Blockchain-based electrolytic aluminum solid waste resource utilization product tracing method and system

Abstract

The application relates to the technical field of blockchains, and discloses an electrolytic aluminum solid waste resourceization product tracing method and system based on a blockchain. The method comprises the following steps: collecting real-time operation data of electrolytic aluminum solid waste in each link of the resourceization process, recording attribute change information of the electrolytic aluminum solid waste in each link, and generating an initial data chain with a phased identifier; verifying and enhancing the chain, using an encryption technology to form a tamper-proof sequence and storing the sequence in a blockchain; performing consistency checking on the sequence through a cross-node synchronization and consensus mechanism, generating a trusted data sequence, and analyzing system load based on the trusted data sequence to dynamically adjust a resource allocation scheme; extracting a trust degree index according to the resource allocation scheme, verifying the trust degree index through a distributed node to obtain a trust verification result, and finally updating a monitoring feedback mechanism based on the result, continuously monitoring data fluctuations, and generating an optimized monitoring record; and the application improves the data transparency and tracing reliability of the whole electrolytic aluminum solid waste resourceization process.

Description

Technical Field

[0001] This application relates to the field of blockchain technology, and in particular to a blockchain-based method and system for tracing the resource recovery of electrolytic aluminum solid waste. Background Technology

[0002] Solid waste generated during aluminum electrolysis, such as red mud and slag, is often highly polluting and difficult to treat. How to efficiently and safely utilize these wastes as resources is a significant technical challenge. With increasingly stringent environmental policies, the standardization and transparency of solid waste treatment have become urgent issues. In the process of waste metal recycling, accurate records are needed at every stage from generation to recycling to ensure reliable data tracking and prevent data loss or falsification. Currently, data management in many solid waste treatment processes relies on traditional centralized database systems. While these systems can meet basic data storage and management needs, they struggle to guarantee transparency and reliability when handling complex, multi-stage operations. Traditional data management methods are also susceptible to human tampering and system failures, leading to compromised data integrity and consistency.

[0003] Therefore, a new technological approach is urgently needed to solve this problem. Blockchain technology, with its decentralized, tamper-proof, and traceable characteristics, has become an ideal choice for improving the reliability and transparency of data in the solid waste resource utilization process. Through blockchain technology, real-time data collection, storage, and verification of the entire waste metal separation process can be achieved, while simultaneously optimizing resource allocation and improving system processing efficiency, thereby promoting the efficient resource utilization and environmentally friendly development of electrolytic aluminum solid waste. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a blockchain-based traceability method and system for electrolytic aluminum solid waste resource recovery products. This system aims to improve the data credibility, traceability integrity and transparency, system resource utilization efficiency, and trust-based intelligent monitoring and decision-making capabilities throughout the entire electrolytic aluminum solid waste resource recovery process.

[0005] Firstly, this application provides a blockchain-based method for tracing the resource recovery of electrolytic aluminum solid waste, the method comprising: Step S1: Collect real-time operation data of electrolytic aluminum solid waste at each stage of the resource utilization process, and record the attribute transformation information of the electrolytic aluminum solid waste at each stage to generate an initial data chain with stage identifiers. Step S2: Verify the integrity of the initial data chain and obtain the enhanced complete data chain. Encrypt the complete data chain using encryption technology to form a tamper-proof sequence and store it in the blockchain. Step S3: Perform consistency verification on the tamper-proof sequence through cross-node synchronization technology, confirm the reliability of each segment in the sequence using the node consensus mechanism, generate a trusted data sequence, analyze the system load of the trusted data sequence, and dynamically adjust the resource allocation scheme. Step S4: Extract trust indexes based on the resource allocation scheme, verify the credibility of the data through distributed nodes, obtain trust verification results, update the monitoring and feedback mechanism of the resource allocation process based on the trust verification results, continuously monitor data fluctuations, and generate optimized monitoring records.

[0006] Secondly, this application provides a blockchain-based traceability system for the resource recovery of electrolytic aluminum solid waste, the system comprising: The chain generation module is used to collect real-time operation data of electrolytic aluminum solid waste in each stage of the resource utilization process, and record the property transformation information of the electrolytic aluminum solid waste in each stage to generate an initial data chain with stage identifiers. The anti-tampering processing module is used to verify the integrity of the initial data chain, obtain the enhanced complete data chain, encrypt the complete data chain using encryption technology, form an anti-tampering sequence, and store it in the blockchain. The resource scheduling module is used to perform consistency verification on the tamper-proof sequence through cross-node synchronization technology, confirm the reliability of each segment in the sequence using the node consensus mechanism, generate a trusted data sequence, analyze the system load of the trusted data sequence, and dynamically adjust the resource allocation scheme. The monitoring and optimization module is used to extract trust indicators based on the resource allocation scheme, verify the credibility of the data through distributed nodes, obtain trust verification results, update the monitoring feedback mechanism of the resource allocation process based on the trust verification results, continuously monitor data fluctuations, and generate optimized monitoring records.

[0007] Compared with the prior art, the beneficial effects of the present invention are at least as follows: This application provides a blockchain-based traceability method and system for the resource recovery of electrolytic aluminum solid waste. First, it utilizes a sensor network to collect real-time operational data from the magnetic separation process. Then, it employs distributed ledger technology to generate an initial data chain from timestamped attribute transformation information, achieving high-fidelity, tamper-proof source recording of the production process and building a solid data foundation for reliable traceability throughout the entire process. Second, by performing integrity verification, node supplementation, conflict correction, and encrypted hash linking on the initial data chain, it not only ensures the integrity and internal consistency of the data chain but also enhances the reliability of traceability conclusions by forming and storing tamper-proof sequences, ensuring that no historical operation records can be altered afterward.

[0008] Furthermore, by generating a trusted data sequence through cross-node consensus verification and dynamically adjusting the resource allocation scheme based on this sequence in real time, the traceability system achieves intelligent operation and maintenance. This not only ensures the global consistency of data status in a distributed environment but also adaptively optimizes resources in high-load production scenarios, prioritizing the complete on-chain uploading of core data streams and improving the stability, efficiency, and resource utilization of the entire traceability system. Finally, by extracting and verifying node trust indicators associated with the resource allocation scheme and dynamically updating the monitoring and feedback mechanism accordingly, data trust assessment and process monitoring are deeply integrated. This enables differentiated monitoring strategies based on node trust status, rapid response to abnormal fluctuations, and traceable recording, achieving closed-loop intelligent management from data generation and verification to risk monitoring. This significantly improves the transparency, security, and adaptive regulatory capabilities of the process. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a blockchain-based traceability method for the resource recovery of electrolytic aluminum solid waste, as described in this application. Figure 2 This is a schematic diagram of the hierarchical structure of the node reputation assessment model in an embodiment of this application. Figure 3 This is a flowchart illustrating the consistency verification process in an embodiment of this application. Figure 4 This is a schematic diagram of a blockchain-based traceability system for the recycling of electrolytic aluminum solid waste, as described in an embodiment of this application. Detailed Implementation

[0011] This application provides a blockchain-based method and system for tracing the resource recovery of electrolytic aluminum solid waste. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0012] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of a blockchain-based traceability method for electrolytic aluminum solid waste resource recovery products includes: Step S1: Collect real-time operation data of electrolytic aluminum solid waste at each stage of the resource utilization process, and record the property transformation information of electrolytic aluminum solid waste at each stage to generate an initial data chain with stage identifiers.

[0013] In step S1, generating an initial data chain with stage identifiers includes: establishing a unique identifier for each batch of electrolytic aluminum solid waste; acquiring real-time operational data, including magnetic field strength, material flow rate, and metal separation efficiency, through a deployed sensor network for the magnetic separation stage in the resource recovery process of electrolytic aluminum solid waste; identifying and recording attribute transformation information corresponding to different process states in the magnetic separation stage based on the real-time operational data; and using distributed ledger technology to bind the attribute transformation information of each stage with a timestamp to form an initial data chain.

[0014] Specifically, in the resource recovery process of electrolytic aluminum solid waste, mainly referring to red mud, the magnetic separation stage is the core process for achieving efficient iron recovery and safe separation of waste residue. To achieve reliable traceability of the entire material property transformation process, the primary task is to digitally record the production operations of this stage with high fidelity. The core of this process is to generate a tamper-proof and verifiable interim data chain, providing the original data foundation for subsequent data integrity verification, consensus confirmation, and resource optimization. In practice, each independent batch of electrolytic aluminum solid waste is assigned a unique identifier at the beginning of the process. This identifier is typically generated by combining the batch number, material type code, and entry time hash value to ensure unique targeting for all subsequent operations on that batch of material in the distributed system. Among the multiple stages included in the resource recovery process, such as crushing and grinding, magnetic separation, roasting or sintering, leaching and washing, and neutralization and solidification / building materialization, the magnetic separation stage is the key stage for recovering valuable metals, mainly iron, and is also the focus of data collection and recording in this embodiment. To obtain real-time operational data for this stage, a network of multiple types of sensors is strategically deployed around the magnetic separator. This network includes a fluxmeter for monitoring magnetic field strength, a radar flowmeter installed on the conveyor belt to measure material flow rate, and an online component analyzer located at the discharge port to evaluate metal separation efficiency in real time. These sensors continuously transmit parameters such as magnetic field strength, material flow rate, and metal separation efficiency calculated in real time based on spectral analysis to the data acquisition server via industrial bus or wireless IoT protocol.

[0015] After obtaining the aforementioned real-time operational data, the system automatically identifies and records attribute transformation information corresponding to different process states in the magnetic separation process, based on a pre-set process knowledge base. For example, the material processing is decomposed into different process states such as "feeding and initial selection," "magnetic field adsorption separation," and "product separation and output." For the "feeding and initial selection" state, the recorded attribute transformation information may include the initial metal distribution of the material; for the "magnetic field adsorption separation" state, it records the adsorption rate and concentration changes of iron under the real-time magnetic field; and for the "product separation and output" state, it finally records the grade of the iron concentrate and the residual metal content of the tailings. This process essentially analyzes a continuous physicochemical reaction into a series of discrete attribute state change events with clear business meaning. To solidify these key transformation events into reliable records, distributed ledger technology is used to bind the attribute transformation information of each stage to a high-precision timestamp synchronously obtained from a reliable time source. Specifically, for each process state identified in the magnetic separation process, such as "before magnetic separation," "during magnetic separation," and "after magnetic separation," the corresponding attribute transformation information, such as the real-time iron grade in the "during magnetic separation" state, is combined with the current timestamp accurate to the millisecond level to form a structured data record unit. Subsequently, the interface of the underlying blockchain framework is called to submit the data record unit to the distributed ledger network. The network arranges and stores multiple data record units submitted in chronological order according to its preset data organization rules, thereby logically forming an initial data chain that extends in chronological order, with each record carrying its own timestamp. This chain clearly includes stage identifiers and fully records the attribute transformation process of this batch of electrolytic aluminum solid waste in the magnetic separation process, constituting the data origin for credible traceability of the entire product life cycle.

[0016] Step S2: Verify the integrity of the initial data chain and obtain the enhanced complete data chain. Use encryption technology to encrypt the complete data chain to form a tamper-proof sequence and store it in the blockchain.

[0017] In step S2, the integrity verification of the initial data chain includes: integrating the initial data chain into a set of records with stage identifiers according to the logical flow of the magnetic separation process; verifying the integrity of the record set by traversing the record set and checking whether the timestamp sequence and the stage identifiers are continuous; if the proportion of missing records exceeds a preset threshold, obtaining historical record information from the backup verification node through a node supplementation mechanism; integrating the historical record information with the existing data chain to form an enhanced complete data chain; performing data consistency verification on the complete data chain to determine whether there are record conflicts; if there are record conflicts, correcting them through the synchronized data of the backup verification node; defining the corrected data chain as the final complete data chain and storing it in the distributed ledger.

[0018] Specifically, after generating the initial data chain with stage identifiers, an automated data quality assurance process is immediately initiated to ensure its reliability as the core basis for credible traceability throughout the entire process. This process aims to perform in-depth verification and enhancement on the initial chain to eliminate data loss, time sequence disorder, or record contradictions that may be caused by intermittent equipment failures, network packet loss, or instantaneous system anomalies, thereby laying a solid and consistent foundation for building an immutable and authoritative data sequence. In practice, the data in the initial chain is first restructured according to the inherent physical and chemical logic of the magnetic separation process. Taking red mud batch "001" as an example, its magnetic separation logic flow requires the material to sequentially undergo three irreversible continuous stages: "pretreatment and feeding," "adsorption and separation of ferromagnetic substances under the action of a magnetic field," and "collection and output of magnetic and non-magnetic products." All data records in the initial data chain of this batch are extracted and categorized according to the state described in their content, under three clear stage markers: "before magnetic separation" (corresponding to pretreatment and feeding, such as recording the initial iron grade and moisture content), "during magnetic separation" (corresponding to the magnetic field adsorption process, such as recording the real-time iron recovery rate under a 0.8T magnetic field), and "after magnetic separation" (corresponding to the product output, such as recording the final iron concentrate grade of 92%). This forms a logically clear record set organized by process stage. This step transforms the original time-series data into a stage view that is more in line with the business analysis perspective, establishing a clear logical framework for subsequent integrity checks. Subsequently, integrity verification is performed on this record set. The process iterates through all records in the set, focusing on two key continuity checks: First, it checks whether the timestamps attached to each record are strictly increasing and without significant gaps, ensuring that there are no missing monitoring segments in the entire production process over time. Second, it checks whether the order of the stage markers conforms to the aforementioned process logic of "before magnetic separation," "in the process of magnetic separation," and "after magnetic separation," preventing record sequences that violate physical laws, such as production records appearing directly without undergoing the separation process. This continuity check is crucial for detecting data loss due to temporary sensor failures, data transmission interruptions, or other reasons. For example, the check might reveal that during the critical separation period of two minutes—from timestamp "2024-05-20, 10:05:00" to "10:07:00"—there are no "in the process of magnetic separation" status records, creating a significant monitoring gap.

[0019] If the inspection reveals a missing record ratio—for example, if the number of missing records exceeds a preset threshold (e.g., 5%) within a period where continuous recording is expected—a node replenishment mechanism is automatically triggered. This mechanism requests historical data from pre-configured backup verification nodes in the blockchain network that store copies of historical data, requesting historical information on key parameters such as magnetic field strength and material flow rate for the missing time period. For example, it might simultaneously request two minutes of missing data from backup node A in a local data center and node B in a remote disaster recovery center. After receiving this supplementary data, its digital signature is verified to ensure the information has not been tampered with during transmission and storage, and its source is trustworthy. A digital signature is a cryptographic technique based on asymmetric encryption algorithms. The data producer uses their private key to encrypt the data, generating a unique "signature." Any recipient holding the corresponding public key can verify this signature, thus confirming that the data was indeed sent by that party and has not been tampered with during transmission, ensuring the authenticity and trustworthiness of the data's source. Once verified, these historical records are precisely inserted into the corresponding gaps in the initial data chain based on the timestamps, thus forming an enhanced and complete data chain, effectively repairing the breakpoints in the data in terms of time and logic.

[0020] Next, a data consistency check is performed on this completed chain to detect and resolve potential record conflicts. This is achieved through technical means, such as calculating and verifying whether the cryptographic hash values ​​of adjacent data blocks in the chain are consecutive and match correctly. In a hash-based blockchain structure, any tampering with existing data will cause its hash value to change, thus breaking the link with subsequent blocks. If such a mismatch is found during the check, it indicates a record conflict, meaning that there may be inconsistent data descriptions on the chain for the same point in time or event. For example, for the time point "2024-05-20, 10:06:30", the instantaneous iron grade recorded on the main chain is 85%, but the data synchronized from a certain node shows 87%, the hash check fails, indicating a conflict at this point. For identified conflicts, the backup verification node network is used again. It retrieves synchronized data corresponding to the conflict time points from multiple reliable backup nodes. This data itself carries verifiable hash link relationships. A decision-making algorithm, such as a "majority voting" mechanism based on data versions from a majority of trusted nodes, is used to determine the correct data content to be adopted and to correct the conflict record in the local chain. Specifically, if three backup nodes are queried, and two nodes report an iron grade of 85% and one reports 87%, then 85% is adopted as the correct value to overwrite the conflict record. Through this correction process, a final complete data chain with consistent data content and continuous hash links is obtained globally. Subsequently, for this complete data chain, a cryptographic hash function is used to generate a unique hash for each data block, and the hash value of the previous block is embedded into the current block, forming an interlocking cryptographic chain. Simultaneously, key attribute proofs are embedded into the corresponding blocks. Finally, through the blockchain network consensus mechanism, this tamper-proof sequence is distributed and stored in the ledgers of each node, achieving data irreversibility and permanent traceability. The specific process is as follows: In step S2, forming an tamper-proof sequence and storing it in the blockchain includes: using encrypted hash linking technology to encrypt the enhanced complete data chain and generate an encrypted data chain; combining the distributed ledger storage mechanism, storing key information from the magnetic separation process in the encrypted data chain, embedding attribute proof information and timestamps to generate an tamper-proof sequence with irreversible recording; recording the hash value of the encryption process for the tamper-proof sequence and associating the hash value with the timestamp to form a traceable encrypted record.

[0021] Specifically, after obtaining a complete data chain that has undergone integrity verification and enhancement, tamper-proof solidification and blockchain storage operations are then performed. The core objective is to transform verified business data into authoritative records with cryptographic irreversibility. First, the complete data chain is encrypted using cryptographic hash chaining technology. Specifically, key data from the magnetic separation stage is extracted from the chain, such as the iron concentrate purity of "95%" and the corresponding material flow rate recorded at the timestamp "2023-10-01, 14:00:00". Then, a secure hash algorithm, such as SHA-256, is used to calculate a unique hash value for the first block containing the complete data chain, for example, a 256-bit hexadecimal string. The first block refers to the earliest data unit in the complete chain arranged chronologically, such as a data packet recording the "before magnetic separation" state data at the start of the batch, including initial iron content and the first timestamp. The hash value is first calculated for this initial data unit, serving as the anchor point for the entire encryption process. When generating the next data block, its content not only includes new business data, such as the purity "96%" at the next moment, but also uses the hash value calculated from the previous block as part of the input. This chain of units is logically transformed into a tightly interlocking, cryptographically linked "encrypted hash chain" of data. Any alteration to data in a historical block, no matter how minor, will cause a change in its hash value, triggering a chain reaction of hash value invalidation in all subsequent blocks, making the alteration instantly detectable.

[0022] While constructing the encrypted chain, the key information of the magnetic separation process is persisted by combining the storage mechanism of a distributed ledger. This includes not only distributing the encrypted data chain itself across multiple nodes in the network, such as production nodes at solid waste treatment plants, data nodes at the group headquarters, regulatory nodes at environmental protection departments, procurement nodes at downstream steel mills, and notary nodes at third-party institutions, but also ensuring that no single party can unilaterally control or tamper with the data, thus jointly building a transparent, mutually trusting traceability network with broad oversight capabilities. More importantly, attribute proof information and timestamps are embedded in the encryption process to generate an tamper-proof sequence with irreversible records. Specifically, external documents that can independently prove the authenticity of the business, such as purity certificates issued by third-party laboratories for this batch of iron concentrate, are hashed, and the resulting digest value is used as attribute proof and written into the corresponding "post-magnetic separation" data block in the chain. Simultaneously, the generation time of each block is synchronized with a trusted time source, such as the National Time Service Center's NTP server, to obtain a precise timestamp. By hashing and linking business data, attribute proof hashes, and authoritative timestamps together, a tamper-proof sequence is generated. This sequence not only ensures the immutability of the data itself but also establishes a strong cryptographic binding between process data, final quality proofs, and precise time. For long-term auditing and rapid verification, a final summary of the encryption process is recorded for the entire tamper-proof sequence: the overall hash value of the complete sequence, called the root hash or Merkle root. This root hash is then associated with a key timestamp identifying the batch processing cycle, such as the batch completion time, forming a "timestamp-root hash" key-value pair. This pair serves as the data fingerprint for the batch and is stored in the blockchain or an auxiliary index. This allows auditors to quickly locate and verify the authenticity and integrity of the complete data chain within a specific time period without traversing all the data, simply by using this associated record and the corresponding timestamp. This creates an efficient, reliable, and traceable encrypted record, providing ultimate cryptographic-level protection for product traceability.

[0023] Step S3: Perform consistency verification on the anti-tampering sequence through cross-node synchronization technology, use the node consensus mechanism to confirm the reliability of each segment in the sequence, generate a trusted data sequence, analyze the system load of the trusted data sequence, and dynamically adjust the resource allocation scheme.

[0024] In step S3, the anti-tampering sequence is validated for consistency through cross-node synchronization technology. This includes: broadcasting the anti-tampering sequence to multiple verification nodes in the blockchain network; each verification node independently calculates the Merkle root hash of the anti-tampering sequence and compares it with its own stored expected root hash to perform consistency verification; each verification node submits the comparison result and its own calculated root hash as a vote to the consensus protocol; the consensus protocol counts the collected votes according to a pre-set validity threshold; when the number of votes indicating that the anti-tampering sequence is valid and the root hash is consistent exceeds the validity threshold, the anti-tampering sequence is determined to have passed the inter-node consistency verification.

[0025] Specifically, after the tamper-proof sequence is generated and stored on the blockchain, to ensure that the sequence is unanimously recognized by all participants in the distributed network and becomes the authoritative basis for subsequent analysis and decision-making, a cross-node consistency verification and consensus confirmation process is initiated. Specifically, the tamper-proof sequence is broadcast to all verification nodes in the network, such as production nodes deployed at the solid waste treatment plant, data nodes at the group headquarters, regulatory nodes of the environmental protection department, procurement nodes of the downstream steel plant, and notary nodes of a third-party institution. Each node, upon receiving the sequence, independently performs verification calculations, such as... Figure 3The diagram shows the flowchart for consistency verification. First, each node constructs a Merkle tree based on all data records in the sequence, such as magnetic field strength and iron concentrate purity at different timestamps. Specifically, the construction process involves calculating the hash value of each data record as a leaf node, then pairing them up and calculating the hash value of their parent node, recursively generating a unique Merkle tree root hash representing the integrity of the entire dataset. Subsequently, each node compares this calculated root hash value with the expected root hash value derived from previous block history in its local ledger. For example, when processing red mud magnetic separation data in batch "001", the generated tamper-proof sequence is broadcast. The processing plant node calculates a Merkle root hash of "H1", and the monitoring platform node independently calculates "H1" as well. However, the purchasing node might calculate a different root hash "H2" due to errors in individual data packets during network transmission. Next, each verification node submits its comparison result ("consistent" or "inconsistent") and the calculated actual root hash value as a vote to the consensus protocol running in the network. This embodiment uses a practical Byzantine fault-tolerant algorithm, which sets a validity threshold requiring more than two-thirds of the total number of nodes; that is, at least four out of five nodes must reach consensus. In this example, the processing plant, group headquarters, environmental protection department, and third-party notary nodes all report "consistent" with a root hash of "H1," while the downstream steel mill purchasing node reports "inconsistent." Statistics show that four nodes report "valid" with a consistent root hash, exceeding the threshold requirement. Therefore, the consensus protocol determines that the tamper-proof sequence has passed the inter-node consistency check. For purchasing nodes that have not reached consensus, the network will trigger a state synchronization mechanism to automatically obtain and apply the correct data copy from the majority of nodes that have reached consensus, ensuring that all nodes ultimately hold a unified and correct tamper-proof sequence. The specific process will be explained later. This process, through Merkle tree summary comparison and Byzantine fault-tolerant consensus mechanism, efficiently and securely establishes the eventual consistency and authority of data on a global scale, laying the foundation for generating a globally recognized and trustworthy data sequence.

[0026] In step S3, generating a trusted data sequence includes: if the consensus protocol determines that no consensus has been reached, the data segment that caused the verification failure is located based on the voting difference and defined as a divergent data segment; for the divergent data segment, a trusted reference node is dynamically selected from the network based on the historical consensus records and real-time trust indicators of each node; standard synchronization data of the divergent data segment is obtained from the selected reference node, and the corresponding copy stored locally is updated using the standard synchronization data; the corrected data segment is re-verified for consistency; when all segments of the tamper-proof sequence pass the verification, the tamper-proof sequence that passes the verification is determined as a trusted data sequence and synchronized to all nodes in the network.

[0027] Specifically, if the consensus protocol determines that a valid consensus has not been reached—for example, only 3 nodes report valid data, failing to reach the threshold of 4—then it automatically enters the divergence handling and trusted sequence generation process. First, it analyzes the voting differences among all nodes to pinpoint the divergent data segment causing the verification failure. For example, through comparison, if, besides the procurement node, a group data node also generates different hashes for magnetic field strength data recorded within a specific time period, such as from "2023-10-01 14:05:00" to "14:05:30," then the data block corresponding to that time period is defined as a divergent data segment. Subsequently, a pre-trained node reputation evaluation model is invoked to dynamically select a reference node, such as... Figure 2The diagram illustrates the hierarchical structure of the node reputation assessment model. This model is a supervised machine learning model based on a gradient boosting decision tree. Training relies on a specific sample set constructed from the historical logs of the blockchain network. Each training sample corresponds to a snapshot of a node's behavior within a specific historical observation window T1. Its feature vector includes quantitative indicators such as the node's historical data submission success rate, average consensus response latency, data challenge frequency, and voting stability within T1. The sample's label is determined based on the node's actual performance within the subsequent verification window T2: if all data submitted within T2 is unconditionally accepted and undisputed by the subsequent network consensus, it is labeled "trustworthy"; otherwise, it is labeled "untrustworthy." The model is trained using a large number of such samples, learning the mapping relationship between behavioral characteristics and future trustworthiness. After deployment, the trained model can output a quantitative comprehensive reputation score in real time by inputting the same characteristics of the target node's recent behavior. Based on this score, a reference node is selected from the nodes with the highest current reputation score, such as a long-term neutral and stable third-party notary node. From the selected reference node, standard synchronization data for the divergent data fragment is requested and obtained. This data includes not only the original business record but also its complete and verifiable hash link context. After obtaining it, this standard synchronization data is used to overwrite and update the locally stored copy of the divergent data. Next, for this corrected data fragment, a new round of limited-scope consensus verification is initiated, for example, only among the few nodes that initially disagreed with this fragment. If this verification passes, the fragment is marked as repaired, and this process is repeated until all data fragments in the tamper-proof sequence pass the consistency verification. When all fragments in the entire sequence have passed the verification, this sequence, which has been finally confirmed by consensus, is officially determined as the current trusted data sequence. This sequence represents the only true version of the batch processing process that is jointly recognized by the network within this consensus cycle. Finally, this trusted data sequence is synchronized to all nodes in the network, including the nodes that previously disagreed, through the blockchain network's peer-to-peer protocol to ensure the final unity and authority of the network's data state. Thus, a reliable data foundation with global consistency, which can be used to support subsequent resource allocation and trust assessment, has been established.

[0028] In step S3, the system load of the trusted data sequence is analyzed, and the resource allocation scheme is dynamically adjusted. This includes: calculating the load index of the magnetic separation stage in real time based on the trusted data sequence; when the load index exceeds the first load threshold, adjusting the resource allocation through the access control mechanism, prioritizing the data storage needs of the high-load stage, and generating the adjusted resource allocation scheme; recording each permission and quota adjustment during the scheme execution as a resource allocation log, and storing the resource allocation log in the distributed ledger; simulating the operation of the adjusted resource allocation strategy, and verifying it again based on the real-time load index of the trusted data sequence; if the load index of the key stage is lower than the second load threshold and the overall system resource utilization remains balanced, then the dynamic adjustment is deemed effective and meets the storage requirements, and the second load threshold is lower than or equal to the first load threshold.

[0029] Specifically, after generating a trusted data sequence, dynamic analysis and management of the system load it carries are immediately initiated. First, based on the trusted data sequence, by parsing its timestamps and data entries, the number of newly added data records to the blockchain per unit time (e.g., per minute) in the magnetic separation process is calculated in real time. This serves as the core load indicator. The specific calculation process is as follows: a fixed duration, such as a one-minute sliding time window, is set, and new block on-chain events are continuously monitored. When a new block with a timestamp falling into the current window and the data identifier belonging to the magnetic separation process is detected, the number of relevant data records contained in that block is accumulated in the window counter. At the end of each minute window, this accumulated value is calculated and output as the load indicator of the magnetic separation process in the current minute. This indicator directly reflects the data output intensity of on-site production activities and the instantaneous processing pressure of the on-chain system. When the load indicator is monitored to continuously exceed the preset first load threshold, such as 50 records per minute, it indicates that the magnetic separation production line is operating under high load, and the data flow it generates has approached or exceeded the stable carrying capacity limit of the conventional data processing and storage bandwidth preset for this process by the current system. Without timely intervention, data queue backlogs and significantly increased real-time on-chain delays could occur, even leading to temporary data loss due to system resource contention, thus jeopardizing the real-time performance and integrity of the entire traceability chain. Therefore, triggering a resource adjustment process at this point aims to provide sufficient guarantees for core production data flow through dynamic resource reallocation, ensuring the stability and reliability of the traceability system under high load. Specifically, the adjustment is implemented through the execution of access control smart contracts deployed on the blockchain. These contracts are a set of business logic rules, permanently embedded in the chain and automatically executed by events. They define the resource access permissions and quotas for different system roles, such as data acquisition nodes and historical analysis nodes, under various load levels. When a high-load event triggers contract execution, the contract automatically issues instructions based on its built-in rules. For example, it might temporarily reduce the data write priority of historical data analysis nodes and decrease their available storage space quota by a certain percentage. Through such automatic adjustments, the system quickly releases computing and storage resources from non-real-time critical tasks.

[0030] The released resources are reallocated by the system scheduler, prioritizing and concentrating resources on the high-load magnetic separation stage. This is specifically reflected in: increasing the transmission bandwidth quota of the magnetic separation field data acquisition equipment, increasing the computing resource allocation for its real-time data encryption processing unit, and providing higher network processing priority and fee subsidies for on-chain transactions generated in this stage. This ensures that the core production data flow can complete the entire process from acquisition to trusted on-chain without obstruction, effectively preventing data backlog or loss due to resource competition. After completing all adjustment operations, the smart contract automatically generates an adjusted resource allocation plan describing the specific permission and quota changes. This document meticulously records all objects involved in the adjustment, the specific parameters and values ​​of the changes. For example, the document states, "The network bandwidth quota of the magnetic separation data acquisition node is increased by 15%; the number of real-time write threads for the historical data archiving service is reduced to 2, and the storage pool quota is temporarily reduced by 20%." The implementation process of this plan is strictly recorded and documented, capturing the operational details and corresponding timestamps of each permission change and quota adjustment, forming a structured resource allocation log. This log is then processed using cryptographic hash chaining technology to form an immutable chain structure, and stored in a distributed ledger through a node consensus mechanism, ensuring the transparency and auditability of the entire adjustment decision-making and execution process. To verify the adjustment effect, the generated resource allocation scheme is loaded into an isolated simulation environment for execution. This environment replicates the data processing logic of the production system and uses recent real and reliable data sequences as input load. During the simulation, the system continuously calculates the simulated load indicators of the magnetic separation process, and simultaneously collects real-time resource utilization data of each virtual node through the built-in monitoring module of the simulation environment, including but not limited to CPU computing utilization, memory usage, disk I / O throughput, and network queue depth. By analyzing whether the trend of the simulated load index has fallen below the second threshold, and comprehensively judging whether the resource utilization of each virtual node is within its preset safety threshold range and remains balanced, the effectiveness and feasibility of this dynamic resource adjustment scheme are finally determined. Only when the load index continues to fall and stabilizes below the second threshold and the overall system resource utilization remains balanced can the dynamic adjustment be confirmed as successfully completed. The second load threshold is lower than the first load threshold, for example, less than 40 per minute. Specifically, the higher first threshold serves as an "alarm line" to trigger emergency resource reallocation, while the lower second threshold serves as a "safety line" to confirm that the adjustment has produced substantial effects and that the system state has safely moved away from the risk of overload. This design not only avoids control command jitter that may be caused by a single threshold, but also ensures that the system can maintain sufficient performance redundancy after adjustment to cope with the smooth operation of subsequent loads, thereby improving the robustness and energy efficiency of the traceability system's adaptive management in high-load production environments.If both conditions are met, the dynamic adjustment is deemed effective and the resource allocation meets the current storage and processing requirements. This closed-loop management mechanism enables adaptive optimization of traceability system resources, ensuring the integrity of core traceability data and the overall stability of the system during periods of high-load production.

[0031] Step S4: Extract trust indicators based on the resource allocation scheme, verify the credibility of the data through distributed nodes, obtain the trust verification result, update the monitoring and feedback mechanism of the resource allocation process based on the trust verification result, continuously monitor data fluctuations and generate optimized monitoring records.

[0032] In step S4, the credibility of the data is verified through distributed nodes to obtain a trust verification result. This includes: calculating the initial trust index of each participating node based on the resource guarantee history of the magnetic selection process in the resource allocation scheme and the integrity verification result of the trusted data sequence; if the trust index is lower than the preset standard, data verification is performed through consensus verification among distributed nodes; based on the voting results, the reliability of the trust index is judged using the Byzantine fault tolerance algorithm, and the trust index and trust status are updated accordingly to form the final trust verification result; the trust verification result is stored in the distributed ledger; a trust verification report containing fluctuation analysis and verification details is generated; and the trust verification result is sent to all nodes.

[0033] Specifically, after the dynamic adjustment of the resource allocation scheme is completed, a trust assessment and verification process is immediately executed to quantify and ensure the long-term reliability of the data provided by each participating node. First, based on the historical resource priority records for the magnetic selection process in the resource allocation scheme (e.g., whether the node has been consistently allocated high-priority resources), combined with the integrity verification results of the data contributed by the node in generating the trusted data sequence (e.g., the pass rate of the data blocks provided in historical Merkle tree verification), a quantified initial trust index is calculated for each participating node using a pre-defined weighted calculation formula. This formula is as follows: ,in, As a representative of historical resource weighting factors, it is calculated as the proportion of the total time that the node has been allocated high-priority resources within a set statistical period, such as the past 24 hours. This represents the data integrity factor, which is the success rate of a node's data blocks passing the Merkle tree consistency check in the most recent, for example, the last 20 data commits. The preset weighting coefficients are used, and they satisfy the following conditions: For example, it can be set , With a greater emphasis on data quality, if a node has been in a high-priority protection state for 18 hours in the past 24 hours (R_history=0.75) and its recent data verification success rate is 95% (I_data=0.95), then its initial trust index is calculated as 0.4*0.75+0.6*0.95=0.87. This index provides an objective and quantitative starting point for subsequent consensus verification and trust state management.

[0034] If a node's initial trust score is lower than a preset standard, such as 0.8, it indicates that the node's overall performance in terms of resource guarantee or data quality has not reached the system's reliable baseline level, posing a data reliability risk. Further consensus verification needs to be triggered to confirm its status. For example, if node B's score is 0.75, lower than the threshold of 0.8, a distributed consensus verification of recently provided data by node B is triggered. This verification is jointly performed by other nodes in the network, such as nodes A, C, D, and E: each verification node independently verifies the data submitted by the target node within a specific disputed period, such as before or after a sudden drop in trust, based on its locally stored complete ledger. Each node votes on whether to accept the batch of data. Based on the collected voting results, a Byzantine fault-tolerant algorithm is used to determine whether the node's data is reliable. The Byzantine fault-tolerant algorithm is a distributed consensus algorithm designed to make distributed networks more reliable. Even with a few nodes potentially failing, including unresponsiveness or maliciously sending erroneous information, multiple nodes in the algorithm can still reach a reliable consensus on a certain state or decision. In the specific implementation of this method, when it is necessary to adjudicate the reliability of data from a particular node, multiple verification nodes are convened for independent verification and voting. Through a pre-set multi-round message exchange and voting mechanism, the algorithm ensures that as long as the number of honest verification nodes exceeds two-thirds of the total number of nodes, a single, universally accepted decision on the reliability of the data is produced, effectively tolerating and isolating the influence of a few problematic nodes. For example, if nodes A, C, and D vote "accept," and E votes "reject," with three out of four nodes agreeing, exceeding the majority threshold, then the batch of data from node B is determined to be basically reliable.

[0035] Based on this "basically reliable" ruling, Node B's trust score was slightly increased from 0.75 to 0.78, and its trust status was adjusted from "Under Observation" to "Trusted." The conclusions of this process were summarized into the final trust verification result, which was then encapsulated and stored in a new block of the distributed ledger using cryptographic hash linking technology. Simultaneously, a detailed trust verification report was generated, stating that Node B's trust score increased from 0.75 to 0.78, analyzing the fluctuation as "an isolated verification failure caused by a single data transmission delay, confirmed by majority consensus as non-malicious behavior," and recording detailed statistics of this consensus vote. Finally, through the blockchain network's peer-to-peer propagation protocol, this updated trust verification result was broadcast and synchronized to all nodes in the network, including Node B itself, ensuring timely updates and consistency in the network's perception of Node B's trustworthiness.

[0036] After obtaining the trust verification results, the monitoring and feedback mechanism of the resource utilization process is immediately updated accordingly. Based on the latest trust status and score of each node, the monitoring strategy is adaptively adjusted: for nodes with reduced trust, the monitoring frequency and anomaly detection sensitivity threshold of the magnetic separation data they report are increased; for nodes with high trust, the restrictions can be relaxed appropriately to save resources. On this basis, the data of the entire process is continuously monitored. Once an abnormal fluctuation exceeding the dynamic threshold is detected in the data from a low-trust node, an anti-tampering record containing the details of the abnormal event is immediately regenerated using encrypted hash linking technology, and it is given high processing priority and inserted into the main data chain. At the same time, a structured anomaly report is generated, thereby realizing the dynamic optimization and closed-loop management of the monitoring strategy.

[0037] In step S4, updating the monitoring feedback mechanism of the resource recovery process based on the trust verification result includes: updating the monitoring feedback mechanism of the waste metal separation process based on the trust verification result, continuously monitoring the attribute change tracking data of the magnetic separation stage, and if abnormal fluctuations are found, regenerating tamper-proof records through encrypted hash linking technology, determining the priority of the optimized monitoring records, storing the optimized monitoring records in the distributed ledger, generating abnormal fluctuation reports during the monitoring process, recording abnormal data in detail, and combining timestamp information to form a traceable report file.

[0038] Specifically, upon obtaining the trust verification results, the monitoring and feedback mechanism for the resource recovery process is immediately updated accordingly. Specifically, monitoring parameters are dynamically adjusted based on the latest trust status and score of each node: for nodes with decreased trust (e.g., a trust score dropping from 0.9 to 0.7), the monitoring frequency of their corresponding magnetic separation data, such as the instantaneous purity of iron concentrate, is increased from once per minute to once per second, and the threshold for judging abnormal fluctuations is tightened (e.g., the allowable fluctuation range is adjusted from ±2% to ±0.5%). Based on this, continuous high-frequency monitoring of the entire process attribute transformation tracking data is conducted. Once abnormal fluctuations exceeding the dynamic threshold are detected from data from low-trust nodes—for example, if the reported iron concentrate purity drops sharply by 5% within one second, exceeding the 0.5% allowable range—the anomaly handling process is immediately initiated. For this anomalous data segment, encrypted hash chaining technology is invoked. Starting from the anomaly point and combining it with the hash value of the preceding normal data block, the SHA-256 algorithm is used to recalculate and generate a new tamper-proof record containing anomaly details, a new timestamp, and a new hash value. Based on the severity of the anomalous fluctuation, a sudden drop of 5% is considered a severe anomaly and its importance in the critical magnetic separation process; therefore, this new record is marked as having the highest processing priority. Subsequently, this high-priority monitoring record is stored through a distributed ledger network. During storage, the record is encrypted, broadcast to network nodes, and its validity is confirmed through a consensus mechanism before being written to the blockchain to ensure its immutability. Simultaneously, a structured anomalous fluctuation report is generated, detailing the time of the anomaly, the associated nodes, the specific deviation value (e.g., "purity suddenly dropped from 92% to 87%)", the handling measures taken (e.g., "a high-priority tamper-proof record has been generated, hash: 0xabcd"), and the potential impact assessment. Finally, the summary hash of the report is strongly correlated with the anomaly timestamp and written together into the data chain, thus forming a complete and traceable optimized monitoring log file from anomaly detection and processing to report archiving, realizing dynamic and closed-loop optimization of the monitoring mechanism.

[0039] In summary, this application provides a blockchain-based traceability method for the resource recovery of electrolytic aluminum solid waste. By integrating IoT sensing, distributed ledgers, smart contracts, and dynamic resource scheduling, it constructs a closed-loop, tamper-proof, and adaptive data management system. The method begins with the core magnetic separation process, collecting key process data in real time through a sensor network and generating an initial data chain with timestamps and stage identifiers using blockchain technology. Then, an enhanced process including integrity verification, node replenishment, conflict correction, and encryption ensures the integrity and consistency of the original data, forming an immutable, tamper-proof sequence. A cross-node consensus mechanism establishes a globally recognized trusted data sequence, enabling real-time analysis of system load and dynamic resource optimization, ensuring system stability under high load. Finally, by establishing a behavior-based node trust assessment model and a dynamic monitoring feedback mechanism, resource allocation, data credibility, and monitoring strategies are closely linked, achieving closed-loop intelligent management of the traceability process. This application not only addresses the pain points of easy tampering and difficulty in tracing solid waste treatment data from a technical perspective, but also improves resource utilization efficiency and the level of collaborative trust across the entire industry chain from a system perspective, providing reliable technical support for the digital transformation and high-quality development of industrial solid waste resource utilization.

[0040] The above describes a blockchain-based traceability method for the resource recovery of electrolytic aluminum solid waste in embodiments of this application. The following describes a blockchain-based traceability system for the resource recovery of electrolytic aluminum solid waste in embodiments of this application. Please refer to [link / reference]. Figure 4 One embodiment of a blockchain-based traceability system for the resource recovery of electrolytic aluminum solid waste in this application includes: The chain generation module is used to collect real-time operation data of electrolytic aluminum solid waste in each stage of the resource utilization process, record the property transformation information of electrolytic aluminum solid waste in each stage, and generate an initial data chain with stage identifiers. The anti-tampering processing module is used to verify the integrity of the initial data chain, obtain the enhanced complete data chain, encrypt the complete data chain using encryption technology, form an anti-tampering sequence, and store it in the blockchain; The resource scheduling module is used to perform consistency verification on the tamper-proof sequence through cross-node synchronization technology, confirm the reliability of each segment in the sequence using the node consensus mechanism, generate a trusted data sequence, analyze the system load of the trusted data sequence, and dynamically adjust the resource allocation scheme. The monitoring and optimization module is used to extract trust indicators based on the resource allocation scheme, verify the credibility of the data through distributed nodes, obtain trust verification results, update the monitoring feedback mechanism of the resource allocation process based on the trust verification results, continuously monitor data fluctuations, and generate optimized monitoring records.

[0041] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0042] 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, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0043] 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.

Claims

1. A blockchain-based method for tracing the resource recovery of electrolytic aluminum solid waste, characterized in that, The method includes the following steps: Step S1: Collect real-time operation data of electrolytic aluminum solid waste at each stage of the resource utilization process, and record the attribute transformation information of the electrolytic aluminum solid waste at each stage to generate an initial data chain with stage identifiers. Step S2: Verify the integrity of the initial data chain and obtain the enhanced complete data chain. Encrypt the complete data chain using encryption technology to form a tamper-proof sequence and store it in the blockchain. Step S3: Perform consistency verification on the anti-tampering sequence using cross-node synchronization technology, and confirm the reliability of each segment in the sequence using the node consensus mechanism. If the consensus protocol in the node consensus mechanism determines that no consensus has been reached, locate the data segment that caused the verification failure based on the voting difference and define it as a divergent data segment. For the divergent data segment, call the pre-trained node reputation evaluation model based on gradient boosting decision tree, input the recent historical data submission success rate, average consensus response latency, data challenge frequency and voting stability of each node into the node reputation evaluation model to obtain the comprehensive reputation score of each node, and select a reference node from the nodes with the highest comprehensive reputation score. From the selected reference node, obtain the standard synchronization data of the divergent data segment, and use the standard synchronization data to update the corresponding copy stored locally; re-perform consistency verification on the corrected data segment; when all segments of the tamper-proof sequence pass the verification, the tamper-proof sequence that has passed the verification is determined as a trusted data sequence and synchronized to all nodes in the network. The system load of the trusted data sequence is analyzed. Based on the trusted data sequence, the number of newly added on-chain data records whose data identifiers belong to the magnetic separation stage is calculated in real time per unit time as the load index of the magnetic separation stage. When the load index exceeds the first load threshold, the resource allocation is adjusted through the access control mechanism, and the data storage needs of the high-load stage are given priority. An adjusted resource allocation scheme is generated. Step S4: Based on the resource guarantee history of the magnetic separation stage in the resource allocation scheme and the integrity verification results of the trusted data sequence, calculate the trust index of each participating node, verify the trustworthiness of the data through distributed nodes, obtain the trust verification result, update the monitoring and feedback mechanism of the resource allocation process according to the trust verification result, continuously monitor data fluctuations and generate optimized monitoring records.

2. The method according to claim 1, characterized in that, Step S1 generates an initial data chain with stage identifiers, including: A unique identifier is established for each batch of electrolytic aluminum solid waste. For the magnetic separation stage in the resource recovery process of electrolytic aluminum solid waste, real-time operation data, including magnetic field strength, material flow rate and metal separation efficiency, is acquired through a deployed sensor network. Based on the real-time operation data, attribute transformation information corresponding to different process states in the magnetic separation stage is identified and recorded. Distributed ledger technology is used to bind the attribute transformation information of each stage with a timestamp to form an initial data chain.

3. The method according to claim 1, characterized in that, Step S2 involves verifying the integrity of the initial data chain, including: The initial data chain is integrated into a set of stage-identified records according to the logic flow of the magnetic separation process. The integrity of the record set is verified by traversing the record set and checking whether the timestamp sequence and the stage identifier are continuous. If the proportion of missing data records exceeds a preset threshold, historical record information is obtained from the backup verification node through the node supplementation mechanism. The historical record information is integrated with the existing data chain to form an enhanced complete data chain. For the complete data chain, a data consistency check is performed to determine whether there are any record conflicts. If there are record conflicts, the data is corrected by synchronizing the data from the backup verification node. The corrected data chain is defined as the final complete data chain and stored in the distributed ledger.

4. The method according to claim 3, characterized in that, Step S2 involves forming a tamper-proof sequence and storing it in the blockchain, including: For the enhanced complete data chain, encrypted hash linking technology is used to encrypt the data and generate an encrypted data chain. Combined with the distributed ledger storage mechanism, the key information of the magnetic separation process is stored in the encrypted data chain, and attribute proof information and timestamps are embedded to generate a tamper-proof sequence with irreversible record. For the tamper-proof sequence, the hash value of the encryption process is recorded, and the hash value is associated with the timestamp to form a traceable encrypted record.

5. The method according to claim 1, characterized in that, Step S3 involves performing a consistency check on the tamper-proof sequence using cross-node synchronization technology, including: The tamper-proof sequence is broadcast to multiple verification nodes in the blockchain network. Each verification node independently calculates the Merkle root hash of the tamper-proof sequence and compares it with the expected root hash stored in its own database to perform a consistency check. Each verification node submits the comparison result and its own calculated root hash as a vote to the consensus protocol. The consensus protocol counts the collected votes according to a pre-set validity threshold. When the number of votes received indicating that the tamper-proof sequence is valid and the root hash is consistent exceeds the validity threshold, the tamper-proof sequence is determined to have passed the consistency check between nodes.

6. The method according to claim 1, characterized in that, Step S3 involves analyzing the system load of the trusted data sequence and dynamically adjusting the resource allocation scheme, including: Each permission and quota adjustment during the execution of the adjusted resource allocation scheme is recorded as a resource allocation log, and the resource allocation log is stored in a distributed ledger; the adjusted resource allocation strategy is simulated and verified again based on the real-time load index of the trusted data sequence. If the load index of the key link is lower than the second load threshold and the overall resource utilization of the system remains balanced, the dynamic adjustment is deemed to be effective and meets the storage requirements, and the second load threshold is lower than or equal to the first load threshold.

7. The method according to claim 1, characterized in that, In step S4, the trustworthiness of the data is verified through distributed nodes to obtain the trust verification result, including: If the trust index is lower than the preset standard, data verification is performed through consensus verification among distributed nodes. Based on the voting results, the reliability of the trust index is determined using the Byzantine fault tolerance algorithm, and the trust index and trust status are updated accordingly to form the final trust verification result. The trust verification result is stored in the distributed ledger, a trust verification report containing fluctuation analysis and verification details is generated, and the trust verification result is sent to all nodes.

8. The method according to claim 7, characterized in that, Step S4 involves updating the monitoring and feedback mechanism of the resource allocation process based on the trust verification result, including: Based on the trust verification results, the monitoring feedback mechanism of the waste metal separation process is updated, and the attribute change tracking data of the magnetic separation process is continuously monitored. If abnormal fluctuations are found, the anti-tampering records are regenerated through encrypted hash linking technology, the priority of the optimized monitoring records is determined, and the optimized monitoring records are stored in the distributed ledger. During the monitoring process, an abnormal fluctuation report is generated, the abnormal data is recorded in detail, and combined with the timestamp information, a traceable report file is formed.

9. A blockchain-based traceability system for the resource recovery of electrolytic aluminum solid waste, used to implement the blockchain-based traceability method for the resource recovery of electrolytic aluminum solid waste as described in any one of claims 1-8, characterized in that, The system includes: The chain generation module is used to collect real-time operation data of electrolytic aluminum solid waste in each stage of the resource utilization process, and record the property transformation information of the electrolytic aluminum solid waste in each stage to generate an initial data chain with stage identifiers. The anti-tampering processing module is used to verify the integrity of the initial data chain, obtain the enhanced complete data chain, encrypt the complete data chain using encryption technology, form an anti-tampering sequence, and store it in the blockchain. The resource scheduling module is used to perform consistency verification on the anti-tampering sequence through cross-node synchronization technology, and to confirm the reliability of each segment in the sequence using the node consensus mechanism. If the consensus protocol in the node consensus mechanism determines that no consensus has been reached, the data segment that caused the verification failure is located based on the voting difference and defined as the divergent data segment. For the divergent data segment, a pre-trained node reputation evaluation model based on gradient boosting decision tree is called. The recent historical data submission success rate, average consensus response latency, data challenge frequency and voting stability of each node are input into the node reputation evaluation model to obtain the comprehensive reputation score of each node, and a reference node is selected from the nodes with the highest comprehensive reputation score. From the selected reference node, obtain the standard synchronization data of the divergent data segment, and use the standard synchronization data to update the corresponding copy stored locally; re-perform consistency verification on the corrected data segment; when all segments of the tamper-proof sequence pass the verification, the tamper-proof sequence that has passed the verification is determined as a trusted data sequence and synchronized to all nodes in the network. The system load of the trusted data sequence is analyzed. Based on the trusted data sequence, the number of newly added on-chain data records whose data identifiers belong to the magnetic separation stage is calculated in real time per unit time as the load index of the magnetic separation stage. When the load index exceeds the first load threshold, the resource allocation is adjusted through the access control mechanism, and the data storage needs of the high-load stage are given priority. An adjusted resource allocation scheme is generated. The monitoring and optimization module is used to calculate the trust index of each participating node based on the resource guarantee history and integrity verification results of the magnetic selection process in the resource allocation scheme, verify the trustworthiness of the data through distributed nodes, obtain the trust verification result, update the monitoring feedback mechanism of the resource allocation process according to the trust verification result, continuously monitor data fluctuations and generate optimized monitoring records.

Images