A Calculation Method and System for Charging Pile Digital Assets Based on Dynamic Carbon Sensing

Abstract

The present invention discloses a calculation method for digital assets of charging piles based on dynamic carbon perception, comprising the following steps: S1: Real-time obtain the clean energy proportion data of the power grid in the area where the charging pile is located through the communication module built in the charging pile; S2: Calculate the carbon emission reduction coefficient according to the clean energy proportion data; S3: Collect user charging behavior data, including the charging power and charging time period, and obtain the ratio of the current capacity to the rated capacity of the vehicle battery through the in-vehicle terminal; S4: Generate a digital asset value based on the dynamic digital asset calculation model; S5: Write the digital asset value R into the blockchain ledger and simultaneously send a power adjustment instruction to the charging pile control system. This method combines the real-time carbon emission data of the power grid, the user charging behavior characteristics and the charging pile power control technology, and realizes the collaborative optimization of digital asset issuance and power grid load through the blockchain, and is mainly applied to the intelligent charging pile operation management system.

Description

Technical Field

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

[0002] With the rapid development of new energy vehicles, the importance of charging piles as important energy supply infrastructure has become increasingly prominent. How to quantify the carbon emission reduction benefits generated by charging behaviors and convert them into traceable digital assets has become the focus of current technical research. In the prior art, carbon emission reduction calculation and digital asset management solutions related to charging piles mainly include the following types:

[0003] (1) Carbon emission reduction calculation based on fixed coefficients: Some solutions use preset static carbon emission coefficients to estimate the emission reduction amount during the charging process. For example, the carbon emission reduction value is calculated through the regional average power generation structure. For example, Patent CN117648518A proposes a method and system for calculating the carbon emission reduction amount of charging piles. The various emission factors for calculating carbon emissions are preferentially obtained through a carbon emission factor database, which can be dynamically updated and obtained according to the priority. Such methods do not consider the real-time fluctuations in the proportion of clean energy in the power grid, resulting in a deviation between the calculation result and the actual emission reduction benefit.

[0004] (2) Binding digital assets to charging behaviors: In the prior art, digital asset issuance is usually directly associated with the charging power. For example, Patent CN114997631A discloses an electric vehicle charging scheduling method, device, equipment, and medium. The method determines the real-time incentive points corresponding to the electric vehicle in the current scheduling based on the power grid load data, the charging price corresponding to the current scheduling in the time-of-use charging price table for electric vehicles, the electric vehicle status data, and the charging station status data, and determines the ratio of the charging cost saved by the electric vehicle participating in the current scheduling according to the real-time incentive points. However, such methods do not consider the impact of the state of health (SOH) of the battery on the charging efficiency, resulting in high-loss batteries still being able to obtain the same digital assets as healthy batteries, causing unreasonable resource allocation.

[0005] (3) Charging power control technology: Existing charging pile power adjustment schemes are mostly set based on the rated power of the equipment or user requirements. Such methods lack coordination with the real-time load of the power grid, and may exacerbate the contradiction between supply and demand during high-load periods of the power grid, or fail to make full use of the surplus power during periods when clean energy is sufficient.

[0006] (4)Application of Blockchain in Charging Piles: Existing blockchain technologies are mostly used to record charging transaction data. For example, Patent CN 114218679A, a method and system for calculating vehicle-grid interaction carbon emission reduction based on blockchain, uses a single chain structure to store charging records and calculate carbon emission reduction. However, with the generation of a large amount of charging data, a single blockchain faces problems such as low storage efficiency and poor data penetration of regulatory nodes, making it difficult to meet the needs of multi-agent collaborative management.

[0007] Based on the existing technologies, the following technical defects mainly exist:

[0008] 1. Insufficient accuracy in carbon emission reduction calculation: 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;

[0009] 2. Unreasonable digital asset distribution mechanism: Weight correction is not combined with the battery health status, and high-loss batteries obtain excessive benefits, exacerbating resource waste;

[0010] 3. Disconnection between power control and the power grid: The adjustment of charging power does not consider the real-time load capacity of the power grid, easily causing local power grid overload or abandonment of clean energy;

[0011] 4. Low data storage and supervision efficiency: The blockchain structure design is single, and the regulatory agency cannot efficiently verify shard data, posing a risk of data tampering;

[0012] 5. Weak anti-cheating measures: Existing solutions rely on manual verification of vehicle identities, making it difficult to intercept fraud behaviors such as cross-pile repeated charging in real time. Summary of the Invention

[0013] To address the above problems, the present invention proposes a method and system for calculating digital assets of charging piles based on dynamic carbon perception and multi-dimensional data fusion. By real-time collecting data on the proportion of clean energy in the power grid, battery health status, and user charging behavior, and combining blockchain shard storage and power collaborative control, it realizes the collaborative optimization of digital asset issuance and power grid load, and improves the clean energy consumption rate.

[0014] To achieve the above objectives, the present invention adopts the following technical solutions:

[0015] A method for calculating digital assets of charging piles based on dynamic carbon perception, comprising the following steps:

[0016] S1: Through the communication module built in the charging pile, real-time obtain the data on the proportion of clean energy in the power grid where the charging pile is located;

[0017] S2: Calculate the carbon emission reduction coefficient according to the clean energy proportion data, and the carbon emission reduction coefficient satisfies the formula:

[0018]

[0019] Among them, \(K_c\) is the carbon emission reduction coefficient, and \(\alpha\) is an adjustment factor dynamically adjusted based on the regional carbon emission policy. is the clean energy power generation in the current power grid. is the total power generation of the power grid.

[0020] S3: Collect user charging behavior data, including the charging power \(E\) and the charging period \(T\), and obtain the state of health (SOH) of the vehicle battery through the on-vehicle terminal, where SOH is the ratio of the current capacity of the battery to the rated capacity.

[0021] S4: Generate a digital asset value \(R\) based on the dynamic digital asset calculation model, and the model is:

[0022]

[0023] Among them, \(\beta\) is a preset digital asset benchmark coefficient, and \(f(T)\) is a period adjustment function dynamically adjusted according to the charging period \(T\).

[0024] , and \(g(SOH)\) is a weight function based on the state of health (SOH) of the battery.

[0025] S5: Write the digital asset value \(R\) into the blockchain ledger, and simultaneously send a power adjustment instruction to the charging pile control system to make the charging power \(P\) satisfy:

[0026]

[0027] Among them, is the rated power of the charging pile. is the upper limit of the power that the power grid can allocate to this charging pile in real time, so as to realize the collaborative optimization of the charging power and the power grid load.

[0028] Furthermore, in order to obtain more reliable clean energy ratio data, the acquisition of the clean energy ratio data in step S1 includes the following sub-steps:

[0029] S1a: Read the real-time power generation side data of the power grid through the smart meter.

[0030] 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.

[0031] S1c: If the deviation between the clean energy ratio data obtained twice in succession exceeds 10%, then call the edge computing node to interpolate and correct the abnormal data, and the interpolation correction uses the linear interpolation method.

[0032] Furthermore, in step S4, the value of the period adjustment function \(f(T)\) is:

[0033]

[0034] Among them, the peak-valley electricity period is divided according to the regional power grid load curve.

[0035] Furthermore, in the step S4, the value of the battery health weight function g(SOH) is:

[0036]

[0037] Further, in order to ensure data security and supervision efficiency, in the step S5, the blockchain ledger adopts a sharded storage structure, including:

[0038] Main chain: Store the hash value of digital asset issuance and the summary information of user wallet addresses;

[0039] Sharded chain: Divided according to the geographical area where the charging pile belongs, store the unique identifier of the charging pile, digital asset details, and power adjustment instructions;

[0040] Among them, the supervision node holds the main chain key and can query the complete data of the sharded chain through the main chain penetration.

[0041] Further, the method further includes an anti-cheating verification step:

[0042] S6a: Collect the vehicle VIN code image through the camera of the charging pile, and extract the VIN code using the optical character recognition algorithm;

[0043] S6b: Compare the extracted VIN code with the vehicle information bound to the user account;

[0044] S6c: If the same VIN code initiates a charging request at different charging piles within a preset 30 minutes, freeze the digital asset issuance and trigger an artificial review process.

[0045] Further, in order to ensure that the charging power is always within the safe range of the power grid and equipment, the maximum charging power upper limit allowed by the charging pile under the current power grid state is obtained through the following steps:

[0046] S7a: Read the rated power of the equipment from the charging pile controller ;

[0047] S7b: Access the distribution monitoring system through the Modbus protocol to obtain the power upper limit that can be allocated to the charging pile by the current power grid;

[0048] S7c: Compare the rated power of the charging pile with the power that can be allocated by the power grid , and take the smaller value as the charging power upper limit :

[0049]

[0050] The present invention also provides a system for the above method, including:

[0051] Data acquisition module: integrated with the power monitoring unit, communication module and image acquisition device of the charging pile, for real-time acquisition of grid data, user charging behavior data and vehicle VIN code images;

[0052] Edge computing node: deployed on the local server of the charging station, for performing carbon emission reduction coefficient calculation, abnormal data verification and interpolation correction;

[0053] Blockchain network: a consortium chain composed of charging operator nodes, regulatory agency nodes and cooperative merchant nodes, for storing digital asset values and power adjustment instructions;

[0054] Power control module: dynamically adjusts the output power of the charging pile according to the power adjustment instructions issued by the blockchain network.

[0055] Due to the adoption of the above solution, the beneficial effects of the present invention are as follows:

[0056] 1. Dynamically correct the carbon emission reduction coefficient to improve the matching degree between digital assets and real emission reduction benefits;

[0057] 2. Introduce a battery health weight function to optimize the fairness of digital asset allocation;

[0058] 3. Improve the clean energy consumption rate through real-time coordination of power instructions and grid load;

[0059] 4. Build a blockchain sharding architecture that can penetrate supervision to ensure data security and supervision efficiency;

[0060] 5. Integrate optical character recognition (OCR) and VIN code verification technology to strengthen the anti-cheating ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flowchart of the method for calculating digital assets of a charging pile based on dynamic carbon perception according to the present invention;

[0062] Figure 2 It is a flowchart of obtaining clean energy ratio data in the method for calculating digital assets of a charging pile based on dynamic carbon perception according to the present invention;

[0063] Figure 3 It is a flowchart of anti-cheating verification in the method for calculating digital assets of a charging pile based on dynamic carbon perception according to the present invention;

[0064] Figure 4 It is the acquisition process of the charging power upper limit in the method for calculating digital assets of a charging pile based on dynamic carbon perception according to the present invention;

[0065] Figure 5 This is the schematic diagram of the application of the charging pile digital asset calculation method based on dynamic carbon perception in the intelligent charging pile operation management system. Specific implementation manner

[0066] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and specific implementation manners. Obviously, the described implementation manners are part of the implementation manners of the present invention, rather than all of the implementation manners. All other implementation manners obtained by those of ordinary skill in the art based on the implementation manners in the present invention without creative work belong to the scope of protection of the present invention.

[0067] As Figure 1 shown, a charging pile digital asset calculation method based on dynamic carbon perception specifically includes the following steps:

[0068] Step S1, the charging pile is built-in with a 4G communication module (model: Quectel EC25) and an intelligent electricity meter (model: Siemens7KM2110). Through the RS485 interface of the intelligent electricity meter, the total power generation of the power grid in the area where the charging pile is located is obtained in real time and the thermal power generation , and the clean energy power generation is calculated . Among them, clean energy includes renewable energy such as hydropower, photovoltaic power, and wind power, and this data is transmitted to the edge computing node for preliminary processing and analysis. As Figure 2 shown, the real-time power generation side data of the power grid is read through the intelligent electricity meter built in the charging pile, and the area energy management platform is accessed through a preset API interface (interface protocol: RESTful) to obtain the minute-level updated 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 between the clean energy ratio data obtained in two adjacent times exceeds 10% (for example, the previous time is 30% and the current time suddenly changes to 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, and the linear interpolation method is used for the interpolation correction to ensure the accuracy and continuity of the clean energy ratio data.

[0069] Step S2, calculate the carbon emission reduction coefficient according to the clean energy ratio data obtained in step S1, and its calculation formula is:

[0070]

[0071] Among them, \(K_c\) is the carbon emission reduction coefficient, and \(\alpha\) is an adjustment factor dynamically adjusted based on regional carbon emission policies, which is dynamically set according to the "Carbon Emission Intensity Guidelines" issued by local governments (for example: \(\alpha = 1.2\) in Beijing, \(\alpha = 0.9\) in Qinghai Province). is the clean energy power generation in the current power grid, and is the total power generation of the power grid. For example: when = 50kw, = 200kw, and \(\alpha = 1.2\), then \(K_c = 1.2×(50÷200)=0.3\).

[0072] Step S3, collect user charging behavior data. This behavior data includes the charging power E of the new energy vehicle bound to the user account (real-time collected through the power metering module of the charging pile), the charging period T (based on Beijing time, divided into peak power periods: 10:00 - 15:00, 18:00 - 21:00; flat power periods: 7:00 - 10:00, 15:00 - 18:00, 21:00 - 23:00; valley power periods: 23:00 - 7:00). In addition, obtain the state of health SOH of the vehicle battery (BMS) through the in-vehicle terminal (CAN bus, baud rate: 500kbps), where SOH is the ratio of the current capacity of the battery to the rated capacity. The calculation formula is:

[0073]

[0074] Step S4, generate the digital asset value R based on the dynamic digital asset calculation model, and allocate the digital asset value R to the corresponding user asset account. This model comprehensively considers the carbon emission reduction coefficient, charging power, charging period, and battery health status to ensure fairness and reasonableness. This model is:

[0075]

[0076] Among them, \(\beta\) is a preset digital asset benchmark coefficient, and \(f(T)\) is a period adjustment function dynamically adjusted according to the charging period T

[0077] , and \(g(SOH)\) is a weight function based on the battery health status SOH;

[0078] The value of the period adjustment function \(f(T)\) is:

[0079]

[0080] The value of the battery health degree weight function \(g(SOH)\) is:

[0081]

[0082] Calculation example: When β = 0.5 (benchmark coefficient), E = 30 kWh, Kc = 0.3, f(T) = 1.2, g(SOH) = 1.0:

[0083] R = 0.5 × 0.3 × 30 × 1.2 × 1.0 = 5.4 (digital asset units)

[0084] Step S5: Write the above digital asset value R into the consortium blockchain (platform: Hyperledger Fabric) through a smart contract (written in Solidity language), generate a transaction hash value and store it in the shard chain (shard rule: divided by province); the user wallet address digest is generated using the SHA-256 algorithm. For example, for address 0x3FZb..., the digest 9f86d... is generated. Power control instruction: As Figure 4 shown, read the currently distributable power of the power grid from the distribution monitoring system (model: Schneider PowerLogic ION9000) = 100 kw, the rated power of the charging pile = 120 kw, take the smaller value = 100 kw, send a power adjustment instruction to the charging pile control system through the MQTT protocol to limit the charging power to P ≤ 100 kw.

[0085] Step S6: Anti-cheating verification. As Figure 3 shown, VIN code recognition: Take a picture of the VIN code image on the vehicle's front windshield through the charging pile camera (model: Hikvision DS-2CD1221), extract the VIN code through the Tesseract OCR engine, and compare it with the vehicle information bound to the user APP. Exception handling: If the same VIN code initiates a charging request at charging pile A and charging pile B simultaneously within 30 minutes, freeze the user's digital asset account and push an alarm message to the operation management platform (manual review and unlocking are required).

[0086] As Figure 5As shown in the figure, a charging pile digital asset allocation system based on dynamic carbon sensing. The data acquisition module 101 includes 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 angle 120°), which is used to collect grid data, user charging behavior data, and vehicle VIN code images in real time; the edge computing node 102 is deployed on the local server of the charging station (operating system: Ubuntu 20.04), running a carbon emission reduction coefficient calculation program (Python3.8), which is used to perform carbon emission reduction coefficient calculation, abnormal data verification, and interpolation correction. If the data deviation exceeds the threshold, an interpolation algorithm (Scipy library interp1d function) is called. The blockchain network 103, the main chain nodes are deployed by the national regulatory agency (server configuration: 64 cores / 256GB of memory); the shard chain nodes are divided by regions such as North China and East China, storing the charging pile ID and digital asset details; for regulatory penetration query, the regulatory agency accesses the shard chain data through the main chain key (query response time <1 second). The power control module 104, the core controller uses an STM32F407 microcontroller; the power regulation circuit uses an IGBT module (model: Infineon FF450R12KE4), supporting continuous adjustment from 0 to 120kW.

[0087] Due to the adoption of the above solution, the present invention realizes dynamic correction of the carbon emission reduction coefficient, improves the matching degree between digital assets and real emission reduction benefits, and automatically executes transactions through smart contracts, ensuring data transparency and credibility, motivating users to participate in emission reduction, promoting the popularization of green energy, forming a virtuous cycle, promoting sustainable development, and demonstrating the profound significance of technology enabling environmental protection. In addition, a battery health weight function is introduced to optimize the fairness of digital asset allocation; through the real-time coordination of power commands and grid loads, the clean energy consumption rate is improved; a blockchain sharding architecture with penetrable supervision is constructed to ensure data security and supervision efficiency; optical character recognition (OCR) and VIN code verification technologies are integrated to strengthen the anti-cheating ability.

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 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 a weight function based on the battery state of health 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 node: 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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