Full-life-cycle carbon neutralization system and method for intelligent network-connected electric vehicle

Abstract

The invention relates to the technical field of carbon neutralization, and discloses a full-life-cycle carbon neutralization system and method for an intelligent networked electric vehicle. The system comprises a full-life-cycle carbon emission system which is used for calculating carbon emission of a vehicle in five stages of material supply, part and whole vehicle manufacturing, charging use, scrap recovery and intelligent network connection cloud service full-life cycle; the ecological system engineering BECNU negative carbon emission system is used for providing globally acknowledged carbon removal certification carbon sink credit (CRCF); the intelligent network connection system is used for deducting and neutralizing carbon emission in the boundary of the system; and the vehicle-mounted carbon neutralization intelligent instrument is used for carrying out digital intelligent network connection recording, metering accounting, authentication and carbon label neutralization identification on carbon footprint emission and removal information.

Description

Technical Field

[0001] The present application relates to the field of carbon neutrality technology, and in particular to a full life cycle carbon neutrality system and method for intelligent connected electric vehicles. Background Art

[0002] Traditional carbon emission calculation methods often only focus on the use phase, ignoring the carbon footprint of production, recycling and other links, resulting in incomplete carbon neutrality. At the same time, existing carbon neutrality solutions are mostly static management, which is difficult to adapt to the dynamic changes in the use of electric vehicles and lacks effective incentive mechanisms for car owners.

[0003] In addition, with the development of intelligent network technology, electric vehicles are no longer just a means of transportation, but have become mobile energy nodes and data centers. However, how to organically combine these new technologies with carbon neutrality goals and give full play to the potential of electric vehicles in smart grids and carbon trading markets remains an urgent problem to be solved. Existing carbon neutrality systems often treat electric vehicles as isolated individuals and fail to fully consider their role and value in the entire ecosystem. Summary of the invention

[0004] The present application provides a full life cycle carbon neutrality system and method for intelligent connected electric vehicles, which are used to achieve full life cycle carbon neutrality monitoring and analysis of intelligent connected electric vehicles.

[0005] In a first aspect, the present application provides an intelligent networked electric carbon neutral system based on the BECNU negative carbon emission ecosystem project, characterized in that it includes:

[0006] The full life cycle carbon emission system is used to calculate the carbon emissions of vehicles in the five stages of their life cycle: material supply, parts and vehicle manufacturing, charging and use, scrapping and recycling, and intelligent network cloud services;

[0007] Ecosystem Engineering BECNU negative carbon emission system is used to provide globally recognized carbon removal certification carbon sink credits C RCF ;

[0008] Intelligent connected systems for carbon offsetting within system boundaries;

[0009] On-board carbon neutrality smart instrument, used for digital intelligent network recording, measurement and accounting, certification and carbon label neutrality identification of carbon footprint emission and removal information;

[0010] Among them, the carbon footprint quantity technical model satisfies the following calculation formula:

[0011] C t +C MA +C VM +C USE +CREC +C RCF =0, C t Carbon footprint of cloud network digital center, C MA Carbon footprint for material acquisition and supply, C VM is the carbon footprint of parts and vehicle manufacturing process, C USE The carbon footprint of the charging and energy storage and discharge process, C REC is the carbon footprint of the waste recycling process, C RCF Carbon sink credits that are internationally recognized for carbon removal certification.

[0012] In a second aspect, the present application provides a method for achieving carbon neutrality throughout the life cycle of an intelligent connected electric vehicle, the method comprising:

[0013] Distributed collection and preprocessing of multi-source data on raw material production, vehicle manufacturing, use process and scrapping and recycling to obtain a full life cycle carbon emission data set;

[0014] Conduct multi-level and multi-scale modeling and fusion analysis on the full life cycle carbon emission data set to obtain a dynamic prediction model for carbon emissions;

[0015] Through the carbon emission dynamic prediction model, spatiotemporal data mining and multi-objective optimization calculation are performed to obtain smart charging and vehicle-grid interaction strategy solutions;

[0016] Perform multi-sensor data fusion and degradation analysis on the smart charging and vehicle-grid interaction strategy solutions and vehicle status data to obtain predictive maintenance and life optimization solutions;

[0017] Perform multi-dimensional carbon footprint evaluation and dynamic trade-off calculation on the predictive maintenance and life optimization scheme to obtain a full life cycle carbon footprint optimization strategy;

[0018] Multi-scenario prediction and adaptive path planning are performed on the full life cycle carbon footprint optimization strategy to obtain a carbon-neutral path for the intelligent connected electric vehicle.

[0019] In the technical solution provided by this application, by analyzing each stage of the electric vehicle's life cycle, accurate calculation of carbon emissions from material acquisition to scrapping and recycling is achieved. In particular, the real-time monitoring and calculation functions of the on-board carbon neutrality smart instrument ensure the accuracy and timeliness of carbon emission data during the use phase. RCF Management, dynamically offsetting carbon emissions in the production stage and the use stage. This design makes the carbon neutralization process more flexible and accurate, and can adapt to different usage situations and changes in the carbon intensity of the power grid. Through deep integration with the smart grid, the system realizes functions such as V2G services and intelligent charging scheduling, which not only improves energy efficiency, but also creates additional carbon emission reduction benefits for car owners. RCFAs a built-in carbon asset of the vehicle, it provides a carbon asset investment platform, enabling car owners to participate in carbon market transactions and achieve a win-win situation of economic and environmental benefits. By incentivizing car owners to participate in carbon neutrality actions, the activity and sustainability of the entire ecosystem are improved. The use of blockchain technology to store and verify key carbon footprint data, combined with the CNAS certification system, ensures the transparency and credibility of the carbon neutrality process, and provides a reliable basis for carbon trading and policy making. Not only does it focus on single-vehicle carbon neutrality, but it also connects electric vehicles with a wider ecosystem project through the BECNU negative carbon emission project, achieving a wider range of carbon neutrality effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 A schematic diagram of an embodiment of a full life cycle carbon neutrality system for an intelligent networked electric vehicle in an embodiment of the present application;

[0022] Figure 2 This is a schematic diagram of an embodiment of a method for achieving carbon neutrality throughout the life cycle of an intelligent connected electric vehicle in an embodiment of the present application. DETAILED DESCRIPTION

[0023] Embodiments of the present application provide a full life cycle carbon neutrality system and method for an intelligent connected electric vehicle. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the intelligent networked electric carbon neutral system based on the BECNU negative carbon emission ecosystem project includes:

[0025] The full life cycle carbon emission system is used to calculate the carbon emissions of vehicles in the five stages of their life cycle: material supply, parts and vehicle manufacturing, charging and use, scrapping and recycling, and intelligent network cloud services;

[0026] Ecosystem Engineering BECNU negative carbon emission system is used to provide globally recognized carbon removal certification carbon sink credits C RCF ;

[0027] Intelligent connected systems for carbon offsetting within system boundaries;

[0028] On-board carbon neutrality smart instrument, used for digital intelligent network recording, measurement and accounting, certification and carbon label neutrality identification of carbon footprint emission and removal information;

[0029] Among them, the carbon footprint quantity technical model satisfies the following calculation formula:

[0030] C t +C MA +C VM +C USE +C REC +C RCF =0, C t Carbon footprint of cloud network digital center, C MA Carbon footprint for material acquisition and supply, C VM is the carbon footprint of parts and vehicle manufacturing process, C USE The carbon footprint of the charging and energy storage and discharge process, C REC is the carbon footprint of the waste recycling process, C RCF Carbon sink credits that are internationally recognized for carbon removal certification.

[0031] Specifically, the intelligent connected electric carbon neutral system in this embodiment includes the following main parts: Full life cycle carbon emission system: The system adopts the carbon emission factor method to establish a carbon emission calculation model covering four stages: material acquisition, vehicle production, use and recycling and reuse. In the material acquisition stage, the system considers the carbon emissions of metal materials such as steel, aluminum, copper, and non-metallic materials such as plastics and rubber. The vehicle production stage covers the energy consumption of process links such as stamping, welding, painting, and assembly. The use stage mainly calculates the carbon emissions caused by electricity consumption and maintenance. The recycling and reuse stage considers the carbon emissions and carbon reductions of processes such as metal recycling and power battery recycling. Ecosystem Engineering BECNU Negative Carbon Emission System: The system provides internationally recognized carbon removal certified carbon sink credits (C RCF). These carbon sink credits come from a variety of ecosystem engineering projects such as afforestation and reforestation, biochar, soil carbon sequestration, and direct air carbon capture. The system ensures the authenticity, measurability, and verifiability of these carbon sink credits. Intelligent network system: The system collects vehicle usage data in real time through vehicle networking technology, including mileage, energy consumption and other information. At the same time, it connects to the power grid system to obtain real-time electricity carbon emission factors. The system is also connected to the carbon trading platform to achieve a dynamic balance between carbon emissions and carbon sinks. On-board carbon neutrality smart instrument: This is an intelligent terminal device installed on the vehicle. It monitors the vehicle's energy consumption data in real time through a sensor network, and calculates the vehicle's real-time carbon footprint in combination with the information provided by the intelligent network system. At the same time, it also records the vehicle's carbon emission data throughout its life cycle and generates a digital carbon label. Through the collaborative work of these systems, the accurate calculation and dynamic balance of the carbon footprint of electric vehicles throughout their life cycle is achieved. When the vehicle generates carbon emissions during use, the system will automatically call the corresponding carbon sink credits from the BECNU negative carbon emission system to offset it, ensuring that the carbon footprint calculation model always meets C t +C MA +C VM +C USE +C REC +C RCF =0 is a balanced equation.

[0032] Optionally, the full life cycle carbon emission system includes:

[0033] The material supply carbon emission subsystem is used to calculate the first unavoidable carbon emissions and its first carbon footprint in the material supply stage; the material supply carbon emission subsystem uses low-carbon emission, ultra-low carbon emission and carbon-neutral metal and non-metal materials, as well as recycled materials and renewable materials, and measures, verifies and certifies the first carbon footprint of the materials in accordance with international or domestic standards to obtain data corresponding to the first carbon footprint and its first carbon label;

[0034] The carbon emission subsystem for parts and vehicle manufacturing is used to calculate the second unavoidable carbon emissions and its second carbon footprint during the manufacturing stage; the carbon emission subsystem for parts and vehicle manufacturing adopts low-carbon emission, ultra-low carbon emission and carbon-neutral energy, low-carbon and intelligent manufacturing processes, and measures, accounts for, verifies and certifies the second carbon footprint of the manufacturing process in accordance with international or domestic standards, and obtains the data corresponding to the second carbon footprint and its second carbon label;

[0035] The charging and use carbon emission subsystem is used to calculate the third unavoidable carbon emissions and its third carbon footprint during the charging and use stages; the charging and use carbon emission subsystem adopts low-carbon emission, ultra-low carbon emission and carbon neutral energy power charging, and the energy power includes wind power, photovoltaic solar power, and uses new energy batteries to participate in smart grid and microgrid energy storage. The third carbon footprint of the charging and use process is measured, calculated, verified and certified according to international standards or domestic standards, and the data corresponding to the third carbon footprint and its third carbon label are obtained;

[0036] The scrap recycling carbon emission subsystem is used to calculate the fourth unavoidable carbon emissions and its fourth carbon footprint in the scrap recycling stage; the scrap recycling carbon emission subsystem adopts low-carbon emission, ultra-low carbon emission and carbon neutral energy and electricity, low-carbon emission, ultra-low carbon emission and carbon neutral logistics measures, and measures, calculates, verifies and certifies the fourth carbon footprint of the whole process of scrap recycling in accordance with international standards or domestic standards, and obtains the data corresponding to the fourth carbon footprint and its fourth carbon label;

[0037] The vehicle-computer industrial Internet, vehicle-road-cloud energy smart microgrid and digital center carbon emission subsystem are used to calculate the relevant fifth unavoidable carbon emissions and its fifth carbon footprint.

[0038] Specifically, the full life cycle carbon emission system consists of five subsystems, each of which is responsible for calculating the carbon emissions and carbon footprint of a specific stage. The material supply carbon emission subsystem uses advanced low-carbon material technology. For example, low-carbon aluminum produced by electrolysis can reduce the carbon emission factor of aluminum materials from 16.4kg (CO 2 ) / kg down to 2.5kg(CO 2) / kg. At the same time, the use of high-strength steel can reduce the weight of the vehicle body, thereby reducing the use of steel and its carbon emissions. The subsystem also gives priority to recycled materials, such as recycled plastics, which significantly reduces the carbon footprint of plastic materials. The system uses bill of materials management to accurately track the use and source of each material and calculates the first carbon footprint according to international standards. The carbon emission subsystem for parts and vehicle manufacturing focuses on optimizing production processes. For example, the use of water-based paint instead of traditional solvent-based paint in the paint shop and the use of heat pump technology to recover waste heat can reduce the carbon emissions of the painting process by 30%. The welding shop uses laser welding technology to improve energy efficiency. The general assembly workshop widely uses intelligent logistics systems and robots to reduce energy waste. The entire manufacturing process gives priority to the use of renewable energy electricity to further reduce the carbon footprint. The charging carbon emission subsystem achieves low-carbon operation through intelligent charging management. The system is connected to the smart grid and gives priority to charging during periods with a high proportion of renewable energy generation. At the same time, the on-board battery is used to participate in the peak load regulation of the power grid, provide V2G services, and create carbon emission reduction benefits. The system also dynamically calculates the carbon footprint of the charging process based on the real-time carbon emission factor of the power grid. The scrap recycling carbon emission subsystem adopts advanced recycling technology. For car body metal, intelligent sorting technology is used to improve the recycling purity and reduce the energy consumption of re-smelting. The power battery adopts fully automatic disassembly line and water cooling crushing technology to greatly improve the recycling efficiency and material recovery rate. The system also optimizes the logistics network and uses new energy logistics vehicles to reduce carbon emissions in the recycling process. The industrial Internet of vehicles and the intelligent microgrid and digital center carbon emission subsystem use edge computing technology to reduce data transmission volume and data center energy consumption. At the same time, efficient cooling technology and AI optimization algorithms are used to improve the PUE value of the data center and reduce operational carbon emissions. Through a unified data interface, each subsystem transmits carbon footprint data to the on-board carbon neutrality smart instrument in real time to achieve accurate calculation and dynamic management of carbon emissions throughout the life cycle.

[0039] Optionally, the vehicle-machine industrial Internet and vehicle-road-cloud-energy smart microgrid and digital center carbon emission subsystem include the unavoidable carbon emissions of the vehicle-machine industrial Internet, the unavoidable carbon emissions of the smart microgrid charging and use, and the carbon fingerprint environmental benefit emission reduction of the battery participating in energy storage and discharge, and carry out measurement, accounting, verification and certification of the fifth carbon footprint of the industrial Internet, smart microgrid and digital center in accordance with international standards or domestic standards, and obtain the data corresponding to the fifth carbon footprint and its fifth carbon label; the on-board carbon neutrality smart instrument is connected to the vehicle-machine system and the smart microgrid cloud for communication and data transmission, and realizes intelligent, digital and transparent carbon footprint identification with the help of the vehicle-road-cloud-energy smart network cloud system and the digital smart cloud supervision and inspection and testing certification and accreditation CNAS system; the factory certificate of the on-board carbon neutrality smart instrument and the initial data table of the carbon neutrality label of the instrument clearly indicate C t , C MA , C VM , CUSE , C REC The carbon footprint value of the vehicle manufacturer or operator and the C RCF Carbon Removal Certified Carbon Sink Credits and their Ecosystem Engineering BECNU Negative Carbon Emissions neutralize the vehicle’s unavoidable carbon emissions.

[0040] Specifically, the industrial Internet of vehicles and the intelligent microgrid and digital center carbon emission subsystem of the vehicle-road cloud energy adopt a comprehensive carbon emission management strategy. This subsystem delegates some data processing tasks to the vehicle-mounted unit through efficient edge computing technology, effectively reducing the computing load and energy consumption of the data center. The data center uses advanced liquid cooling technology and intelligent scheduling algorithms to optimize the PUE value to below 1.1, significantly reducing operational carbon emissions. The intelligent microgrid charging system is closely integrated with regional renewable energy power generation facilities, giving priority to the use of clean energy such as photovoltaics and wind power. The system predicts the grid load and renewable energy generation through intelligent algorithms, reasonably arranges charging periods, and maximizes the proportion of clean energy use. At the same time, the on-board battery participates in the peak load regulation of the grid as a mobile energy storage unit and provides V2G services. The system accurately records each charging and discharging behavior, calculates the carbon emission reduction, and forms the owner's personal carbon account. The on-board carbon neutrality smart meter is the core equipment of the entire system. It uses high-precision sensors to monitor vehicle energy consumption data in real time, and combines GPS positioning information and driving behavior analysis to accurately calculate the carbon emissions during driving. The smart meter is connected to the cloud platform in real time through the 5G network to upload carbon emission data and receive the latest information such as the grid carbon emission factor to ensure the accuracy of carbon footprint calculation. The smart meter is also connected to the blockchain system to store key carbon footprint data on the chain to ensure the data is tamper-proof and traceable. This provides a reliable data basis for subsequent carbon footprint certification and carbon trading. The system automatically performs carbon offset operations through smart contracts to ensure that the vehicle is always in a carbon neutral state. When the vehicle leaves the factory, the initial data table of the on-board carbon neutral smart meter records the carbon footprint in detail. t , C MA , C VM , C USE , C REC The carbon footprint values ​​of each stage are also indicated. RCF Carbon sink credits, these carbon sinks come from strictly certified BECNU ecosystem engineering projects, such as forest carbon sinks, ocean carbon sinks, etc. The entire system realizes unified data management and analysis through the intelligent network cloud platform of vehicle, road, cloud and energy. The cloud platform is connected to the CNAS testing and certification system, regularly conducts third-party verification of carbon footprint calculation results, and generates authoritative carbon neutrality certificates.

[0041] Optionally, the C RCF Carbon removal certification carbon sink credits include the following two properties and uses:

[0042] Used to neutralize C t +C MA +C VM C RCF Carbon removal certification carbon sink credits are liquidated and cancelled according to market rules when the vehicle leaves the factory;

[0043] Used to neutralize the third inevitable carbon emissions during charging USE C RCF Carbon removal certified carbon sink credits, as regulated carbon assets attached to the carbon-neutral vehicle itself, have their value included in the sales price of the carbon-neutral vehicle, and have built-in presets for offsetting the inevitable carbon emissions during the charging and use of the carbon-neutral vehicle.

[0044] Specifically, C in this embodiment RCF Carbon removal certification and carbon sink credits adopt a dual-track management strategy to achieve precise carbon neutrality throughout the life cycle of electric vehicles. RCF To offset C t (Cloud Network Digital Center Carbon Footprint), C MA (material acquisition and supply carbon footprint) and C VM (Carbon footprint of parts and vehicle manufacturing process). This part of carbon emissions mainly occurs in the vehicle production stage and is fixed and predictable. The system uses the life cycle assessment (LCA) method to accurately calculate the carbon emissions of each vehicle during the production process. For example, for a mid-sized electric SUV, its C MA At 7000-8000kg CO 2 Equivalent to about C VM About 500-600kg CO 2 Equivalent, and C t The amount of carbon dioxide will fluctuate according to the specific use of cloud services. Based on the calculation results, the car company purchases an equal amount of carbon dioxide from the certified carbon sink project. RCF These carbon sinks come from a variety of sources, including afforestation projects, wetland restoration, and soil carbon sequestration. RCF Each part has a unique identification code, which records its source, quantity and validity period. RCF Matching with the vehicle carbon footprint and recording the transaction on the blockchain completes the liquidation and cancellation. This ensures that the carbon emissions in the production phase are fully offset. RCF For C USE (Charging, energy storage and discharge process carbon footprint), which belongs to the dynamic carbon emissions of the vehicle use stage. The system estimates the power consumption and corresponding carbon emissions of the vehicle's entire life cycle based on the vehicle model characteristics, battery capacity and expected service life. For example, assuming that an electric car travels 15,000 kilometers per year, has a service life of 10 years, and consumes 15kWh of electricity per 100 kilometers, its C USE About 13500kg CO2 Equivalent (taking into account the gradual cleanliness of the future power grid). Car companies purchase an equivalent amount of the second type C RCF , and include its value in the vehicle's selling price as the vehicle's "carbon asset". RCF The carbon emission factor of the vehicle is stored in digital form in the on-board carbon neutrality smart meter. When the vehicle is charging, the smart meter accurately calculates the carbon emission of this charging according to the actual power consumption and the real-time carbon emission factor of the local power grid, and calculates the carbon emission of the vehicle from the preset carbon emission factor. RCF The system also designs a carbon asset trading mechanism. If the owner's actual carbon emissions are lower than the estimated value, the remaining C RCF It can be traded on the platform or used for other carbon neutral projects. This mechanism not only ensures carbon neutrality throughout the use of the vehicle, but also provides car owners with the opportunity to participate in the carbon market and encourage low-carbon travel behavior.

[0045] Optionally, it also includes the BECNU negative carbon emission neutralization incentive mechanism for carbon neutral car owners. The mechanism connects the carbon emission reduction market with the carbon removal market and uses the car owner's digital identity ID in the cloud network digital center system to build a carbon neutral car owner's carbon emission neutralization incentive mechanism based on the carbon emission neutralization incentive mechanism built into the car when it leaves the factory. RCF Internationally recognized carbon removal certification carbon sink credit assets, management, to achieve ecosystem engineering BECNU negative carbon emission neutrality and ecological value incentives

[0046] Specifically, each car owner is assigned a unique digital ID, which is bound to the car's carbon neutrality smart meter. This ID serves as the car owner's identity in the entire ecosystem and is used to record and manage all of its carbon neutrality-related activities. The system has established a comprehensive carbon asset management platform. Car owners can view their carbon asset status in real time through mobile applications, including the carbon asset status preset when the vehicle leaves the factory. RCF Carbon sink quota, as well as carbon emissions and carbon sink consumption during use. In terms of microgrid energy storage and emission reduction, the system encourages car owners to participate in V2G (vehicle to grid) services. When the vehicle is connected to a smart charging pile, the on-board battery can be discharged according to the needs of the grid to provide peak and frequency regulation services for the grid. The system accurately records the power and time of each V2G service, calculates the corresponding carbon emission reduction, and converts it into additional carbon points to reward the car owner.

[0047] The above describes the intelligent connected electric carbon neutral system based on the BECNU negative carbon emission ecosystem project in the embodiment of the present application. The following describes the full life cycle carbon neutral method of the intelligent connected electric vehicle in the embodiment of the present application. Please refer to Figure 2 In the embodiments of the present application, one embodiment of the carbon neutrality method for the entire life cycle of an intelligent networked electric vehicle includes:

[0048] Step S101, performing distributed collection and preprocessing of multi-source data on raw material production, vehicle manufacturing, use process and scrapping and recycling to obtain a full life cycle carbon emission data set;

[0049] It is understandable that the execution subject of this application can be the carbon neutral system of the entire life cycle of the intelligent networked electric vehicle, or it can be a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0050] Specifically, the energy consumption and emission data generated in the raw material production process are collected in real time. Through various sensors and monitoring systems installed in production equipment and factories, the energy consumption and emission data in each production link are recorded. Through the analysis of material flow relationships, a supply chain network model is constructed to help the system understand the flow of each raw material in the supply chain and the specific structure of its carbon emissions. For the time series collection and abnormality identification of production line data in the vehicle manufacturing process, data collection equipment and monitoring systems are installed at each key node of the production line to record the operation status of the production line and the energy consumption and emission data of each manufacturing link in real time. Through time series analysis and abnormality identification of these data, the manufacturing stage carbon emission time series is generated to help understand the carbon emission rules and abnormalities in the manufacturing process. Multi-source fusion of driving data, charging data and environmental data during vehicle use is performed. Through on-board sensors, charging station data collection systems and environmental monitoring equipment, the energy consumption, emissions, charging conditions of the vehicle during driving and the impact of the external environment on the vehicle operation are recorded. Through cleaning and standardization of these data, carbon emission characteristic data in the use stage are generated. These data can help understand the carbon emission characteristics of the vehicle under different use conditions. The disassembly data and material reuse data in the scrapping and recycling process are classified and sorted, and the carbon emissions of each scrapped vehicle during the disassembly and material reuse process are recorded. By classifying and sorting these data, the carbon emission classification results of the scrapping and recycling stage are generated. By aligning and synchronizing the data structure of raw material carbon emissions, the carbon emission time series of the manufacturing stage, the carbon emission characteristic data of the use stage, and the carbon emission classification results of the scrapping and recycling stage, the spatiotemporal correlation data of carbon emissions in the whole life cycle are generated. After these data are processed by distributed storage and secure encryption technology, a complete carbon emission data set for the whole life cycle is formed.

[0051] Step S102: Perform multi-level and multi-scale modeling and fusion analysis on the full life cycle carbon emission data set to obtain a carbon emission dynamic prediction model;

[0052] Specifically, the full life cycle carbon emission data set is decomposed in time scales. By dividing the data set into short-term, medium-term and long-term carbon emission data subsets, the changing patterns of carbon emissions at different time scales are understood. By establishing a multi-time scale carbon emission data structure, the carbon emissions at different stages are analyzed. Through segmented data analysis, the rapid changes in the short term, the medium-term trends and the long-term cumulative effects are identified. Based on the multi-time scale carbon emission data structure, the spatial correlation analysis of the carbon emission data at each life cycle stage is carried out, and the spatial distribution characteristics of each life cycle stage are comprehensively considered, such as the source of raw materials, the geographical location of the manufacturing plant, the use area and the location of scrapping and recycling. By constructing a multi-spatial scale carbon emission correlation model, the correlation relationship of carbon emissions at different spatial scales is revealed, and the spatial distribution characteristics of carbon emissions are more comprehensively understood. The multi-time scale carbon emission data structure and the multi-spatial scale carbon emission correlation model are cross-validated and feature extracted to extract the multi-dimensional correlation characteristics of carbon emissions. These features contain information in both time and space dimensions, and can more accurately reflect the true situation of carbon emissions. According to the multi-dimensional correlation characteristics of carbon emissions, corresponding sub-models are established for each life cycle stage of raw material production, vehicle manufacturing, use process and scrapping and recycling, and these sub-models are coupled and integrated to form an initial prediction model for carbon emissions throughout the life cycle. The initial prediction model for carbon emissions throughout the life cycle is backtested and error analyzed with historical data. By comparing historical data, the error distribution characteristics between the model prediction and the actual situation are identified. According to the error distribution characteristics, a model correction mechanism is designed to self-correct the initial prediction model to form a more accurate self-correcting carbon emission prediction model. The dynamic parameter update mechanism and real-time data access interface of the self-correcting carbon emission prediction model are optimized to enable the model to be dynamically adjusted according to real-time data, ensuring that the carbon emission prediction model can always reflect the latest data changes, and finally obtaining a dynamic prediction model for carbon emissions.

[0053] Step S103, performing spatiotemporal data mining and multi-objective optimization calculations through a carbon emission dynamic prediction model to obtain a smart charging and vehicle-grid interaction strategy solution;

[0054] Specifically, the output data of the carbon emission dynamic prediction model is subjected to spatiotemporal clustering analysis to identify charging demand hotspots in different geographical locations and time periods, and obtain the spatiotemporal distribution map of charging demand. According to the spatiotemporal distribution map of charging demand, the grid load data and renewable energy generation forecast data are correlated and analyzed to reflect the load situation of the grid and the supply situation of renewable energy at different times and locations, and generate dynamic grid state assessment results. The dynamic grid state assessment results and vehicle usage pattern data are cross-analyzed in multiple dimensions to predict the best time window for vehicle-grid interaction. The interactive time window prediction helps the system understand in what time period and at what location it is most efficient for vehicles to charge or feed back electricity to the grid. According to the time window prediction results, the distribution and capacity of charging stations are optimized to obtain a smart charging infrastructure layout plan to ensure that charging stations have sufficient charging capacity in high-demand areas and time periods to meet the needs of electric vehicle users. Multi-objective optimization calculations are performed on the smart charging infrastructure layout plan to determine the best charging scheduling strategy. The initial charging scheduling strategy is the result of optimization based on multiple factors such as charging demand, dynamic grid status, and renewable energy supply. Dynamic pricing and incentive mechanisms are adjusted for the initial charging scheduling strategy. This adjustment can guide electric vehicle users to charge when the grid load is low or the renewable energy supply is sufficient through dynamic electricity prices and corresponding incentives, thereby reducing the pressure on the grid and improving the utilization rate of renewable energy. Finally, a smart charging and vehicle-grid interaction strategy is obtained.

[0055] Step S104: performing multi-sensor data fusion and degradation analysis on the smart charging and vehicle-grid interaction strategy solutions and vehicle status data to obtain predictive maintenance and life optimization solutions;

[0056] Specifically, the smart charging and vehicle-grid interaction strategy schemes are decomposed to extract the mode characteristics of the vehicle in different usage scenarios. The feature vector describes the behavior pattern of the vehicle during charging, discharging and driving. Through the mode feature vector, the status data of the key components of the vehicle are fused from multiple sources, including sensor data, on-board diagnostic system data and environmental monitoring data. By fusing multiple sources of information, a comprehensive vehicle health status indicator is obtained, which reflects the current health status of each key component of the vehicle. The vehicle health status indicators are analyzed in time series, and the component degradation trend model is established by analyzing the change trend of these indicators over time. These models can capture the degradation law of key components and generate the remaining life prediction results of key components through the degradation trend model. These prediction results can help the system identify in advance which components will fail in the future. According to the remaining life prediction results of key components, the maintenance needs are prioritized, and a multi-level maintenance decision tree is constructed. According to the remaining life of the components, the availability of maintenance resources and other related factors, an optimized maintenance scheduling plan is formulated to ensure the maximum operation reliability and safety of the vehicle under limited maintenance resources. The cost-benefit analysis of the predictive maintenance scheduling plan is carried out. By analyzing the cost and expected benefits of each maintenance operation, an initial life optimization strategy is determined. This strategy not only considers the direct cost of maintenance, but also the indirect benefits of extending component life and avoiding failures. Real-time vehicle status and environmental factors are integrated with the strategy to dynamically adjust the maintenance plan to cope with real-time changing vehicle status and environmental conditions.

[0057] Step S105: Perform multi-dimensional carbon footprint evaluation and dynamic trade-off calculation on the predictive maintenance and life optimization solutions to obtain a full life cycle carbon footprint optimization strategy;

[0058] Specifically, by calculating the characteristics of predictive maintenance and life optimization solutions, maintenance activity characteristics and life extension indicators are obtained. These characteristics and indicators can specifically reflect the carbon emissions and life extension benefits of various maintenance activities. The maintenance activity characteristics and life extension indicators are integrated and analyzed with the data of raw material production and manufacturing processes to form a comprehensive basic data set for full life cycle carbon footprint assessment. The basic data set covers carbon emission data from raw material production, vehicle manufacturing, use to scrapping and recycling. According to the basic data set for full life cycle carbon footprint assessment, the carbon emissions of each life cycle stage are quantitatively calculated and a multi-dimensional carbon footprint assessment model is constructed. Through this model, the carbon emissions of each stage are calculated to generate the initial carbon footprint assessment results. The key influencing factors of the initial carbon footprint assessment results are analyzed, and a carbon footprint sensitivity model is established. Through this model, the factors that have the greatest impact on carbon emissions are identified, and the carbon footprint influencing factor weight matrix is ​​constructed through the carbon footprint sensitivity model to clarify the importance of each factor in the carbon footprint. According to the carbon footprint influencing factor weight matrix, the predictive maintenance and life optimization solutions are dynamically adjusted to generate an alternative set of optimization solutions. The alternative scheme comprehensively considers the impact of different maintenance activities and life extension strategies on carbon emissions, and selects a more environmentally friendly scheme through the guidance of the weight matrix. Dynamic response analysis is performed on the optimization scheme alternative set to form an adaptive carbon footprint optimization framework. The framework can be adjusted according to real-time data and environmental changes to ensure that the optimization scheme can effectively reduce the carbon footprint under various conditions. Feedback adjustment optimization is performed on the adaptive carbon footprint optimization framework. Through continuous feedback and adjustment, the carbon footprint strategy is continuously optimized to obtain a full life cycle carbon footprint optimization strategy. This strategy can minimize carbon emissions while ensuring vehicle performance and life.

[0059] Step S106: Perform multi-scenario prediction and adaptive path planning on the full life cycle carbon footprint optimization strategy to obtain a carbon-neutral path for the intelligent connected electric vehicle.

[0060] Specifically, the full life cycle carbon footprint optimization strategy is analyzed to extract the key decision variables and constraints required to achieve carbon neutrality. These variables and conditions include not only internal parameters, such as the charging frequency, service life, and maintenance cycle of the electric vehicle, but also external environmental factors, such as weather conditions, energy supply, and traffic flow. After integrating the decision variables and constraints with the external environmental factors, a basic parameter set for multi-scenario prediction is generated. Scenarios are constructed based on the basic parameter set for multi-scenario prediction to obtain a scenario probability distribution matrix that reflects the probability of occurrence of different scenarios, including multiple scenario combinations from the best to the worst. For each scenario, a carbon emission trajectory simulation is performed, and the initial carbon neutrality path network of each decision node is calculated using a dynamic programming model. The dynamic programming model can identify potential paths to achieve carbon neutrality based on the carbon emission trajectory under different scenarios and form a preliminary path network. A path sensitivity analysis is performed on the preliminary path network to identify key factors and potential risk points that affect path selection. These factors include policy changes, market fluctuations, and technological advances. Based on the influencing factors and risk points, a path evaluation indicator system is established to evaluate the feasibility and risk of each path. A path feasibility scoring table is generated based on the path evaluation index system. Through multi-criteria decision analysis, different paths are comprehensively evaluated to obtain a dynamically adjusted carbon neutral path plan. In order to ensure the effectiveness and feasibility of the carbon neutral path plan, a feedback mechanism and checkpoint configuration are performed on the dynamically adjusted carbon neutral path plan. The checkpoint can monitor the execution of the path in real time and can also make dynamic adjustments based on actual conditions to ensure that the path plan is always in the optimal state. Finally, the carbon neutral path of the intelligent connected electric vehicle is obtained.

[0061] In the embodiments of the present application, all stages of the entire life cycle of intelligent connected electric vehicles are covered, including raw material production, vehicle manufacturing, use process and scrap recycling, realizing comprehensive management and optimization of carbon emissions. By establishing a dynamic prediction model for carbon emissions, it is possible to reflect the changes in carbon emissions at each stage in real time, and to make dynamic adjustments and optimizations, thereby improving the adaptability and effectiveness of the carbon neutrality strategy. By utilizing multi-source data collection, multi-level modeling and multi-dimensional analysis, the accuracy of carbon emission assessment is greatly improved, and multiple aspects such as intelligent charging, vehicle-grid interaction, and predictive maintenance are organically combined to realize the coordinated optimization of energy systems, transportation systems, and maintenance systems. Through multi-scenario prediction and adaptive path planning, it is possible to cope with various situations that arise, thereby realizing carbon neutrality monitoring and analysis of the entire life cycle of intelligent connected electric vehicles.

[0062] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0063] (1) Collect energy consumption and emission data in the raw material production process in real time, and build a supply chain network model based on the material flow relationship to obtain the raw material carbon emission data structure;

[0064] (2) Time series data collection and anomaly identification of the production line data of the vehicle manufacturing process are performed to obtain the carbon emission time series of the manufacturing stage;

[0065] (3) Multi-source fusion of driving data, charging data, and environmental data during vehicle use, and cleaning and standardization processing to obtain carbon emission characteristic data during the use phase;

[0066] (4) Classify and sort the disassembly data and material reuse data in the scrap recycling process to obtain the carbon emission classification results of the scrap recycling stage;

[0067] (5) Data alignment and time synchronization are performed on the raw material carbon emission data structure, the manufacturing stage carbon emission time series, the use stage carbon emission characteristic data, and the carbon emission classification results of the scrapping and recycling stage to obtain the spatiotemporal correlation data of carbon emissions throughout the life cycle;

[0068] (6) Distributed storage and secure encryption of the spatiotemporal correlation data of carbon emissions throughout the life cycle are performed to obtain a full life cycle carbon emissions dataset.

[0069] Specifically, the energy consumption and emission data in the raw material production process are collected in real time, and a supply chain network model is constructed based on the material flow relationship to obtain the raw material carbon emission data structure. In this process, energy consumption is monitored in real time and emission data is recorded through sensors installed on raw material production equipment and transportation vehicles. These sensors include power metering devices, fuel usage monitoring equipment, and emission measurement equipment. The energy consumed and pollutants emitted by each batch of raw materials from mining to processing to transportation are recorded. For example, suppose E rm Represents the total energy consumption in the production process of raw materials, P r m represents the total emissions, and the formula is as follows:

[0070]

[0071] Among them, e i represents the energy consumption of the ith production link, p iRepresents the emission of the i-th production link, and n represents the total number of production links. By analyzing these data, a supply chain network model is constructed based on the material flow relationship. The supply chain network model can be represented by a directed graph, in which the nodes represent different links such as production, processing, and transportation, and the edges represent the flow direction and quantity of materials, forming a raw material carbon emission data structure that reflects the energy consumption and emissions of each link. The production line data of the vehicle manufacturing process is collected in time series and anomalies are identified to obtain the carbon emission time series of the manufacturing stage. During the vehicle manufacturing process, data acquisition equipment is installed at each workstation on each production line. These devices can record the operating status of the production line in real time, including the energy consumption, production efficiency, and emissions of each workstation. By analyzing the time series data, abnormal situations that occur in the production process are identified, such as a sudden increase in energy consumption or excessive emissions. By identifying and handling abnormal situations, the operating efficiency of the production line can be improved and unnecessary energy consumption and emissions can be reduced. Assume E mfg (t) represents the energy consumption at a certain moment in the manufacturing process, P mfg (t) represents the corresponding emission, and the formula is as follows:

[0072]

[0073] Among them, e j (t) represents the energy consumption of the jth station at time t, p j(t) represents the emission of the jth station at time t, and m represents the total number of stations. The driving data, charging data and environmental data during the use of the vehicle are multi-source fused, cleaned and standardized to obtain the carbon emission characteristic data of the use stage. During the operation of the vehicle, the vehicle's driving speed, mileage, charging status and environmental data are recorded in real time through the on-board sensors and vehicle management system. These data are transmitted to the data center through the wireless network, cleaned and standardized to remove noise and outliers. For example, the driving data of the vehicle may include speed, acceleration and braking frequency, the charging data may include charging time, charging power and battery status, and the environmental data may include temperature, humidity and air quality. By multi-source fusion of these data, the carbon emission characteristic data set of the use stage is obtained. The disassembly data and material reuse data in the scrapping and recycling process are classified and sorted to obtain the carbon emission classification results of the scrapping and recycling stage. In the process of scrapping and recycling of vehicles, the on-site data of the disassembly plant and the material reuse plant are collected through sensors and monitoring equipment to record the energy consumption and emissions of each disassembly and reuse link. By classifying and arranging these data, the classification results of carbon emissions in the scrapping and recycling stage are obtained. For example, the carbon emissions generated during the disassembly process can include emissions from fuel consumption, equipment operation and waste treatment, while the carbon emissions generated during the reuse process include emissions from material processing and remanufacturing. The raw material carbon emission data structure, the carbon emission time series of the manufacturing stage, the carbon emission characteristic data of the use stage and the carbon emission classification results of the scrapping and recycling stage are aligned and time synchronized to obtain the spatiotemporal correlation data of carbon emissions in the whole life cycle. In this process, the data from different data sources are aligned and time synchronized to ensure the consistency of the data in time and space. For example, the data is marked with a unified timestamp and geographic coordinates, and interpolation and alignment are performed according to the actual situation of the data. In this way, a spatiotemporal correlation data set of carbon emissions in the whole life cycle is obtained, covering the carbon emissions in each stage from raw material production, vehicle manufacturing, use to scrapping and recycling. In order to ensure the security and reliability of the data, the spatiotemporal correlation data of carbon emissions in the whole life cycle are distributedly stored and securely encrypted to obtain a full life cycle carbon emission data set. Distributed storage can improve the storage efficiency and reliability of data. By storing data copies on multiple nodes, it ensures that data will not be lost due to single point failures. Secure encryption can protect the privacy and security of data. It encrypts data through encryption algorithms to prevent data from being stolen or tampered with during transmission and storage.

[0074] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0075] (1) Decompose the full life cycle carbon emission data set into time scales and divide it into short-term, medium-term and long-term carbon emission data subsets to obtain a multi-time scale carbon emission data structure;

[0076] (2) Based on the multi-time scale carbon emission data structure, the spatial correlation analysis of carbon emission data at each life cycle stage is conducted to construct a multi-spatial scale carbon emission correlation model;

[0077] (3) Cross-validate and extract features of the multi-time scale carbon emission data structure and the multi-spatial scale carbon emission correlation model to obtain the multi-dimensional correlation features of carbon emissions;

[0078] (4) Based on the multidimensional correlation characteristics of carbon emissions, corresponding sub-models are established for each life cycle stage of raw material production, vehicle manufacturing, use process and scrapping and recycling, and the sub-models are coupled and integrated to obtain an initial prediction model for carbon emissions over the entire life cycle;

[0079] (5) Perform historical data backtesting and error analysis on the initial prediction model of carbon emissions over the entire life cycle to obtain the error distribution characteristics, and design a model correction mechanism based on the error distribution characteristics to obtain a self-correcting carbon emissions prediction model;

[0080] (6) The dynamic parameter update mechanism and real-time data access interface of the self-correcting carbon emission prediction model are optimized to obtain a dynamic carbon emission prediction model.

[0081] Specifically, the time scale of the full life cycle carbon emission data set is decomposed, and the data is divided into different time periods to better understand the changing laws and trends of carbon emissions. According to the multi-time scale carbon emission data structure, the carbon emission data of each life cycle stage is spatially correlated and a multi-spatial scale carbon emission correlation model is constructed. The carbon emission data is geospatial analyzed to determine the carbon emission correlation between different geographical regions and different life cycle stages. For example, through the Geographic Information System (GIS) tool, the carbon emission data of raw material production sites, vehicle manufacturing plants, vehicle use areas and scrap recycling sites are analyzed to find the spatial correlation between different regions and stages. The construction of a multi-spatial scale carbon emission correlation model can help identify high-emission areas and links, so as to carry out targeted carbon emission reduction measures. The multi-time scale carbon emission data structure and the multi-spatial scale carbon emission correlation model are cross-validated and feature extracted to obtain the multi-dimensional correlation features of carbon emissions. Cross-validation is to verify the accuracy and reliability of the model. By dividing the data set into a training set and a test set, the model is trained and tested to ensure that the model can accurately predict carbon emissions. Feature extraction is to extract the variables that best represent the characteristics of carbon emissions from the data, such as energy consumption, emissions, production efficiency, etc. In this way, a multidimensional correlation feature of carbon emissions with time and space dimensions is obtained. According to the multidimensional correlation feature of carbon emissions, corresponding sub-models are established for each life cycle stage of raw material production, vehicle manufacturing, use process and scrapping and recycling, and the sub-models are coupled and integrated to obtain the initial prediction model of carbon emissions for the whole life cycle. For example, the sub-model of the raw material production stage can be modeled based on energy consumption and emission data using linear regression or machine learning algorithms; the sub-model of the vehicle manufacturing stage can be modeled based on the energy consumption and emission data of the production line using time series analysis and anomaly detection algorithms; the sub-model of the vehicle use stage can be modeled based on driving data, charging data and environmental data using multi-source data fusion and standardized processing technology; the sub-model of the scrapping and recycling stage can be modeled based on disassembly data and material reuse data using classification and clustering algorithms. By coupling and integrating these sub-models, a comprehensive initial prediction model of carbon emissions for the whole life cycle is formed. The initial prediction model of carbon emissions for the whole life cycle is backtested with historical data and error analysis is performed to obtain the error distribution characteristics. Historical data backtesting is to test the prediction ability of the model by applying it to historical data; error analysis is to obtain the error distribution characteristics by calculating the difference between the predicted value and the actual value. i is the actual carbon emission value, is the model prediction value, n is the total number of data points, then the error e i It can be expressed as:

[0082]

[0083] The total error can be expressed as the mean square error (MSE):

[0084]

[0085] A model correction mechanism is designed according to the error distribution characteristics to obtain a self-correcting carbon emission prediction model. The self-correcting mechanism can correct the prediction error by updating the model parameters in real time. For example, the model is dynamically adjusted using Kalman filtering or recursive least squares algorithm to reduce the prediction error and improve the accuracy of the model. The dynamic parameter update mechanism and real-time data access interface optimization of the self-correcting carbon emission prediction model are performed to obtain a dynamic carbon emission prediction model. The dynamic parameter update mechanism refers to the continuous monitoring and updating of model parameters during the operation of the model to cope with changes in data and environment. The real-time data access interface optimization refers to ensuring that data can be transmitted to the model in a timely and accurate manner for processing by optimizing the data collection and transmission interface. For example, the Internet of Things (IoT) technology is used to collect vehicle operation data and environmental data in real time, and transmit them to the cloud through a wireless network for processing and analysis. In this way, a real-time and dynamic carbon emission prediction model is obtained.

[0086] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0087] (1) Perform spatiotemporal clustering analysis on the output data of the carbon emission dynamic prediction model to obtain spatiotemporal clustering results, and divide the charging demand hotspots of the spatiotemporal clustering results according to geographical location and time period to obtain a spatiotemporal distribution map of charging demand;

[0088] (2) Based on the spatiotemporal distribution diagram of charging demand, correlation analysis is performed on the grid load data and renewable energy generation forecast data to generate dynamic grid status assessment results;

[0089] (3) Perform multi-dimensional cross-analysis on dynamic grid status assessment results and vehicle usage pattern data to obtain a prediction of the vehicle-grid interaction time window;

[0090] (4) Based on the prediction of the vehicle-grid interaction time window, the distribution and capacity of charging stations are optimized to obtain a layout plan for the smart charging infrastructure;

[0091] (5) Perform multi-objective optimization calculations on the layout plan of the smart charging infrastructure to obtain the initial charging scheduling strategy, and dynamically adjust the pricing and incentive mechanism of the initial charging scheduling strategy to obtain the smart charging and vehicle-grid interaction strategy plan.

[0092] Specifically, the output data of the carbon emission dynamic prediction model is subjected to spatiotemporal clustering analysis to identify carbon emission patterns at different times and locations. Spatiotemporal clustering analysis can use the K-means clustering algorithm or the DBSCAN algorithm to group data points according to their geographic location and timestamp. For example, let D represent the carbon emission dataset, d i represents the i-th data point, which contains the timestamp t i and geographic location (x i ,y i ), the clustering goal is to divide D into k clusters, each cluster contains similar time and geographic location data.

[0093]

[0094] Among them, C i represents the i-th cluster, μ i represents the centroid of the cluster, ∥d-μ i ∥ represents the data point d and the cluster centroid μ i The distance between them. The results obtained through spatiotemporal clustering analysis are divided into charging demand hotspots according to geographical location and time period, and a spatiotemporal distribution map of charging demand is obtained. These hotspots reflect the intensity of charging demand in a specific geographical location within a specific time period. For example, the commercial area of ​​a city will have a higher charging demand during the day on weekdays, while the residential area is a charging hotspot at night. According to the spatiotemporal distribution map of charging demand, the grid load data and renewable energy generation forecast data are correlated and analyzed to generate dynamic grid status assessment results. The grid load data includes the power demand of the grid in each time period and geographical area, and the renewable energy generation forecast data includes the power generation forecast of renewable energy such as solar energy and wind energy in different time periods in the future. By correlating these data, the grid load situation and renewable energy supply at different times and places are evaluated. For example, let L(t) represent the grid load at time t, and R(t) represent the renewable energy generation at time t. The dynamic grid status assessment result can be expressed as:

[0095] S(t)=L(t)-R(t);

[0096] Where S(t) is the dynamic grid state at time t. A positive value indicates that the grid load exceeds the renewable energy supply, and a negative value indicates that the renewable energy supply exceeds the grid load. The dynamic grid state assessment results and vehicle usage pattern data are cross-analyzed in multiple dimensions to obtain the vehicle-grid interaction time window prediction. The vehicle usage pattern data includes the driving and charging behaviors of the vehicle in different time periods. By cross-analyzing these data with the grid state, the best time window for vehicle-grid interaction is identified. For example, if the renewable energy generation is high and the grid load is low in a certain time period, this period is suitable for a large number of vehicles to charge or feed power back to the grid. According to the vehicle-grid interaction time window prediction, the distribution and capacity of charging stations are optimized to obtain the layout plan of smart charging infrastructure. By analyzing the charging demand hotspots in different regions and time periods, the geographical distribution and capacity configuration of charging stations are optimized to ensure that sufficient charging services can be provided during the peak period of charging demand. For example, a linear programming model is used to optimize the distribution and capacity configuration of charging stations, with the goal of minimizing the total construction cost and operating cost while meeting the charging demand of all time and space hotspots. Let C represent the construction and operation cost of the charging station, D(t,x,y) represents the charging demand at time t and location (x,y), and the optimization objective is:

[0097]

[0098] Where n represents the number of charging stations, C s represents the cost of the sth charging station, Q s (t,x,y) represents the charging capacity of the sth charging station at time t and location (x,y). Multi-objective optimization calculations are performed on the smart charging infrastructure layout plan to obtain the initial charging scheduling strategy, and the dynamic pricing and incentive mechanism adjustments are performed on the initial charging scheduling strategy, and finally the smart charging and vehicle-grid interaction strategy plan is obtained. The initial charging scheduling strategy maximizes the use of renewable energy and reduces the pressure on the peak load of the power grid by optimizing the vehicle charging time and charging amount. Through the dynamic pricing mechanism, the charging price can be reduced when the grid load is low or the renewable energy supply is sufficient, incentivizing users to charge during these time periods. For example, let P(t) represent the charging price at time t, and the initial charging scheduling strategy D c (t) can be obtained by optimizing the model:

[0099]

[0100]

[0101] Where T represents the total number of time periods, and Q(t) represents the charging capacity at time t.

[0102] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0103] (1) Decompose the smart charging and vehicle-grid interaction strategy schemes to obtain the vehicle usage pattern feature vector;

[0104] (2) Based on the vehicle usage pattern feature vector, multi-source information fusion is performed on the status data of key vehicle components to obtain the vehicle health status index;

[0105] (3) Conduct time series analysis on vehicle health indicators, establish component degradation trend models, and generate prediction results for the remaining life of key components through the component degradation trend models;

[0106] (4) Prioritize maintenance needs based on the remaining life prediction results of key components, build a multi-level maintenance decision tree, and obtain a predictive maintenance scheduling plan;

[0107] (5) Perform a cost-benefit analysis on the predictive maintenance scheduling plan to obtain an initial life optimization strategy, and integrate the real-time vehicle status and environmental factors with the initial life optimization strategy to obtain a predictive maintenance and life optimization plan.

[0108] Specifically, the smart charging and vehicle-grid interaction strategy solutions are decomposed to obtain the vehicle usage pattern feature vector, including indicators such as driving distance, charging frequency, charging time, and discharge frequency. These feature vectors are used to describe the behavior patterns of vehicles in different usage scenarios. For example, let u represent the vehicle usage pattern feature vector, whose components include driving distance d, charging frequency f, and charging time. c 、Charging time t c , discharge frequency f d wait:

[0109] u=[d,f c ,t c ,f d ,…];

[0110] According to the vehicle usage pattern feature vector, the status data of key vehicle components are fused from multiple sources to obtain the vehicle health status index. The status data of key vehicle components include the operating data and sensor data of components such as the engine, transmission, and battery. Through multi-source information fusion technology, these data are integrated to generate an index reflecting the overall health status of the vehicle. For example, let H represent the vehicle health status index, which can be obtained by weighted averaging the status data of each key component:

[0111]

[0112] Among them, s i represents the status data of the i-th key component, wi represents the corresponding weight, and n represents the total number of key components. i It can be set according to the importance of each component and the degree of impact on vehicle health. Perform time series analysis on vehicle health indicators, establish component degradation trend model, and generate prediction results of remaining life of key components through component degradation trend model. Time series analysis can use methods such as autoregressive integral moving average model or long short-term memory network to capture the trend of vehicle health indicators changing over time. For example, let H(t) represent the vehicle health indicator at time t, and use the time series analysis model to predict the health status at future moments:

[0113] H(t+1)=f(H(t),H(t-1),…);

[0114] According to the changing trend of health status, a component degradation trend model is established to predict the remaining life of key components. For example, let R(t) represent the remaining life at time t, which can be predicted by the degradation trend model:

[0115] R(t) = g(H(t), t);

[0116] Among them, function g represents the degradation trend model, which takes into account the influence of health status and time. According to the prediction results of the remaining life of key components, the maintenance needs are prioritized, and a multi-level maintenance decision tree is constructed to obtain a predictive maintenance scheduling plan. The maintenance needs are prioritized based on the remaining life and operational importance of key components. For example, components with short remaining life and high operational importance should be maintained first. Let P i represents the maintenance priority of the i-th component, which can be determined based on the remaining life R i and the importance weight w i calculate:

[0117]

[0118] According to the priority sorting results, a multi-level maintenance decision tree is constructed. The nodes of the decision tree represent different maintenance operations and decision paths, and the leaf nodes represent specific maintenance actions. For example, the root node can be "Is immediate maintenance required?", which is bifurcated according to the remaining life and priority to obtain a predictive maintenance scheduling plan. A cost-benefit analysis is performed on the predictive maintenance scheduling plan to obtain an initial life optimization strategy, and the real-time vehicle status and environmental factors are integrated with the initial life optimization strategy to obtain a predictive maintenance and life optimization plan. The cost-benefit analysis needs to consider the direct cost of maintenance operations and the indirect cost caused by downtime. Let C m represents the maintenance cost, C d represents the downtime cost, and the total cost can be expressed as:

[0119] C=Cm +C d ;

[0120] By optimizing the model, the total cost can be minimized while ensuring the reliability of vehicle operation. The real-time vehicle status and environmental factors are integrated with the initial life optimization strategy, and the final predictive maintenance and life optimization plan is obtained through real-time data updates and dynamic adjustments. For example, the real-time vehicle status includes the current driving data, charging status, ambient temperature, etc. By monitoring these data in real time, the maintenance strategy is dynamically adjusted to ensure the timeliness and effectiveness of the maintenance plan.

[0121] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0122] (1) Calculate the characteristics of predictive maintenance and life optimization solutions to obtain maintenance activity characteristics and life extension indicators, and integrate and analyze the maintenance activity characteristics and life extension indicators with raw material production and manufacturing process data to obtain a basic data set for full life cycle carbon footprint assessment;

[0123] (2) Based on the basic data set of the full life cycle carbon footprint assessment, quantify the carbon emissions at each life cycle stage, build a multidimensional carbon footprint assessment model, and calculate the initial carbon footprint assessment results through the multidimensional carbon footprint assessment model;

[0124] (3) Analyze the key influencing factors of the initial carbon footprint assessment results, establish a carbon footprint sensitivity model, and construct a carbon footprint influencing factor weight matrix through the carbon footprint sensitivity model;

[0125] (4) According to the weight matrix of carbon footprint influencing factors, the predictive maintenance and life optimization schemes are dynamically adjusted to obtain a set of alternative optimization schemes;

[0126] (5) Dynamic response analysis is performed on the alternative set of optimization schemes to obtain an adaptive carbon footprint optimization framework, and feedback adjustment optimization is performed on the adaptive carbon footprint optimization framework to obtain a full life cycle carbon footprint optimization strategy.

[0127] Specifically, the specific characteristics of each maintenance activity are quantified, including maintenance time, maintenance frequency, required resources, and impact on the life of key components. i represents the ith maintenance activity, t i represents the maintenance time, f i represents the maintenance frequency, r i Indicates the required resources, l i Indicates the extension of component life. Through these characteristics, the maintenance activity characteristics and life extension index are calculated. The maintenance activity feature vector can be expressed as:

[0128] m i =[t i ,f i ,r i ];

[0129] The life extension index L can be expressed as the sum of the life extension of each maintenance:

[0130]

[0131] Where n is the total number of maintenance activities. The maintenance activity characteristics and life extension indicators are integrated and analyzed with the raw material production and manufacturing process data to obtain the basic data set for the full life cycle carbon footprint assessment. The data of the raw material production and manufacturing process include the energy consumption, carbon emissions, and production efficiency of each link. For example, suppose E p Represents the energy consumption of raw material production process, C p represents the corresponding carbon emissions. The energy consumption and carbon emissions of the manufacturing process are E m and C m By integrating the data of maintenance activities and manufacturing processes, a comprehensive data set covering all life cycle stages is formed. The basic data set for full life cycle carbon footprint assessment can be expressed as:

[0132] LCF={(m i ,l i ,E p ,C p ,E m ,C m ,…)};

[0133] Based on the basic data set, the carbon emissions at each life cycle stage are quantified and calculated, and a multi-dimensional carbon footprint assessment model is constructed. The carbon footprint assessment model can use the life cycle analysis method to quantify the carbon emissions at each stage and calculate the carbon footprint of the entire life cycle. For example, assuming the total carbon emissions C total The sum of carbon emissions in each stage:

[0134]

[0135] in, Represents the carbon emissions of the ith maintenance activity. After calculating the initial carbon footprint assessment results, key influencing factor analysis is performed to establish a carbon footprint sensitivity model. The key influencing factor analysis is to identify the factors that have the greatest impact on carbon emissions, such as energy type, production process, maintenance frequency, etc. Through regression analysis or sensitivity analysis, the impact of these factors is determined. The carbon footprint sensitivity model can be expressed as:

[0136]

[0137] Among them, S represents carbon footprint sensitivity, F j represents the jth influencing factor, w j represents the corresponding weight, and m is the total number of influencing factors. The carbon footprint influencing factor weight matrix is ​​constructed through the carbon footprint sensitivity model. This matrix is ​​used to represent the weight of each influencing factor on carbon emissions. Let W be the weight matrix, where the element w i j represents the weight of the influence of the i-th factor on the carbon emissions of the j-th life cycle stage. The weight matrix can help understand which factors contribute the most to carbon emissions, so as to optimize them in a targeted manner. According to the weight matrix of carbon footprint influencing factors, the predictive maintenance and life optimization schemes are dynamically adjusted to obtain the optimization scheme alternative set. These alternatives are aimed at reducing carbon emissions by adjusting maintenance frequency, selecting low-carbon materials, improving manufacturing processes, etc. For example, by adjusting the maintenance frequency, unnecessary maintenance activities can be reduced, thereby reducing carbon emissions. The optimization goal can be expressed as minimizing total carbon emissions. Dynamic response analysis is performed on the optimization scheme alternative set to obtain an adaptive carbon footprint optimization framework. Dynamic response analysis evaluates the effects of each alternative by simulating carbon emissions under different conditions. The adaptive carbon footprint optimization framework can be adjusted according to real-time data and environmental changes to ensure that carbon emissions can be minimized under different conditions. For example, when the supply of renewable energy is sufficient, the frequency of maintenance activities can be increased and low-carbon energy can be used for maintenance, otherwise the maintenance frequency can be reduced. Feedback adjustment optimization is performed on the adaptive carbon footprint optimization framework to obtain a full life cycle carbon footprint optimization strategy. Feedback optimization ensures that the optimization framework is always in the best state by continuously monitoring and adjusting it.

[0138] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0139] (1) Extract decision variables and constraints for the full life cycle carbon footprint optimization strategy, obtain decision variables and constraints, and integrate the decision variables and constraints with external environmental factors to generate a basic parameter set for multi-scenario prediction;

[0140] (2) Scenario construction is performed based on the basic parameter set of multi-scenario prediction to obtain the scenario probability distribution matrix;

[0141] (3) Simulate the carbon emission trajectory for each scenario in the scenario probability distribution matrix, construct a dynamic programming model, and calculate the initial carbon neutrality path network for each decision node through the dynamic programming model;

[0142] (4) Conduct path sensitivity analysis on the initial carbon neutrality path network to obtain influencing factors and potential risk points, and establish a path evaluation indicator system based on the influencing factors and potential risk points;

[0143] (5) Generate a path feasibility score sheet based on the path evaluation index system, and conduct a multi-criteria decision analysis on the path feasibility score sheet to obtain a dynamically adjusted carbon neutrality path plan;

[0144] (6) Configure feedback mechanism and checkpoints for the dynamically adjusted carbon neutrality path plan to obtain the carbon neutrality path for intelligent connected electric vehicles.

[0145] Specifically, we analyze the specific processes and influencing factors of each life cycle stage, including raw material production, manufacturing, use and scrap recycling. By analyzing these links, we can determine the key decision variables, such as production process, energy use, material selection, maintenance frequency, etc., as well as the corresponding constraints such as resource limitations, cost limitations, and regulatory requirements. For example, let x 1 represents the choice of production process, x 2 represents the energy usage, x 3 Indicates material selection, x 4 represents the maintenance frequency. The constraints can be expressed as resource constraints R and cost constraints C.

[0146]

[0147] Among them, r i represents the resource consumption of the ith decision variable, c i represents the cost of the ith decision variable, and n represents the total number of decision variables. The decision variables and constraints are integrated with external environmental factors to generate a basic parameter set for multi-scenario prediction. External environmental factors include market demand, policy changes, energy prices, climate conditions, etc. For example, let the external environmental factor be e 1 (Market demand), e 2 (Policy Changes), e 3 (Energy price), e 4 (climate conditions), by integrating these factors, a basic parameter set for multi-scenario prediction is generated:

[0148] P={(x 1 ,x 2 ,x 3 ,x 4 ,e 1 ,e 2 ,e 3 ,e 4 )};

[0149] Scenarios are constructed based on the basic parameter set for multi-scenario prediction, and the scenario probability distribution matrix is ​​obtained. The probability of each scenario occurring is evaluated by simulating the parameter combinations under different scenarios. For example, let S represent the scenario, P(S i ) indicates scenario S iThe probability of occurrence can be generated through statistical methods or Monte Carlo simulation to generate a scenario probability distribution matrix:

[0150]

[0151] Where m represents the total number of scenarios. The carbon emission trajectory is simulated for each scenario in the scenario probability distribution matrix, a dynamic programming model is constructed, and the initial carbon neutral path network of each decision node is calculated through the dynamic programming model. The carbon emission trajectory simulation is based on the carbon emissions under different scenarios. The path selection of each decision node can be optimized through the dynamic programming model. For example, let C i Indicates scenario S i The dynamic programming goal is to minimize the total carbon emissions under:

[0152]

[0153] Conduct path sensitivity analysis on the initial carbon neutrality path network to obtain influencing factors and potential risk points, and establish a path evaluation index system based on the influencing factors and potential risk points. Sensitivity analysis identifies the most critical influencing factors and potential risk points by evaluating the impact of various factors on path selection. For example, suppose F j represents the jth influencing factor, R k represents the kth risk point, and the path evaluation index system can be expressed as:

[0154] I={F j ,R k};

[0155] A path feasibility scoring table is generated based on the path evaluation index system, and a multi-criteria decision analysis is performed on the path feasibility scoring table to obtain a dynamically adjusted carbon neutral path plan. The path feasibility scoring table scores different path plans by comprehensively considering various evaluation indicators. For example, if the path feasibility score is S p , multi-criteria decision analysis can use weighted scoring method:

[0156]

[0157] Among them, w j represents the weight of the jth indicator, I jRepresents the score of the jth indicator. The feedback mechanism and checkpoint configuration are performed on the dynamically adjusted carbon neutral path plan to obtain the carbon neutral path of the intelligent connected electric vehicle. The feedback mechanism ensures the real-time optimization of the plan by continuously monitoring and adjusting the path plan. Checkpoint configuration refers to setting key points during the path execution process, evaluating the path execution effect in real time, and making necessary adjustments and optimizations. For example, in path planning, every time a key time node or milestone is passed, the current carbon emissions and resource usage are evaluated, and adjustments are made according to the actual situation to ensure the achievement of the carbon neutrality goal.

[0158] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0159] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0160] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent connected electric carbon neutral system based on the BECNU negative carbon emission ecosystem project, characterized in that: include: The full life cycle carbon emission system is used to calculate the carbon emissions of vehicles in the five stages of their life cycle: material supply, parts and vehicle manufacturing, charging and use, scrapping and recycling, and intelligent network cloud services; Ecosystem Engineering BECNU negative carbon emission system is used to provide globally recognized carbon removal certification carbon sink credits C RCF ; Intelligent connected systems for carbon offsetting within system boundaries; On-board carbon neutrality smart instrument, used for digital intelligent network recording, measurement and accounting, certification and carbon label neutrality identification of carbon footprint emission and removal information; Among them, the carbon footprint quantity technical model satisfies the following calculation formula: C t +C MA +C VM +C USE +C REC +C RCF =0, C t Carbon footprint of cloud network digital center, C MA Carbon footprint for material acquisition and supply, C VM is the carbon footprint of parts and vehicle manufacturing process, C USE The carbon footprint of the charging and energy storage and discharge process, C REC is the carbon footprint of the end-of-life recycling process, C RCF Carbon sink credits that are internationally recognized for carbon removal certification.

2. According to claim 1, the intelligent networked electric carbon neutral system based on the BECNU negative carbon emission ecosystem engineering is characterized in that: The full life cycle carbon emission system includes: The material supply carbon emission subsystem is used to calculate the first unavoidable carbon emissions and its first carbon footprint in the material supply stage; the material supply carbon emission subsystem uses low-carbon emission, ultra-low carbon emission and carbon-neutral metal and non-metal materials, as well as recycled materials and renewable materials, and measures, verifies and certifies the first carbon footprint of the materials in accordance with international or domestic standards to obtain data corresponding to the first carbon footprint and its first carbon label; The carbon emission subsystem for parts and vehicle manufacturing is used to calculate the second unavoidable carbon emissions and its second carbon footprint during the manufacturing stage; the carbon emission subsystem for parts and vehicle manufacturing adopts low-carbon emission, ultra-low carbon emission and carbon-neutral energy, low-carbon and intelligent manufacturing processes, and measures, accounts for, verifies and certifies the second carbon footprint of the manufacturing process in accordance with international or domestic standards, and obtains the data corresponding to the second carbon footprint and its second carbon label; The charging and use carbon emission subsystem is used to calculate the third unavoidable carbon emissions and its third carbon footprint during the charging and use stages; the charging and use carbon emission subsystem adopts low-carbon emission, ultra-low carbon emission and carbon neutral energy power charging, and the energy power includes wind power, photovoltaic solar power, and uses new energy batteries to participate in smart grid and microgrid energy storage. The third carbon footprint of the charging and use process is measured, calculated, verified and certified according to international standards or domestic standards, and the data corresponding to the third carbon footprint and its third carbon label are obtained; The scrap recycling carbon emission subsystem is used to calculate the fourth unavoidable carbon emissions and its fourth carbon footprint in the scrap recycling stage; the scrap recycling carbon emission subsystem adopts low-carbon emission, ultra-low carbon emission and carbon neutral energy and electricity, low-carbon emission, ultra-low carbon emission and carbon neutral logistics measures, and measures, calculates, verifies and certifies the fourth carbon footprint of the whole process of scrap recycling in accordance with international standards or domestic standards, and obtains the data corresponding to the fourth carbon footprint and its fourth carbon label; The vehicle-computer industrial Internet, vehicle-road-cloud energy smart microgrid and digital center carbon emission subsystem are used to calculate the relevant fifth unavoidable carbon emissions and its fifth carbon footprint.

3. According to claim 1, the intelligent networked electric carbon neutral system based on the BECNU negative carbon emission ecosystem engineering is characterized in that: The vehicle-machine industrial Internet and vehicle-road-cloud-energy smart microgrid and digital center carbon emission subsystem includes the unavoidable carbon emissions of the vehicle-machine industrial Internet, the unavoidable carbon emissions of the smart microgrid charging and use, and the carbon fingerprint environmental benefit emission reduction of the battery participating in energy storage and discharge, and carries out measurement, accounting, verification and certification of the fifth carbon footprint of the industrial Internet, smart microgrid and digital center in accordance with international standards or domestic standards, and obtains the data corresponding to the fifth carbon footprint and its fifth carbon label; the on-board carbon neutrality smart instrument is connected to the vehicle-machine system and the smart microgrid cloud for communication and data transmission, and realizes intelligent, digital and transparent carbon footprint identification with the help of the vehicle-road-cloud-energy smart network cloud system and the digital smart cloud supervision, inspection, testing and certification and accreditation CNAS system; the factory certificate of the on-board carbon neutrality smart instrument and the initial data table of the carbon neutrality label of the instrument clearly indicate C t , C MA , C VM , C USE , C REC The carbon footprint value of the vehicle manufacturer or operator and the C RCF Carbon Removal Certified Carbon Sink Credits and their Ecosystem Engineering BECNU Negative Carbon Emissions neutralize the vehicle’s unavoidable carbon emissions.

4. According to claim 3, the intelligent networked electric carbon neutral system based on the BECNU negative carbon emission ecosystem engineering is characterized in that: The C RCF Carbon removal certification carbon sink credits include the following two properties and uses: Used to neutralize C t +C MA +C VM C RCF Carbon removal certification carbon sink credits are liquidated and cancelled according to market rules when the vehicle leaves the factory; Used to neutralize the third inevitable carbon emissions during charging USE C RCF Carbon removal certified carbon sink credits, as regulated carbon assets attached to the carbon-neutral vehicle itself, have their value included in the sales price of the carbon-neutral vehicle, and have built-in presets for offsetting the inevitable carbon emissions during the charging and use of the carbon-neutral vehicle.

5. According to claim 1, the intelligent networked electric carbon neutral system based on the BECNU negative carbon emission ecosystem engineering is characterized in that: It also includes the BECNU negative carbon emission neutralization incentive mechanism, an ecosystem project for carbon neutral car owners. The mechanism connects the carbon reduction market with the carbon removal market and uses the car owner's digital identity ID in the cloud network digital center system to build a carbon neutral car owner's carbon emission neutralization incentive mechanism based on the carbon emission neutralization incentive mechanism built into the car when it leaves the factory. RCF Internationally recognized carbon removal certification carbon sink credit assets are managed to achieve negative carbon emission neutrality and ecological value incentives for ecosystem engineering BECNU.

6. A method for achieving carbon neutrality throughout the life cycle of an intelligent connected electric vehicle, characterized in that: Applied to the system according to any one of claims 1 to 5, the method comprises: Distributed collection and preprocessing of multi-source data on raw material production, vehicle manufacturing, use process and scrapping and recycling to obtain a full life cycle carbon emission data set; Conduct multi-level and multi-scale modeling and fusion analysis on the full life cycle carbon emission data set to obtain a dynamic prediction model for carbon emissions; Through the carbon emission dynamic prediction model, spatiotemporal data mining and multi-objective optimization calculation are performed to obtain smart charging and vehicle-grid interaction strategy solutions; Perform multi-sensor data fusion and degradation analysis on the smart charging and vehicle-grid interaction strategy solutions and vehicle status data to obtain predictive maintenance and life optimization solutions; Perform multi-dimensional carbon footprint evaluation and dynamic trade-off calculation on the predictive maintenance and life optimization scheme to obtain a full life cycle carbon footprint optimization strategy; Multi-scenario prediction and adaptive path planning are performed on the full life cycle carbon footprint optimization strategy to obtain a carbon-neutral path for the intelligent connected electric vehicle.

7. The method for achieving carbon neutrality throughout the life cycle of an intelligent networked electric vehicle according to claim 6, characterized in that: The multi-source data of raw material production, vehicle manufacturing, use process and scrapping and recycling are distributedly collected and preprocessed to obtain a full life cycle carbon emission data set, including: Collect energy consumption and emission data in the raw material production process in real time, build a supply chain network model based on the material flow relationship, and obtain the raw material carbon emission data structure; Conduct time series collection and anomaly identification on production line data of vehicle manufacturing process to obtain the time series of carbon emissions in the manufacturing stage; The driving data, charging data and environmental data during the use of the vehicle are integrated from multiple sources, cleaned and standardized to obtain the carbon emission characteristic data during the use phase; Classify and sort the disassembly data and material reuse data in the scrap recycling process to obtain the carbon emission classification results in the scrap recycling stage; Performing data alignment and time synchronization on the raw material carbon emission data structure, the manufacturing stage carbon emission time series, the use stage carbon emission characteristic data and the scrapping and recycling stage carbon emission classification results to obtain the full life cycle carbon emission spatiotemporal correlation data; The full life cycle carbon emission spatiotemporal correlation data is distributedly stored and securely encrypted to obtain a full life cycle carbon emission data set.

8. The method for achieving carbon neutrality throughout the life cycle of an intelligent networked electric vehicle according to claim 6, characterized in that: The multi-level and multi-scale modeling and fusion analysis of the full life cycle carbon emission data set to obtain a carbon emission dynamic prediction model includes: Decomposing the full life cycle carbon emission data set by time scale and dividing it into short-term, medium-term and long-term carbon emission data subsets to obtain a multi-time scale carbon emission data structure; According to the multi-time-scale carbon emission data structure, a spatial correlation analysis is performed on the carbon emission data of each life cycle stage to construct a multi-spatial-scale carbon emission correlation model; Cross-validating and extracting features of the multi-time-scale carbon emission data structure and the multi-spatial-scale carbon emission correlation model to obtain multi-dimensional correlation features of carbon emissions; According to the multi-dimensional correlation characteristics of carbon emissions, corresponding sub-models are established for each life cycle stage of raw material production, vehicle manufacturing, use process and scrapping and recycling, and the sub-models are coupled and integrated to obtain an initial prediction model for carbon emissions over the entire life cycle; Performing historical data backtesting and error analysis on the initial prediction model of carbon emissions over the entire life cycle to obtain error distribution characteristics, and designing a model correction mechanism based on the error distribution characteristics to obtain a self-correcting carbon emissions prediction model; The dynamic parameter update mechanism and real-time data access interface of the self-correcting carbon emission prediction model are optimized to obtain a carbon emission dynamic prediction model.

9. The method for achieving carbon neutrality throughout the life cycle of an intelligent networked electric vehicle according to claim 6, characterized in that: The carbon emission dynamic prediction model is used to perform spatiotemporal data mining and multi-objective optimization calculations to obtain smart charging and vehicle-grid interaction strategy solutions, including: Performing spatiotemporal clustering analysis on the output data of the carbon emission dynamic prediction model to obtain spatiotemporal clustering results, and dividing the charging demand hotspots of the spatiotemporal clustering results according to geographical locations and time periods to obtain a spatiotemporal distribution map of charging demand; According to the spatiotemporal distribution diagram of charging demand, correlation analysis is performed on the grid load data and the renewable energy generation forecast data to generate a dynamic grid state assessment result; Performing a multi-dimensional cross-analysis on the dynamic grid state assessment results and vehicle usage pattern data to obtain a vehicle-grid interaction time window prediction; According to the vehicle-grid interaction time window prediction, the distribution and capacity of charging stations are optimized to obtain a smart charging infrastructure layout plan; A multi-objective optimization calculation is performed on the smart charging infrastructure layout plan to obtain an initial charging scheduling strategy, and dynamic pricing and incentive mechanism adjustment are performed on the initial charging scheduling strategy to obtain a smart charging and vehicle-grid interaction strategy plan.

10. The method for achieving carbon neutrality throughout the life cycle of an intelligent networked electric vehicle according to claim 6, characterized in that: The multi-sensor data fusion and degradation analysis of the smart charging and vehicle-grid interaction strategy and vehicle status data to obtain a predictive maintenance and life optimization solution includes: Decomposing the smart charging and vehicle-grid interaction strategy scheme to obtain a vehicle usage pattern feature vector; According to the vehicle usage pattern feature vector, multi-source information fusion is performed on the status data of key components of the vehicle to obtain a vehicle health status indicator; Performing time series analysis on the vehicle health status indicators, establishing a component degradation trend model, and generating a prediction result of the remaining life of key components through the component degradation trend model; Prioritize maintenance needs according to the remaining life prediction results of the key components, build a multi-level maintenance decision tree, and obtain a predictive maintenance scheduling plan; A cost-benefit analysis is performed on the predictive maintenance scheduling scheme to obtain an initial life optimization strategy, and real-time vehicle status and environmental factors are integrated with the initial life optimization strategy to obtain a predictive maintenance and life optimization scheme.

Images