
[0051] All data transmissions use the Advanced Encryption Standard 256-bit encryption algorithm, with a key rotation cycle of 30 days.
[0052] Distributed ledger nodes are deployed across multiple geographical regions, and a practical Byzantine fault-tolerant consensus mechanism is used to ensure data consistency.
[0053] The hardware deployment scheme for the environmental benefit quantification feedback system for leased new energy vehicles includes an onboard data acquisition terminal, edge computing nodes, and a cloud server cluster. The onboard data acquisition terminal integrates a global positioning system module and a cellular communication module, with a positioning accuracy better than 5 meters. The communication module supports multi-mode transmission of fourth-generation and fifth-generation mobile communication technologies.
[0054] Edge computing nodes are deployed in regional data centers and are responsible for preprocessing energy consumption data and geographic location information.
[0055] The cloud server cluster adopts a containerized deployment approach, with each microservice running independently in an isolated container environment, and resource allocation dynamically adjusted according to the load.
[0056] The system performance metrics include three dimensions: real-time data acquisition, calculation accuracy, and service availability. Real-time data acquisition requires an end-to-end latency of less than 5 seconds from vehicle data generation to cloud reception. Calculation accuracy requires an absolute carbon emission reduction calculation error of no more than 2% and an environmental effort index calculation error of no more than 1%. Service availability is ensured through multi-region load balancing and automatic failover, with a design target of 99.95% annual availability.
[0057] The system monitoring system comprises two subsystems: business indicator monitoring and technical indicator monitoring. Business indicator monitoring tracks user activity and cumulative environmental benefits, while technical indicator monitoring collects data on system response time and resource utilization.
[0058] The data storage architecture of the environmental benefit quantification feedback system for leased new energy vehicles adopts a layered design. Hot data is stored in an in-memory database, including current user session data and real-time intermediate calculation results. Warm data is stored in a relational database, covering basic user information and vehicle technical parameters. Cold data is archived to an object storage service, including historical rental records and environmental report backups. The data backup strategy combines full and incremental backups, with full backups performed weekly and incremental backups performed every 6 hours. The data retention strategy complies with relevant regulations; user behavior data is retained for 5 years, while environmental benefit data is permanently stored.
[0059] The alternative implementation scheme for the environmental benefit quantification feedback system for leased new energy vehicles adopts a combined architecture of an enhanced environmental benefit quantification module and a simplified user behavior analysis module. The enhanced environmental benefit quantification module adds driving behavior analysis functionality to standard data collection. It collects acceleration and angular velocity data through an onboard inertial measurement unit and combines this with GPS trajectory data to identify non-environmentally friendly driving behaviors such as rapid acceleration and sudden braking. The driving behavior data is preprocessed by edge computing nodes and then uploaded to the cloud to correct the absolute carbon emission reduction calculation results.
[0060] The simplified user behavior analysis module focuses on core evaluation dimensions, simplifying the calculation of the environmental effort index to a direct ratio of actual emission reductions to baseline emission reductions. The baseline emission reductions are based on the average emission reduction data for the same vehicle model in the region, sourced from historical platform statistics. This module eliminates the calculation of theoretical maximum emission reduction potential, achieving rapid evaluation through a vehicle energy efficiency grading table. This table divides all vehicle models on the platform into five levels based on energy consumption per 100 kilometers, with each level corresponding to a different baseline adjustment coefficient.
[0061] In this embodiment, the dynamic incentive generation module uses a rule engine instead of a multi-objective optimization algorithm. The rule engine sets fixed incentive factors based on the environmental effort index range and the user's car rental frequency. The rule table contains six combinations of three effort index ranges and two car rental frequency levels, each corresponding to a preset incentive factor value. Rule updates are implemented through the management interface, allowing platform operators to quickly adjust incentive strategies according to activity needs.
[0062] In this embodiment, the cross-platform interaction module provides a basic visual report, with the report content reduced to two core indicators: absolute carbon emission reduction and equivalent tree planting quantity. The report display uses a standardized template, eliminating personalized radar charts and social ranking features. The social challenge engine retains the basic functionality of the continuous car rental challenge, with the challenge cycle adjusted to 5 days and 3 car rentals. Challenge progress is displayed intuitively using a progress bar.
[0063] The system hardware deployment adopts a lightweight approach, integrating a simplified sensor suite into the vehicle-mounted data acquisition terminal, and merging edge computing nodes into the regional gateway device. The cloud service uses a monolithic architecture, with each functional module interacting with data through internal interface calls. Data storage utilizes a unified relational database, employing partitioned table technology to improve query efficiency. System performance metrics have been appropriately adjusted: data acquisition latency has been relaxed to 10 seconds, calculation accuracy requirements remain unchanged, and service availability is set at 99.9%.
[0064] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0065] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A quantitative feedback system for the environmental benefits of leasing new energy vehicles, characterized in that, include: The environmental benefit quantification module is used to collect raw energy consumption data and geographical location information during the operation of rented new energy vehicles, and calculate the absolute carbon emission reduction generated by a user's single car rental behavior based on a preset benchmark carbon emission model and real-time grid carbon intensity factor. The user behavior analysis module receives absolute carbon emission reduction data from the environmental benefit quantification module and combines it with the user's historical car rental records, the technical parameters of the selected vehicle model, and the average rental duration in the same region during the same period to build a user environmental effort assessment model. This model generates a standardized environmental effort index by calculating the percentage of the user's actual emission reduction relative to its theoretical maximum emission reduction potential. The dynamic incentive generation module receives the environmental protection effort index output by the user behavior analysis module and accesses user relationship graph data from third-party social platforms. This module has a built-in multi-objective optimization algorithm to calculate personalized dynamic incentive factors based on the environmental protection effort index and the user's social influence. The cross-platform interaction module integrates incentive factors from the dynamic incentive generation module with raw data from the environmental benefit quantification module to generate a multi-dimensional, visualized environmental report, which is then pushed to user terminals and designated social platforms via an application programming interface.
2. The environmental benefit quantification feedback system for leased new energy vehicles according to claim 1, characterized in that, The specific calculation process of the environmental benefit quantification module is as follows: Real-time collection of vehicle mileage, energy consumption data and charging pile identifiers through the vehicle-mounted remote information processing unit; querying the real-time regional power grid carbon emission factor database released by the State Grid Corporation of China to obtain the average carbon intensity of the power grid corresponding to the current charging event; The benchmark carbon emission model is invoked, which stores the average fuel consumption and carbon emission coefficient of mainstream fuel vehicles under the same mileage. The absolute carbon emission reduction of this car rental behavior is obtained by subtracting the indirect carbon emission from the grid corresponding to the actual electricity consumption of the new energy vehicle from the benchmark carbon emission of the fuel vehicle. The result is measured in kgCO2e.
3. The environmental benefit quantification feedback system for leased new energy vehicles according to claim 1, characterized in that, The construction process of the environmental protection effort assessment model in the user behavior analysis module is as follows: Based on the battery capacity and energy consumption per 100 kilometers of the vehicle model selected by the user, combined with the average driving range of the vehicle model under typical urban road conditions, the theoretical maximum driving distance of the user's current car rental is calculated. Based on the theoretical maximum driving distance and the benchmark carbon emission model, the theoretical maximum emission reduction potential of this car rental by the user is calculated. Divide the actual absolute carbon emission reduction achieved by the user by the theoretical maximum emission reduction potential, and then multiply by 100 to obtain an environmental effort index value between 0 and 100.
4. The environmental benefit quantification feedback system for leased new energy vehicles according to claim 1, characterized in that, The multi-objective optimization algorithm of the dynamic incentive generation module operates as follows: the algorithm optimizes two objective functions simultaneously. The first objective function is to maximize the overall environmental benefits of the platform, and the second objective function is to balance user participation. The algorithm inputs include the current user's environmental effort index, the user's centrality index in their social network, and the recent distribution of effort indices of all users on the platform. The multi-objective problem is transformed into a single-objective problem through linear weighting, where the weight coefficients are dynamically adjusted according to the platform's operation strategy. The final output is a dynamic incentive factor for each user.
5. The environmental benefit quantification feedback system for leased new energy vehicles according to claim 4, characterized in that, The dynamic incentive generation module is also connected to an incentive points database; this module calculates the incentive points that the user should receive based on the environmental protection effort index and dynamic incentive factors, according to a preset points conversion rule. The points conversion rule is defined as the environmental protection effort index multiplied by a dynamic incentive factor and then multiplied by a basic points coefficient, where the basic points coefficient is set by the platform operator according to the activity cycle.
6. The environmental benefit quantification feedback system for leased new energy vehicles according to claim 1, characterized in that, The visualized environmental protection report generated by the cross-platform interactive module consists of three core parts: Part 1 uses a pie chart to show the absolute carbon emission reduction of the user's car rental and its equivalent environmental contribution to the number of trees planted. Part 2 uses radar charts to display users' ratings across multiple dimensions, including their environmental efforts, rental frequency, and the greenness of their vehicle selection. Part 3 shows the trend of a user's environmental ranking in their social circle and the details of the incentive points they have earned.
7. The environmental benefit quantification feedback system for leased new energy vehicles according to claim 1, characterized in that, The cross-platform interaction module also includes a social challenge engine. Based on historical data from the user behavior analysis module, the engine regularly generates personalized environmental challenge tasks, including continuous car rental challenges and high-effort maintenance challenges. The progress and completion status of the challenges will be updated in real time to a visual environmental report and synchronized to social platforms through the application programming interface.
8. The environmental benefit quantification feedback system for leased new energy vehicles according to claim 1, characterized in that, The overall system architecture adopts a microservice design. The environmental benefit quantification module, user behavior analysis module, dynamic incentive generation module and cross-platform interaction module are deployed as independent microservices and exchange data through the representative state transmission application programming interface. All data transmission between modules is encrypted, and key data such as user environmental effort index and incentive points are stored on a distributed ledger to ensure immutability.
9. A quantitative feedback system for the environmental benefits of leasing new energy vehicles according to claim 2, characterized in that, The benchmark carbon emission model pre-stores the average fuel consumption data and carbon emission coefficient of mainstream fuel vehicles under the same driving mileage.
10. A quantitative feedback system for the environmental benefits of leasing new energy vehicles according to claim 3, characterized in that, The theoretical maximum driving distance is calculated by dividing the battery capacity by the energy consumption per 100 kilometers and then multiplying by 100. The calculation process for the theoretical maximum emission reduction potential refers to the benchmark carbon emission calculation method of the environmental benefit quantification module, replacing the actual driving mileage with the theoretical maximum driving distance.