Block chain reliable data-based battery reuse method and related equipment

Abstract

The invention discloses a decommissioned battery pack multiplexing method and related equipment based on block chain reliable data, and the method comprises the steps: selecting a target weight in a preset multi-target optimization coefficient set according to a multiplexing scene corresponding to a decommissioned battery pack; obtaining credible historical data of the decommissioned battery pack and actual measurement data of the decommissioned battery pack, and verifying whether the credible historical data is reliable or not based on the actual measurement data; if yes, the comprehensive health degree of the decommissioned battery pack for the reuse scene is calculated based on the target weight and credible historical data; according to the comprehensive health degree and the characteristic data of the decommissioned battery packs, clustering and sorting the decommissioned battery packs through a DBSCAN algorithm, and determining sorting labels and cluster information of the decommissioned battery packs; and recombining the retired battery pack according to the cluster information to obtain a recombined power supply, and determining a management strategy for the recombined power supply according to the sorting label.

Description

Technical Field

[0001] This invention belongs to the field of power management technology, and specifically relates to a battery reuse method and related equipment based on reliable blockchain data. Background Technology

[0002] With the explosive growth of the new energy vehicle industry, the first batch of vehicle-mounted power batteries has entered its large-scale retirement period. Directly scrapping and dismantling them would result in a huge waste of resources and environmental pressure. At the same time, there is a huge demand for electrochemical energy storage in areas such as smoothing renewable energy power generation and peak shaving and valley filling of the power grid, but the cost is high. Using retired batteries in energy storage systems can significantly reduce energy storage costs and achieve a circular economy.

[0003] However, there are four major pain points in the cascade utilization of retired batteries: First, data is not transparent, and the lack of or unreliable historical performance data leads to inaccurate assessments and difficulty in lifespan prediction; second, consistency is poor, and traditional sorting methods are inefficient and ineffective, affecting the overall performance and safety of the system; third, economic efficiency is insufficient, and the lack of a reliable value assessment system makes it difficult to form a large-scale market; fourth, utilization is extensive, and the lack of a refined energy management strategy linked to the sorting results makes it impossible to maximize the residual value of the batteries.

[0004] Existing technologies often employ simple voltage and internal resistance sorting and crude reassembly, or focus only on physical disassembly, failing to deeply integrate reliable data from the entire battery lifecycle, multi-scenario evaluation models, and intelligent evaluation methods, making it difficult to achieve large-scale, highly reliable, and economically efficient reliable applications. Summary of the Invention

[0005] This invention provides a battery reuse method and related equipment based on reliable blockchain data, aiming to solve the problem of existing reliable application technologies that are difficult to achieve on a large scale, with high credibility and high cost-effectiveness.

[0006] To achieve the above objectives, the present invention proposes the following technical solution: A battery reuse method based on reliable blockchain data includes: Based on the reuse scenarios corresponding to the retired battery packs in the target batch, target weights are selected from a set of preset multi-objective optimization coefficients; The reliable historical data of the retired battery packs of the target batch on the blockchain traceability platform and the measured data obtained by sampling the retired battery packs of the target batch are obtained, and the reliability of the reliable historical data is verified based on the measured data. If the trusted historical data is reliable, then the overall health of the retired battery pack for the reuse scenario is calculated based on the target weight and the trusted historical data. If the credible historical data is unreliable, then obtain the batch test data of the retired battery pack, and calculate the comprehensive health of the retired battery pack for the reuse scenario based on the target weight and the batch test data; Based on the comprehensive health status and the characteristic data of the retired battery packs, the retired battery packs are clustered and sorted using the DBSCAN algorithm to determine the sorting labels and cluster information of the retired battery packs. The retired battery packs are reassembled according to the cluster information to obtain reassembled power supplies, and a management strategy for the reassembled power supplies is determined according to the sorting labels.

[0007] Optionally, before the step of selecting target weights from a preset multi-objective optimization coefficient set based on the reuse scenario corresponding to the target batch of retired battery packs, the method further includes: Using machine learning algorithms, with the goal of maximizing the correlation coefficient between overall health and key performance indicators under different scenarios, training and fitting are performed to obtain the weight coefficients of each key performance indicator under different scenarios. Among them, different scenarios include at least: environmental protection scenarios with battery remaining life and recycling value as key performance indicators, economic scenarios with current available capacity as key performance indicators, and performance scenarios with power output capability as key performance indicators.

[0008] Optionally, the step of verifying the reliability of the credible historical data based on the measured data includes: Calculate the correlation coefficient between the measured data and the reliable historical data, and determine whether the reliable historical data is reliable by comparing the correlation coefficient with the reliability threshold.

[0009] Optionally, the step of clustering and sorting the retired battery packs using the DBSCAN algorithm based on the comprehensive health status and feature data of the retired battery packs, and determining the sorting labels of the retired battery packs, includes: A four-dimensional feature vector is established based on the comprehensive health score and the feature data, and the four-dimensional feature vector is Z-score standardized to obtain standardized data. The feature data includes measured capacity, DC internal resistance and self-discharge rate. Calculate the Euclidean distance between each pair of retired battery packs, and determine the cluster information of retired battery packs using the k-distance curve method. When determining the k value, the Euclidean distance value corresponding to the inflection point of the curve is the Eps parameter. Based on the k value and the Eps parameter, the retired battery packs are clustered and sorted using the DBSCAN algorithm to determine the sorting labels for the retired battery packs.

[0010] Optionally, the step of determining the management strategy for the recombinant power supply based on the sorting label includes: For recombinant power supplies with sorting labels of low internal resistance / high power, a high-rate fast charging and discharging strategy is adopted, where the allowable charging and discharging current is higher than a first threshold and the SOC operating window is maintained in the first range. For recombinant power supplies with sorting labels of high capacity / energy, a deep charging and discharging strategy is adopted, where the allowable charging and discharging current is lower than a first threshold but higher than a second threshold and the SOC operating window is maintained in the second range, where the first range is within the second range. For recombinant power supplies with sorting labels of long life / environmentally friendly, a shallow charging and discharging long life strategy is matched, where the allowable charging and discharging current is lower than a second threshold and the SOC operating window is maintained in the third range, where the third range is within the first range.

[0011] Optionally, the step of reassembling the retired battery pack according to the cluster information to obtain a reassembled power source includes: Based on the cluster information, the retired battery packs are reassembled through a general electrical interface to obtain a reassembled power source.

[0012] Optionally, the step of calculating the comprehensive health of the retired battery pack for the reuse scenario based on the target weight and the reliable historical data includes: Based on the target weight and the reliable historical data, using a preset formula: SOH_fused=α*(S_cap / C_rated)+β*(1-(Cycle_hist / Cycle_max))-γ*(R_est-R_rated)-δ*SDR_est Calculate the overall health of the retired battery pack for the reuse scenario, where the overall health is the overall health score, α, β, γ, and δ are the target weights, S_cap is the current capacity in the reliable historical data, C_rated is the rated capacity, Cycle_hist is the historical cycle count in the reliable historical data, Cycle_max is the maximum theoretical cycle count, R_est is the estimated internal resistance in the reliable historical data, R_rated is the rated internal resistance, and SDR_est is the estimated self-discharge rate in the reliable historical data.

[0013] On the other hand, this application provides a battery reuse device based on reliable blockchain data, comprising: The weight selection module is used to select target weights from a preset multi-objective optimization coefficient set based on the reuse scenario corresponding to the retired battery packs in the target batch. The data verification module is used to obtain the credible historical data of the retired battery packs of the target batch on the blockchain traceability platform and the measured data obtained by sampling and measuring the retired battery packs of the target batch, and to verify the reliability of the credible historical data based on the measured data. The calculation module is used to calculate the overall health of the retired battery pack for the reuse scenario based on the target weight and the reliable historical data, if the reliable historical data is reliable. The calculation module is used to obtain batch measured data of retired battery packs if the trusted historical data is unreliable, and to calculate the comprehensive health of retired battery packs for the reuse scenario based on the target weight and the batch measured data. The sorting module is used to cluster and sort retired battery packs based on the comprehensive health status and feature data of retired battery packs using the DBSCAN algorithm, and to determine the sorting labels and cluster information of retired battery packs. The management module is used to reassemble retired battery packs according to the cluster information to obtain reassembled power supplies, and to determine the management strategy for the reassembled power supplies according to the sorting labels.

[0014] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the battery reuse method based on blockchain reliable data as described above.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the battery reuse method based on reliable blockchain data as described above.

[0016] The beneficial effects of this invention are mainly reflected in the following aspects: Trustworthiness, Transparency, and Economy: By establishing a trusted data chain through blockchain technology and adopting a "sampling calibration, batch evaluation" model, significant testing costs are saved, and the problems of data silos and trust are solved. Precision, Efficiency, and Intelligence: Based on a dynamic evaluation model with multi-objective optimization and weighting coefficients for different scenarios, DBSCAN clustering and sorting achieves accuracy and reliability far exceeding traditional methods, ensuring consistency of the reorganization system from the source. Safety and Long Lifespan: Employing whole-pack-level tiered utilization and differentiated branch management avoids complex dismantling and reduces safety hazards; matching optimal management strategies extends the overall lifespan of the system. Maximizing Value: Achieving a seamless transition from "intelligent sorting" to "intelligent management" allows battery clusters with different performance characteristics to operate under conditions best suited to their characteristics, achieving an optimal balance of system-level benefits. Closed-Loop Management and Assetization: Achieving a closed-loop data system covering the entire lifecycle from "production to vehicle use to tiered utilization to precise scheduling" provides a technological foundation for the digital management and financialization of battery assets. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a battery reuse method based on reliable blockchain data in an embodiment of the present invention; Figure 2 This is a flowchart of another battery reuse method based on reliable blockchain data in an embodiment of the present invention; Figure 3 This is a schematic diagram of the battery reuse device based on reliable blockchain data in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of this application. Detailed Implementation

[0019] It should be understood that the following embodiments are merely further elaborations on the present invention and not limitations thereof. Various modifications and variations can be made by those skilled in the art without departing from the spirit of the present invention, and all such modifications and variations should fall within the protection scope of the present invention.

[0020] The following is in conjunction with the appendix Figure 1 The following describes typical embodiments of the present invention and their specific steps.

[0021] As attached Figure 1 As shown, a battery reuse method based on reliable blockchain data includes: S110. Based on the reuse scenario corresponding to the retired battery packs in the target batch, select the target weight from the preset multi-objective optimization coefficient set; In one possible embodiment, before the step of selecting target weights from a preset multi-objective optimization coefficient set based on the reuse scenario corresponding to the target batch of retired battery packs, the method further includes: Using machine learning algorithms, with the goal of maximizing the correlation coefficient between overall health and key performance indicators under different scenarios, training and fitting are performed to obtain the weight coefficients of each key performance indicator under different scenarios. Among them, different scenarios include at least: environmental protection scenarios with battery remaining life and recycling value as key performance indicators, economic scenarios with current available capacity as key performance indicators, and performance scenarios with power output capability as key performance indicators.

[0022] For example, the economic coefficient set (for peak shaving and valley filling scenarios) is optimized to maximize the correlation between health and current available capacity. Its coefficients tend to reflect immediate energy storage capacity, with exemplary values ​​of [α=0.6, β=0.2, γ=0.1, δ=0.1] (capacity weight is highest). The performance coefficient set (for grid frequency regulation scenarios) is optimized to maximize the correlation between health and the reciprocal of DC internal resistance (i.e., conductance). Its coefficients tend to reflect power characteristics, with exemplary values ​​of [α=0.2, β=0.1, γ=0.6, δ=0.1] (internal resistance weight is highest). The environmentally friendly coefficient set (for long-term backup power and other scenarios) is optimized to maximize the correlation between health and predicted remaining cycle life. Its coefficients tend to reflect historical degradation and long-term durability, with exemplary values ​​of [α=0.3, β=0.5, γ=0.1, δ=0.1] (cycle history weight is highest). The preset multi-objective optimization coefficient set is stored in the system database or blockchain smart contract and can be called on demand during evaluation.

[0023] S120. Obtain the trusted historical data of the retired battery packs of the target batch on the blockchain traceability platform and the measured data obtained by sampling and measuring the retired battery packs of the target batch, and verify the reliability of the trusted historical data based on the measured data. In one possible embodiment, the step of verifying the reliability of the credible historical data based on the measured data includes: Calculate the correlation coefficient between the measured data and the reliable historical data, and determine whether the reliable historical data is reliable by comparing the correlation coefficient with the reliability threshold.

[0024] For example, the reliable historical data shows that the historical cycle count (Cycle_hist) is 1285, the last estimated capacity of the on-board BMS has decayed to 84.0 Ah, and the internal resistance has increased to 0.88 mΩ. The measured data shows that the measured capacity (C_measured) is 83.5 Ah, the DC internal resistance (R_internal) is 0.89 mΩ, and the self-discharge rate (SDR) is 1.8%. Correlation verification: The correlation coefficient between the last estimated on-chain capacity and the measured offline capacity of this batch of sample packs is calculated (e.g., 0.95), and the correlation coefficient between the last estimated on-chain internal resistance and the measured offline internal resistance is calculated (e.g., 0.90). If the correlation coefficients of all key indicators are greater than 0.9, the on-chain data quality of this batch of battery packs is considered highly reliable and can be used to represent its current performance status. This verification passed.

[0025] S130. If the trusted historical data is reliable, calculate the overall health of the retired battery pack for the reuse scenario based on the target weight and the trusted historical data; if the trusted historical data is unreliable, obtain the batch test data of the retired battery pack, and calculate the overall health of the retired battery pack for the reuse scenario based on the target weight and the batch test data. In one possible embodiment, the step of calculating the comprehensive health of the retired battery pack for the reuse scenario based on the target weight and the reliable historical data includes: Based on the target weight and the reliable historical data, using a preset formula: SOH_fused=α*(S_cap / C_rated)+β*(1-(Cycle_hist / Cycle_max))-γ*(R_est-R_rated)-δ*SDR_est Calculate the overall health of the retired battery pack for the reuse scenario, where the overall health is the overall health score, α, β, γ, and δ are the target weights, S_cap is the current capacity in the reliable historical data, C_rated is the rated capacity, Cycle_hist is the historical cycle count in the reliable historical data, Cycle_max is the maximum theoretical cycle count, R_est is the estimated internal resistance in the reliable historical data, R_rated is the rated internal resistance, and SDR_est is the estimated self-discharge rate in the reliable historical data.

[0026] For example, such as Figure 2 As shown, offline tests are conducted using standard charging and discharging equipment and an internal resistance tester to obtain batch test data of retired battery packs. When the reliable historical data is unreliable, batch test data of retired battery packs are obtained, and the comprehensive health of retired battery packs for the reuse scenario is calculated based on the target weight and the batch test data.

[0027] S140. Based on the comprehensive health status and the characteristic data of the retired battery pack, the retired battery pack is clustered and sorted using the DBSCAN algorithm to determine the sorting label and cluster information of the retired battery pack. In one possible embodiment, the step of clustering and sorting the retired battery packs using the DBSCAN algorithm based on the comprehensive health status and feature data of the retired battery packs, and determining the sorting labels of the retired battery packs, includes: A four-dimensional feature vector is established based on the comprehensive health score and the feature data, and the four-dimensional feature vector is Z-score standardized to obtain standardized data. The feature data includes measured capacity, DC internal resistance and self-discharge rate. Calculate the Euclidean distance between each pair of retired battery packs, and determine the cluster information of retired battery packs using the k-distance curve method. When determining the k value, the Euclidean distance value corresponding to the inflection point of the curve is the Eps parameter. Based on the k value and the Eps parameter, the retired battery packs are clustered and sorted using the DBSCAN algorithm to determine the sorting labels for the retired battery packs.

[0028] For example, a comprehensive health calculation is performed on 300 battery packs, and each battery pack obtains a four-dimensional feature vector containing comprehensive health (SOH_fused), measured capacity (C_measured), DC internal resistance (R_internal), and self-discharge rate (SDR).

[0029] First, the data is standardized using Z-score to eliminate the influence of dimensions. The formula is: z=(x-μ) / σ, where x is the original data, μ is the sample mean, and σ is the sample standard deviation.

[0030] Then, the Euclidean distance between each pair of data points in all battery packs is calculated using the following formula: Distance(A,B)=√[(SOH_fused_A-SOH_fused_B)²+(C_measured_A-C_measured_B)²+(R_internal_A-R_internal_B)²+(SDR_A-SDR_B)²] Using the k-distance curve method, when k is set to (MinPts=5), the distance value corresponding to the inflection point of the curve can be determined as the Eps parameter. For the sample data in this embodiment, the calculated Eps value is 0.75. Based on this parameter, the DBSCAN algorithm can effectively sort battery packs into clusters such as high-performance (cluster A), average-performance (cluster B), and abnormal-performance (noise points). Battery pack B-001 is assigned to cluster B. Subsequently, the DBSCAN algorithm (Eps=0.75, MinPts=5) is used for clustering and sorting, and the results are as follows: Cluster A (High-performance cluster): 100 packets, average estimated capacity 88Ah.

[0031] Cluster B (Medium Performance Cluster): 165 packets, average estimated capacity 82Ah. Packet B-001 is assigned to this cluster.

[0032] Noise points: 35 packages, which should be scrapped.

[0033] The sorted battery packs are reassembled in parallel as a whole into two independent branches, and the system capacity is calculated: Cluster A branch (100 packets): Total available capacity = 100 packs × 88Ah / pack = 8,800Ah.

[0034] Total available energy = 51.2V × 8,800Ah / 1000 = 450.56kWh.

[0035] Cluster B branch (165 packets): Total available capacity = 165 packs × 82Ah / pack = 13,530Ah.

[0036] Total available energy = 51.2V × 13,530Ah / 1000 = 692.74kWh.

[0037] Total system energy: 450.56 + 692.74 = 1,143.3 kWh (approximately 1.14 MWh). The system's rated power can be configured from 250 to 500 kW, meeting the energy storage requirements of a 1 MW photovoltaic power station.

[0038] S150. The retired battery pack is reassembled according to the cluster information to obtain a reassembled power source, and a management strategy for the reassembled power source is determined according to the sorting label.

[0039] In one possible embodiment, the step of reassembling the retired battery pack according to the cluster information to obtain a reassembled power source includes: Based on the cluster information, the retired battery packs are reassembled through a general electrical interface to obtain a reassembled power source.

[0040] For example, battery packs in the same cluster can be reassembled into independent reconfigurable power sources (energy storage branches) via a universal electrical interface without disassembly.

[0041] In one possible embodiment, the step of determining the management strategy for the recombined power supply based on the sorting label includes: For recombinant power supplies with sorting labels of low internal resistance / high power, a high-rate fast charging and discharging strategy is adopted, where the allowable charging and discharging current is higher than a first threshold and the SOC operating window is maintained in the first range. For recombinant power supplies with sorting labels of high capacity / energy, a deep charging and discharging strategy is adopted, where the allowable charging and discharging current is lower than a first threshold but higher than a second threshold and the SOC operating window is maintained in the second range, where the first range is within the second range. For recombinant power supplies with sorting labels of long life / environmentally friendly, a shallow charging and discharging long life strategy is matched, where the allowable charging and discharging current is lower than a second threshold and the SOC operating window is maintained in the third range, where the third range is within the first range.

[0042] For example, the scheduling strategy configuration is as follows: BMS Configuration: Independent BMS slave control units are configured for each of the two branches. The master BMS uses an inductive active balancing circuit, and the strategy is set independently based on the average performance parameters of the battery packs in each branch and the specific scenario they face, rather than the rated parameters of the batteries. EMS Strategy: The EMS receives power data from the photovoltaic inverter and performs smooth control. It can intelligently allocate charging and discharging tasks according to the real-time performance status of the two branches, prioritizing the use of the higher-performing cluster A branch for high-power response, while the cluster B branch is used to provide sustained energy support.

[0043] After the reorganization is completed, the system generates new digital identities for the two branches and stores them on the blockchain, while also assigning performance tags: In this example, for the economical scenario: Cluster A branch is labeled as high-capacity / energy type. The BMS automatically applies a deep charge / discharge strategy: setting the charge / discharge current to 0.3C and the SOC operating window to 20%-90%. Cluster B branch is labeled as medium-capacity / energy type. The BMS applies a strategy for it: setting the charge / discharge current to 0.25C and the SOC operating window to 25%-85%.

[0044] For performance-oriented scenarios, the process is similar to that in this embodiment, except that a performance-oriented coefficient set (α=0.2, γ=0.6) needs to be called to calculate and sort health status, ultimately selecting low internal resistance clusters. The BMS will then match a high-rate fast charging and discharging strategy for these clusters, such as setting the charging and discharging current to 1C and the SOC working window to 50%-80%, to meet the grid frequency regulation requirements for rapid response. For environmentally friendly scenarios, an environmentally friendly coefficient set (α=0.3, β=0.5) will be called to select long-life clusters, and a shallow charging and discharging long-life strategy will be matched.

[0045] The Energy Management System (EMS) intelligently allocates charging and discharging tasks based on the characteristics of each branch. The BMS periodically uploads operational data to the blockchain, forming a closed loop.

[0046] For example, for branches with sorting labels of low internal resistance / high power, a high-rate fast charging and discharging strategy is matched, and a higher allowable charging and discharging current value is set, with the SOC operating window maintained at 50%-80%; for branches with sorting labels of high capacity / energy, a deep charging and discharging strategy is matched, and a moderate charging and discharging current is set, with the SOC operating window set at 20%-90%; for branches with sorting labels of long life / environmentally friendly, a shallow charging and discharging long life strategy is matched, and a smaller charging and discharging current is set, with the SOC operating window set at 60%-70%.

[0047] On the other hand, such as Figure 3 As shown, this application provides a battery reuse device based on reliable blockchain data, comprising: The weight selection module 201 is used to select target weights from a preset multi-objective optimization coefficient set based on the reuse scenario corresponding to the retired battery packs of the target batch. The data verification module 202 is used to obtain the credible historical data of the retired battery packs of the target batch on the blockchain traceability platform and the measured data obtained by sampling and measuring the retired battery packs of the target batch, and to verify the reliability of the credible historical data based on the measured data. The calculation module 203 is used to calculate the comprehensive health of the retired battery pack for the reuse scenario based on the target weight and the reliable historical data if the reliable historical data is reliable. The calculation module 203 is also used to obtain batch test data of retired battery packs if the trusted historical data is unreliable, and calculate the comprehensive health of retired battery packs for the reuse scenario based on the target weight and the batch test data. The sorting module 204 is used to perform clustering and sorting of retired battery packs based on the comprehensive health status and feature data of retired battery packs using the DBSCAN algorithm, and to determine the sorting label and cluster information of retired battery packs. The management module 205 is used to reassemble retired battery packs according to the cluster information to obtain reassembled power supplies, and to determine a management strategy for the reassembled power supplies according to the sorting labels.

[0048] In one possible implementation, such as Figure 4 As shown, this application embodiment provides a terminal device 300, including: a memory 310, a processor 320, and a first computer program 311 stored on the memory 310 and executable on the processor 320. When the processor 320 executes the first computer program 311, it performs the above-mentioned selection of target weights from a preset multi-objective optimization coefficient set according to the reuse scenario corresponding to the target batch of retired battery packs; obtains trusted historical data of the target batch of retired battery packs on a blockchain traceability platform and measured data obtained by sampling and measuring the target batch of retired battery packs, and verifies the reliability of the trusted historical data based on the measured data; if the trusted historical data... If the historical data is reliable, the overall health of the retired battery pack for the reuse scenario is calculated based on the target weight and the reliable historical data. If the reliable historical data is unreliable, batch test data of the retired battery pack is obtained, and the overall health of the retired battery pack for the reuse scenario is calculated based on the target weight and the batch test data. Based on the overall health and the feature data of the retired battery pack, the retired battery pack is clustered and sorted using the DBSCAN algorithm to determine the sorting label and cluster information of the retired battery pack. Based on the cluster information, the retired battery pack is recombined to obtain a recombined power supply, and a management strategy for the recombined power supply is determined according to the sorting label.

[0049] In one possible implementation, such as Figure 5 As shown, this application embodiment provides a computer-readable storage medium 400, on which a second computer program 411 is stored. When the second computer program 411 is executed by a processor, it selects target weights from a preset multi-objective optimization coefficient set according to the reuse scenario corresponding to the target batch of retired battery packs; acquires trusted historical data of the target batch of retired battery packs on a blockchain traceability platform and measured data obtained by sampling and measuring the target batch of retired battery packs, and verifies the reliability of the trusted historical data based on the measured data; if the trusted historical data is reliable, it then verifies the reliability of the trusted historical data based on the target weights and the trusted historical data. The process involves: calculating the overall health of retired battery packs for the reuse scenario based on historical data; if the reliable historical data is unreliable, obtaining batch measured data of retired battery packs and calculating the overall health of retired battery packs for the reuse scenario based on the target weight and the batch measured data; clustering and sorting retired battery packs using the DBSCAN algorithm based on the overall health and feature data of retired battery packs to determine the sorting labels and cluster information of retired battery packs; reorganizing retired battery packs according to the cluster information to obtain reorganized power supplies, and determining the management strategy for the reorganized power supplies according to the sorting labels.

[0050] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0051] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0052] If the integrated unit is implemented as 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, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0053] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0054] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0055] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0056] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0057] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A battery reuse method based on blockchain reliable data, characterized in that, The method comprises the following steps: According to the reuse scenario corresponding to the retired battery pack of the target batch, select the target weight from the preset multi-objective optimization coefficient set; Obtain the reliable historical data of the retired battery pack of the target batch on the blockchain traceability platform and the measured data obtained by sampling and measuring the retired battery pack of the target batch, and verify whether the reliable historical data is reliable based on the measured data; If the reliable historical data is reliable, calculate the comprehensive health degree of the retired battery pack for the reuse scenario based on the target weight and the reliable historical data; If the reliable historical data is not reliable, obtain batch measured data of the retired battery pack, and calculate the comprehensive health degree of the retired battery pack for the reuse scenario based on the target weight and the batch measured data; According to the comprehensive health degree and the characteristic data of the retired battery pack, the DBSCAN algorithm is used to cluster and sort the retired battery pack, and the sorting label and cluster information of the retired battery pack are determined; According to the cluster information, the retired battery pack is reorganized to obtain a reorganized power supply, and the management strategy for the reorganized power supply is determined according to the sorting label. 2.The battery reuse method based on blockchain reliable data according to claim 1, wherein, Before the step of selecting the target weight from the preset multi-objective optimization coefficient set according to the reuse scenario corresponding to the retired battery pack of the target batch, the method further comprises the following steps: Through a machine learning algorithm, the weight coefficients of each key performance indicator in different scenarios are obtained by training and fitting with the goal of maximizing the correlation coefficient of the comprehensive health degree and the key performance indicators in different scenarios, wherein the different scenarios at least include: an environmental protection scenario taking battery remaining life and recycling value as key performance indicators, an economic scenario taking current available capacity as a key performance indicator, and a performance scenario taking power output capability as a key performance indicator. 3.The battery repurposing method based on blockchain reliable data of claim 1, wherein, The step of verifying whether the reliable historical data is reliable based on the measured data comprises the following steps: Calculate the correlation coefficient of the measured data and the reliable historical data, and determine whether the reliable historical data is reliable by comparing the correlation coefficient with the reliability threshold. 4.The battery repurposing method based on blockchain reliable data of claim 1, wherein, The step of clustering and sorting the retired battery pack according to the comprehensive health degree and the characteristic data of the retired battery pack by the DBSCAN algorithm to determine the sorting label and cluster information of the retired battery pack comprises the following steps: A four-dimensional feature vector is established according to the comprehensive health degree and the characteristic data, and the four-dimensional feature vector is subjected to Z-score standardization processing to obtain standardized data, wherein the characteristic data includes measured capacity, direct current internal resistance and self-discharge rate; The Euclidean distance between each pair of retired battery packs is calculated, and the cluster information of the retired battery packs is determined by the k-distance curve method, and when the k value is determined, the Euclidean distance value corresponding to the curve inflection point is the Eps parameter; According to the k value and the Eps parameter, the DBSCAN algorithm is used to cluster and sort the retired battery pack to determine the sorting label of the retired battery pack. 5.The battery reuse method based on blockchain reliable data according to claim 1, wherein, The step of determining the management strategy for the reorganized power supply according to the sorting label comprises the following steps: The recombined power supply is sorted into a low internal resistance / high power type, and a high-rate fast charging and fast discharging strategy is adopted, in which the allowed value of the charging and discharging current is higher than the first threshold value, and the SOC working window is maintained in a first interval; the recombined power supply is sorted into a high capacity / energy type, and a deep charging and discharging strategy is adopted, in which the allowed value of the charging and discharging current is lower than the first threshold value but higher than a second threshold value, and the SOC working window is maintained in a second interval, the first interval being within the second interval; the recombined power supply is sorted into a long life / eco-friendly type, and a shallow charging and discharging long life strategy is matched, in which the allowed value of the charging and discharging current is lower than the second threshold value, and the SOC working window is maintained in a third interval, the third interval being within the first interval. 6.The battery reuse method based on blockchain reliable data according to claim 1, wherein, The step of recombining the retired battery pack according to the cluster information to obtain the recombined power supply comprises: The retired battery pack is recombined through a universal electrical interface according to the cluster information to obtain the recombined power supply. 7.The battery repurposing method based on blockchain reliable data of claim 1, wherein, The step of calculating the comprehensive health degree of the retired battery pack for the reuse scenario based on the target weight and the reliable historical data comprises: The comprehensive health degree of the retired battery pack for the reuse scenario is calculated based on the target weight and the reliable historical data through a preset formula: SOH_fused=α*(S_cap / C_rated)+β*(1-(Cycle_hist / Cycle_max))-γ*(R_est-R_rated)-δ*SDR_est wherein the comprehensive health degree is a comprehensive health degree, α, β, γ, and δ are the target weight, S_cap is the current capacity in the reliable historical data, C_rated is the rated capacity, Cycle_hist is the historical cycle number in the reliable historical data, Cycle_max is the maximum theoretical cycle number, R_est is the estimated internal resistance in the reliable historical data, R_rated is the rated internal resistance, and SDR_est is the estimated self-discharge rate in the reliable historical data. 8.A battery reuse device based on blockchain reliable data, characterized in that, Comprise: The weight selection module is configured to select a target weight from a preset multi-target optimization coefficient set according to a reuse scenario corresponding to the retired battery pack of the target batch; The data verification module is configured to obtain reliable historical data of the retired battery pack of the target batch on a blockchain traceability platform and measured data obtained by sampling and measuring the retired battery pack of the target batch, and verify whether the reliable historical data is reliable based on the measured data; The calculation module is configured to, if the reliable historical data is reliable, calculate a comprehensive health degree of the retired battery pack for the reuse scenario based on the target weight and the reliable historical data; The calculation module is configured to, if the reliable historical data is unreliable, obtain batch measured data of the retired battery pack, and calculate a comprehensive health degree of the retired battery pack for the reuse scenario based on the target weight and the batch measured data; The sorting module is configured to cluster and sort the retired battery pack according to the comprehensive health degree and characteristic data of the retired battery pack by using a DBSCAN algorithm, and determine a sorting label and cluster information of the retired battery pack. A management module is configured to reorganize the retired battery packs according to the cluster information to obtain reorganized power supplies, and determine a management strategy for the reorganized power supplies according to the sorting labels.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The computer program is executed by the processor to implement the battery reuse method based on the reliable data of the block chain as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the battery reuse method based on the reliable data of the block chain as claimed in any one of claims 1 to 7.

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