Charging pile digital asset calculation method and system based on dynamic carbon perception

Abstract

The invention discloses a charging pile digital asset calculation method based on dynamic carbon perception, and the method comprises the following steps: S1, obtaining the clean energy proportion data of a regional power grid where a charging pile is located in real time through a built-in communication module of the charging pile; s2, calculating a carbon emission reduction coefficient according to the clean energy proportion data; s3, collecting user charging behavior data including the charging electric quantity and the charging time period, and obtaining the ratio of the current capacity to the rated capacity of the vehicle battery through the vehicle-mounted terminal; s4, generating a digital asset value based on the dynamic digital asset calculation model; and S5, writing the digital asset value R into a block chain account book, and synchronously sending a power adjustment instruction to a charging pile control system. According to the method, power grid real-time carbon emission data, user charging behavior characteristics and a charging pile power control technology are combined, collaborative optimization of digital asset issuing and power grid loads is achieved through a block chain, and the method is mainly applied to an intelligent charging pile operation management system.

Description

Technical Field

[0001] The present invention relates to the technical field of blockchain management of new energy vehicle charging piles, and in particular to a charging pile digital asset calculation method and system based on dynamic carbon perception. The method combines real-time carbon emission data of the power grid, user charging behavior characteristics and charging pile power control technology, and realizes the coordinated optimization of digital asset issuance and power grid load through blockchain. The method is mainly used in intelligent charging pile operation and management systems. Background Art

[0002] With the rapid development of new energy vehicles, the importance of charging piles as energy supply infrastructure has become increasingly prominent. How to quantify the carbon emission reduction benefits generated by charging behavior and convert them into traceable digital assets has become the focus of current technical research. In the existing technology, the carbon emission reduction calculation and digital asset management solutions related to charging piles mainly include the following types: (1) Carbon emission reduction calculation based on fixed coefficients: Some schemes use a preset static carbon emission coefficient to estimate the emission reduction during the charging process, such as calculating the carbon emission reduction value through the regional average power generation structure. For example, patent CN117648518A proposes a method and system for calculating the carbon reduction of electric piles. Various emission factors for calculating carbon emissions are preferentially obtained through the carbon emission factor database, which can be dynamically updated and obtained according to priority. This method does not take into account the real-time fluctuations in the proportion of clean energy in the power grid, resulting in a deviation between the calculation results and the actual emission reduction benefits.

[0003] (2) Binding digital assets to charging behavior: Existing technologies usually directly link the issuance of digital assets to the amount of charging power. For example, patent CN114997631A discloses a method, device, equipment, medium, and method for scheduling charging of electric vehicles. The method determines the real-time incentive points corresponding to the electric vehicles in the current scheduling based on the power load data of the power grid, the charging price corresponding to the current scheduling in the electric vehicle time-sharing charging price table, the electric vehicle status data, and the charging station status data, and determines the charging cost ratio saved by the electric vehicle participating in the current scheduling based on the real-time incentive points. However, such methods do not consider the impact of the battery health state (SOH) on the charging efficiency, resulting in high-loss batteries still being able to obtain the same digital assets as healthy batteries, resulting in unreasonable resource allocation.

[0004] (3) Charging power control technology: Existing charging pile power adjustment schemes are mostly based on the rated power of the equipment or user demand. Such methods lack coordination with the real-time load of the power grid, which may aggravate the contradiction between supply and demand during periods of high power load, or fail to fully utilize surplus power during periods of sufficient clean energy.

[0005] (4) Application of blockchain in charging piles: Existing blockchain technology is mostly used to record charging transaction data. For example, patent CN 114218679A, a vehicle-grid interactive carbon emission reduction accounting method and system based on blockchain, uses a single chain structure to store charging records and calculate carbon emission reduction. However, with the generation of massive charging data, a single blockchain faces the problems of low storage efficiency and poor data penetration of regulatory nodes, making it difficult to meet the needs of multi-subject collaborative management.

[0006] Based on the existing technology, there are mainly the following technical defects: 1. Insufficient calculation accuracy of carbon emission reduction: The static coefficient cannot reflect the dynamic changes in the proportion of clean energy in the power grid, resulting in low credibility of carbon emission reduction values; 2. The digital asset allocation mechanism is unreasonable: the weight is not adjusted in combination with the battery health status, and high-loss batteries obtain excess returns, exacerbating resource waste; 3. Power control is disconnected from the power grid: Charging power regulation does not take into account the real-time carrying capacity of the power grid, which can easily cause local power grid overload or abandonment of clean energy; 4. Low efficiency of data storage and supervision: The blockchain structure is designed to be single, and regulators cannot efficiently verify shard data, which poses a risk of data tampering; 5. Weak anti-cheating measures: Existing solutions rely on manual review of vehicle identity, making it difficult to intercept fraudulent behaviors such as repeated charging across charging piles in real time. Summary of the invention

[0007] In response to the above problems, the present invention proposes a charging pile digital asset calculation method and system that integrates dynamic carbon perception and multi-dimensional data. By real-time collection of the clean energy proportion of the power grid, battery health status and user charging behavior data, combined with blockchain shard storage and power collaborative control, the coordinated optimization of digital asset issuance and power grid load is achieved, thereby improving the clean energy consumption rate.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions: A method for calculating charging pile digital assets based on dynamic carbon perception includes the following steps: S1: Through the built-in communication module of the charging pile, the clean energy proportion data of the power grid in the area where the charging pile is located is obtained in real time; S2: Calculate the carbon emission reduction coefficient according to the clean energy proportion data, and the carbon emission reduction coefficient satisfies the formula: Among them, Kc is the carbon emission reduction coefficient, α is the adjustment factor based on the dynamic adjustment of regional carbon emission policies, The clean energy power generation capacity in the current power grid, is the total power generation capacity of the power grid; S3: Collect user charging behavior data, including charging power E and charging period T, and obtain the vehicle battery health status SOH through the vehicle terminal, where SOH is the ratio of the current battery capacity to the rated capacity; S4: Generate a digital asset value R based on a dynamic digital asset calculation model, wherein the model is: Among them, β is the preset digital asset benchmark coefficient, f(T) is the time period adjustment function dynamically adjusted according to the charging period T , g(SOH) is the weight function based on the battery health state SOH; S5: Write the digital asset value R into the blockchain account book, and simultaneously send a power adjustment instruction to the charging pile control system so that the charging power P satisfies: in, is the rated power of the charging pile; The upper limit of the power that the grid can allocate to the charging pile in real time is used to achieve coordinated optimization of charging power and grid load.

[0009] Furthermore, in order to obtain more reliable clean energy proportion data, the acquisition of clean energy proportion data in step S1 includes the following sub-steps: S1a: Read the real-time power generation data of the power grid through smart meters; S1b: Access the regional energy management platform through the preset API interface to obtain the proportion of photovoltaic power generation and wind power generation in the total power generation; S1c: If the deviation of the clean energy proportion data obtained twice in a row exceeds 10%, the edge computing node is called to perform interpolation correction on the abnormal data, and the interpolation correction adopts linear interpolation method.

[0010] Furthermore, in step S4, the value of the time period adjustment function f(T) is: Among them, peak and valley electricity periods are divided according to the regional power grid load curve.

[0011] Furthermore, in step S4, the value of the battery health weight function g(SOH) is: Furthermore, in order to ensure data security and regulatory efficiency, in step S5, the blockchain ledger adopts a shard storage structure, including: Main chain: stores summary information of digital asset issuance hash values ​​and user wallet addresses; Shard chain: divided by the geographical area to which the charging pile belongs, storing the charging pile unique identifier, digital asset details and power adjustment instructions; Among them, the regulatory node holds the main chain key and can query the complete data of the shard chain through the main chain.

[0012] Furthermore, the method further includes an anti-cheating verification step: S6a: The camera of the charging pile is used to collect the image of the vehicle VIN code, and the VIN code is extracted using an optical character recognition algorithm; S6b: Compare the extracted VIN code with the vehicle information bound to the user account; S6c: If the same VIN code initiates a charging request at different charging piles within the preset 30 minutes, the issuance of digital assets will be frozen and the manual review process will be triggered.

[0013] Furthermore, in order to ensure that the charging power is always within the safety range of the power grid and the equipment, the maximum charging power limit allowed to be output by the charging pile under the current power grid state is Obtain it by following these steps: S7a: Read device rated power from charger controller ; S7b: Access the power distribution monitoring system through the Modbus protocol to obtain the upper limit of the power that the current power grid can allocate to the charging pile; S7c: Compare charging station power ratings Distributable power to the grid , take the smaller value as the upper limit of charging power : The present invention also provides a system for the method, comprising: Data acquisition module: The power monitoring unit, communication module and image acquisition device integrated in the charging pile are used to collect power grid data, user charging behavior data and vehicle VIN code images in real time; Edge computing nodes: deployed on the local server of the charging station to perform carbon emission reduction coefficient calculation, abnormal data verification and interpolation correction; Blockchain network: A consortium chain consisting of charging operator nodes, regulatory agency nodes, and partner merchant nodes, used to store digital asset values ​​and power regulation instructions; Power control module: dynamically adjusts the output power of the charging pile according to the power adjustment instructions issued by the blockchain network.

[0014] Due to the adoption of the above scheme, the beneficial effects of the present invention are as follows: 1. Dynamically correct the carbon emission reduction coefficient to improve the matching degree between digital assets and real emission reduction benefits; 2. Introduce a battery health weight function to optimize the fairness of digital asset distribution; 3. Improve the clean energy consumption rate through real-time coordination of power instructions and grid load; 4. Build a blockchain sharding architecture that can penetrate supervision to ensure data security and supervision efficiency; 5. Integrate optical character recognition (OCR) and VIN code verification technology to enhance anti-cheating capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a charging pile digital asset calculation method based on dynamic carbon perception of the present invention; Figure 2 A flowchart of obtaining clean energy proportion data in the charging pile digital asset calculation method based on dynamic carbon perception of the present invention; Figure 3 It is a flow chart of anti-cheating verification in the charging pile digital asset calculation method based on dynamic carbon perception of the present invention; Figure 4 The process of obtaining the upper limit of charging power in the method for calculating the digital assets of charging piles based on dynamic carbon perception of the present invention; Figure 5 This is a schematic diagram of the application of the charging pile digital asset calculation method based on dynamic carbon perception in the intelligent charging pile operation and management system of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] like Figure 1 As shown, a method for calculating the digital assets of a charging pile based on dynamic carbon perception specifically includes the following steps: Step S1: The charging pile has a built-in 4G communication module (model: Quectel EC25) and a smart meter (model: Siemens7KM2110). Through the RS485 interface of the smart meter, the total power generation power of the power grid in the area where the charging pile is located is obtained in real time. Thermal power generation , calculate the clean energy power generation Clean energy includes renewable energy such as hydropower, photovoltaics, and wind power, and transmits the data to edge computing nodes for preliminary processing and analysis. Figure 2As shown, the real-time power generation data of the power grid is read through the smart meter built into the charging pile, and the regional energy management platform is connected through the preset API interface (interface protocol: RESTful) to obtain the minute-level update data of photovoltaic and wind power generation, calculate the clean energy ratio, and then encrypt the data and upload it to the blockchain network. If the deviation of the clean energy ratio data obtained twice exceeds 10% (for example, the previous time was 30%, and the current mutation is 15%), the edge computing node (deployed on the local server of the charging station, CPU: Intel Xeon E5-2678) is called to interpolate and correct the abnormal data. The interpolation correction adopts linear interpolation to ensure the accuracy and continuity of the clean energy ratio data.

[0018] Step S2, calculating the carbon emission reduction coefficient according to the clean energy proportion data obtained in step S1, and the calculation formula is: Among them, Kc is the carbon emission reduction coefficient, α is the adjustment factor based on the dynamic adjustment of regional carbon emission policies, which is dynamically set according to the "Carbon Emission Intensity Guidelines" issued by local governments (for example: Beijing α=1.2, Qinghai Province α=0.9), The clean energy power generation capacity in the current power grid, is the total power generation of the power grid. For example: =50kw, =200kw, α=1.2, Kc=1.2×(50÷200)=0.3.

[0019] Step S3, collect user charging behavior data, which includes the charging power E of the new energy vehicle bound to the user account (real-time collection through the charging pile power metering module), charging time period T (based on Beijing time, divided into peak power time period: 10:00-15:00, 18:00-21:00; flat power time period: 7:00-10:00, 15:00-18:00, 21:00-23:00; valley power time period: 23:00-7:00), in addition, the vehicle battery (BMS) health status SOH is obtained through the vehicle terminal (CAN bus, baud rate: 500kbps), where SOH is the ratio of the current battery capacity to the rated capacity. The calculation formula is: Step S4, generate a digital asset value R based on a dynamic digital asset calculation model, and allocate the digital asset value R to the corresponding user asset account. The model comprehensively considers the carbon reduction coefficient, charging power, charging time and battery health status to ensure fairness and rationality. The model is: Among them, β is the preset digital asset benchmark coefficient, f(T) is the time period adjustment function dynamically adjusted according to the charging period T , g(SOH) is the weight function based on the battery health state SOH; The value of the time period adjustment function f(T) is: The value of the battery health weight function g(SOH) is: Calculation example: When β=0.5 (base coefficient), E=30kWh, Kc=0.3, f(T)=1.2, g(SOH)=1.0: R = 0.5 × 0.3 × 30 × 1.2 × 1.0 = 5.4 (digital asset unit) Step S5, write the above digital asset value R into the alliance chain (platform: Hyperledger Fabric) through a smart contract (written in Solidity language), generate a transaction hash value and store it in the shard chain (sharding rule: divided by province); the user wallet address summary is generated using the SHA-256 algorithm, for example, the address 0x3FZb... generates the summary 9f86d.... Power control instruction: such as Figure 4 As shown, the current grid distributable power is read from the power distribution monitoring system (model: Schneider PowerLogic ION9000) =100kw, rated power of charging pile =120kw, whichever is smaller =100kw, send a power adjustment instruction to the charging pile control system through the MQTT protocol to limit the charging power to P≤100kw.

[0020] Step S6, anti-cheating verification. Figure 3 As shown, VIN code recognition: Use the charging pile camera (model: Hikvision DS-2CD1221) to capture the VIN code image of the vehicle's front windshield, extract the VIN code through the Tesseract OCR engine, and compare it with the vehicle information bound to the user's APP. Exception handling: If the same VIN code initiates a charging request at both charging pile A and charging pile B within 30 minutes, the user's digital asset account will be frozen and an alarm message will be pushed to the operation management platform (manual review and unlocking are required).

[0021] like Figure 5As shown, a charging pile digital asset allocation system based on dynamic carbon perception, a data acquisition module 101, including a power monitoring unit integrated in the charging pile (integrated voltage / current sensor, model: LEM LV25-P), a communication module (supporting 4G / 5G dual-mode communication, Quectel RM500Q) and an image acquisition device (2 million pixel wide-angle camera, field of view 120°), for real-time collection of power grid data, user charging behavior data and vehicle VIN code images; an edge computing node 102, deployed on a local server of a charging station (operating system: Ubuntu 20.04), runs a carbon emission reduction coefficient calculation program (Python3.8), for performing carbon emission reduction coefficient calculation, abnormal data verification and interpolation correction. If the data deviation exceeds the threshold, the interpolation algorithm (Scipy library interp1d function) is called. Blockchain network 103, the main chain node is deployed by the national regulatory agency (server configuration: 64 cores / 256GB memory); the shard chain nodes are divided into North China, East China and other regions, storing the charging pile ID and digital asset details; regulatory penetration query, the regulatory agency accesses the shard chain data through the main chain key (query response time <1 second). Power control module 104, the core controller uses the STM32F407 microcontroller; the power regulation circuit uses the IGBT module (model: Infineon FF450R12KE4), supporting 0-120kW continuous regulation.

[0022] Due to the adoption of the above scheme, the present invention realizes the dynamic correction of carbon emission reduction coefficient, improves the matching degree between digital assets and real emission reduction benefits, and automatically executes transactions through smart contracts to ensure data transparency and credibility, encourage users to participate in emission reduction, promote the popularization of green energy, form a virtuous circle, promote sustainable development, and demonstrate the far-reaching significance of technology empowering environmental protection. In addition, the battery health weight function is introduced to optimize the fairness of digital asset allocation; the clean energy consumption rate is improved through real-time coordination of power instructions and grid loads; a blockchain sharding architecture that can penetrate supervision is constructed to ensure data security and supervision efficiency; optical character recognition (OCR) and VIN code verification technology are integrated to enhance anti-cheating capabilities.

Claims

1. A charging pile digital asset calculation method based on dynamic carbon perception, characterized by: The following steps are involved: S1: Through the built-in communication module of the charging pile, the clean energy proportion data of the power grid in the area where the charging pile is located is obtained in real time; S2: Calculate the carbon emission reduction coefficient according to the clean energy proportion data, and the carbon emission reduction coefficient satisfies the formula: ； Among them, Kc is the carbon emission reduction coefficient, α is the adjustment factor based on the dynamic adjustment of regional carbon emission policies, The clean energy power generation capacity in the current power grid, is the total power generation capacity of the power grid; S3: Collect user charging behavior data, including charging power E and charging period T, and obtain the vehicle battery health status SOH through the vehicle terminal, where SOH is the ratio of the current battery capacity to the rated capacity; S4: Generate a digital asset value R based on a dynamic digital asset calculation model, wherein the model is: ； Among them, β is the preset digital asset benchmark coefficient, f(T) is the period adjustment function dynamically adjusted according to the charging period T, and g(SOH) is the weight function based on the battery health state SOH; S5: Write the digital asset value R into the blockchain account book, and simultaneously send a power adjustment instruction to the charging pile control system so that the charging power P satisfies: ； in, is the rated power of the charging pile; The upper limit of the power that the grid can allocate to the charging pile in real time is used to achieve coordinated optimization of charging power and grid load.

2. The method according to claim 1, characterized in that: The acquisition of clean energy proportion data in step S1 includes the following sub-steps: S1a: Read the real-time power generation data of the power grid through smart meters; S1b: Access the regional energy management platform through the preset API interface to obtain the proportion of photovoltaic power generation and wind power generation in the total power generation; S1c: If the deviation of the clean energy proportion data obtained twice in a row exceeds 10%, the edge computing node is called to perform interpolation correction on the abnormal data, and the interpolation correction adopts linear interpolation method.

3. The method according to claim 1, characterized in that: In step S4, the value of the time period adjustment function f(T) is: ； Among them, peak and valley electricity periods are divided according to the regional power grid load curve.

4. The method according to claim 1, characterized in that: In step S4, the value of the battery health weight function g(SOH) is: 。 5. The method according to claim 1, characterized in that: In step S5, the blockchain account book adopts a shard storage structure, including: Main chain: stores summary information of digital asset issuance hash values ​​and user wallet addresses; Shard chain: divided by the geographical area to which the charging pile belongs, storing the charging pile unique identifier, digital asset details and power adjustment instructions; Among them, the regulatory node holds the main chain key and can query the complete data of the shard chain through the main chain.

6. The method according to claim 1, characterized in that It also includes anti-cheating verification steps: S6a: The camera of the charging pile is used to collect the image of the vehicle VIN code, and the VIN code is extracted using an optical character recognition algorithm; S6b: Compare the extracted VIN code with the vehicle information bound to the user account; S6c: If the same VIN code initiates a charging request at different charging piles within the preset 30 minutes, the issuance of digital assets will be frozen and the manual review process will be triggered.

7. The method according to claim 1, characterized in that: The maximum power upper limit allowed to be output by the charging pile under the current grid state is obtained by the following steps: S7a: Read device rated power from charger controller ; S7b: Access the power distribution monitoring system through the Modbus protocol to obtain the upper limit of the power that the current power grid can allocate to the charging pile; S7c: Compare charging station power ratings Distributable power to the grid , take the smaller value as the upper limit of charging power : 。 8. A system for implementing the method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module: The power monitoring unit, communication module and image acquisition device integrated in the charging pile are used to collect power grid data, user charging behavior data and vehicle VIN code images in real time; Edge computing nodes: deployed on the local server of the charging station to perform carbon emission reduction coefficient calculation, abnormal data verification and interpolation correction; Blockchain network: A consortium chain consisting of charging operator nodes, regulatory agency nodes, and partner merchant nodes, used to store digital asset values ​​and power regulation instructions; Power control module: dynamically adjusts the output power of the charging pile according to the power adjustment instructions issued by the blockchain network.

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