A bankruptcy information management method and system based on blockchain

Abstract

This invention proposes a blockchain-based bankruptcy information management method and system. The method includes: collecting bankruptcy information during the bankruptcy process and encrypting it multiple times; packaging the encrypted bankruptcy information onto the blockchain, using a sharding mechanism to store different types of data in shards; dynamically adjusting the weights of consensus nodes and verifying transactions within each shard using a dynamic consensus algorithm; and, after consensus verification, constructing an innovative hash chain using a wave function for further verification. This method and corresponding system can effectively improve system performance and data tamper resistance, ensuring the rights and interests of all participants and data integrity.

Description

Technical Field

[0001] The present invention proposes a bankruptcy information management method based on blockchain, which relates to the field of information processing technology. Background Art

[0002] In modern economic activities, bankruptcy management is a complex process that affects the interests of multiple parties. Traditional bankruptcy management methods rely on centralized management systems and manual processes, which present multiple problems and challenges that seriously affect the efficiency, transparency, and security of bankruptcy management. 1. Traditional bankruptcy management systems typically rely on centralized databases for data storage. This centralized storage method has significant security vulnerabilities, including the risks of data leakage, tampering, and unauthorized access. 2. In blockchain networks, common consensus mechanisms such as Proof of Work (PoW) and Proof of Stake (PoS) suffer from high energy consumption, low efficiency, and over-reliance on a single metric, failing to ensure fair competition and effective participation among nodes. Summary of the Invention

[0003] The present invention provides a bankruptcy information management method and system based on blockchain to solve the above-mentioned problems:

[0004] The present invention proposes a bankruptcy information management method based on blockchain, the method comprising:

[0005] Collecting bankruptcy management information during the bankruptcy management process and encrypting the bankruptcy management information multiple times;

[0006] The encrypted bankruptcy management information is packaged and uploaded to the blockchain, and the sharding mechanism stores different types of data in shards.

[0007] Dynamically adjust the weight of consensus nodes and verify transactions within each shard through a dynamic consensus algorithm;

[0008] After consensus verification, an innovative hash chain is constructed by using the wave function and further verification is performed.

[0009] Furthermore, bankruptcy management information during the bankruptcy management process is collected and encrypted multiple times, including:

[0010] Obtain bankruptcy management information T during the bankruptcy management process, symmetrically encrypt the bankruptcy management information T to generate a symmetric key K, and use the symmetric key K to encrypt the bankruptcy management information T to generate a ciphertext EK(T): EK(T) = EncryptSym(T, K);

[0011] Encrypt the symmetric key K using an asymmetric encryption algorithm to generate a pair of keys, namely a public key and a private key, and encrypt the symmetric key K using the public key: EPK(K)=EncryptAsym(K,PK);

[0012] Perform homomorphic encryption on the bankruptcy management information T to generate HC(T): HC(T)=HomomorphicEncrypt(T);

[0013] Then generate the zero-knowledge proof ZK of the bankruptcy management information T: ZK=GenerateProof(T).

[0014] Furthermore, the encrypted bankruptcy management information is packaged and uploaded to the blockchain. The sharding mechanism stores different types of data in shards, including:

[0015] Package and upload the encrypted bankruptcy management information to the blockchain;

[0016] After uploading to the blockchain, the entire encrypted bankruptcy management information is divided according to defined data categories to generate multiple sharded data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple sharded data sets include asset information shards, creditor information shards, and debtor information shards;

[0017] Within each shard, a storage hierarchy is defined, including a light node storage hierarchy, a full node storage hierarchy, and a super node storage hierarchy.

[0018] Based on historical bankruptcy management information access data, the importance, access frequency, data size, and data sensitivity of bankruptcy management information are marked;

[0019] A comprehensive score of the data is calculated based on the importance, access frequency, data size and data sensitivity of the bankruptcy management information. When the comprehensive score does not exceed the first preset threshold, it is stored in the light node storage level. When the comprehensive score exceeds the first preset threshold and does not exceed the second preset threshold, it is stored in the full node storage level. When the comprehensive score exceeds the second preset threshold and does not exceed the third preset threshold, it is stored in the super node storage level.

[0020] Furthermore, the weight of consensus nodes is dynamically adjusted, and transaction verification is performed within each shard through a dynamic consensus algorithm, including:

[0021] Collect historical records and performance indicator data of each node and perform preprocessing;

[0022] Extract and summarize the key features that affect node reputation and performance from the preprocessed data. These key features include consensus success rate, CPU performance, bandwidth, storage capacity, and participation consistency.

[0023] The random forest model is used to identify the intrinsic relationship between the key features, and the trained model is used to predict the reputation value and performance score of the node data;

[0024] The weight of each node is calculated based on the reputation value and performance score according to the dynamic weight model. Specifically, the dynamic weight model is:

[0025]

[0026] Among them, w i represents the weight of the node, s i Represents the reputation value of the consensus node, P i Indicates the performance score of the consensus node;

[0027] The weight of each node is dynamically adjusted based on a preset time interval, and transactions are verified within each shard through a dynamic consensus algorithm based on the weight of the consensus node.

[0028] Furthermore, after consensus verification, an innovative hash chain is constructed using the wave function and further verification is performed, including:

[0029] After consensus verification, the initial value H0 of the hash chain is set, and each hash value is calculated in sequence using the wave hash chain generation formula. Specifically, the wave hash chain generation formula is:

[0030]

[0031] Among them, H i Represents the current hash value, ⊕ represents the XOR operator, H is the hash function, T i is the current bankruptcy management information, WTF(i) is the wave function;

[0032] WTF(i)=sin(k·i)+cos(k·i)

[0033] Where i is the index of the current hash chain, indicating the position of the calculated hash value in the hash chain, and k is a constant used to adjust the periodicity and amplitude of the wave function;

[0034] When new data is added to the hash chain after each transaction, the value of the hash chain is recalculated and compared.

[0035] The present invention proposes a blockchain-based bankruptcy information management system, which includes:

[0036] An encryption module, used for collecting bankruptcy management information during the bankruptcy management process and encrypting the bankruptcy management information multiple times;

[0037] The shard storage module is used to package the encrypted bankruptcy management information and upload it to the blockchain. The sharding mechanism stores different types of data in shards.

[0038] The consensus verification module is used to dynamically adjust the weight of consensus nodes and verify transactions within each shard through a dynamic consensus algorithm;

[0039] The hash verification module is used to build an innovative hash chain after consensus verification by using the wave function for further verification.

[0040] Furthermore, the encryption module includes:

[0041] Obtain bankruptcy management information T during the bankruptcy management process, symmetrically encrypt the bankruptcy management information T to generate a symmetric key K, and use the symmetric key K to encrypt the bankruptcy management information T to generate a ciphertext EK(T): EK(T) = EncryptSym(T, K);

[0042] Encrypt the symmetric key K using an asymmetric encryption algorithm to generate a pair of keys, namely a public key and a private key, and encrypt the symmetric key K using the public key: EPK(K)=EncryptAsym(K,PK);

[0043] Perform homomorphic encryption on the bankruptcy management information T to generate HC(T): HC(T)=HomomorphicEncrypt(T);

[0044] Then generate the zero-knowledge proof ZK of the bankruptcy management information T: ZK=GenerateProof(T).

[0045] Furthermore, the shard storage module includes:

[0046] The upload module is used to package and upload the encrypted bankruptcy management information to the blockchain;

[0047] A data partitioning module is used to partition the entire encrypted bankruptcy management information according to defined data categories after uploading to the blockchain to generate multiple sharded data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple sharded data sets include asset information shards, creditor information shards, and debtor information shards;

[0048] A storage hierarchy definition module is used to define a storage hierarchy within each shard, including a light node storage hierarchy, a full node storage hierarchy, and a super node storage hierarchy;

[0049] A labeling module is used to label the importance, access frequency, data size and data sensitivity of bankruptcy management information based on historical bankruptcy management information access data;

[0050] A storage module is used to calculate a comprehensive score of the data based on the importance, access frequency, data size and data sensitivity of the bankruptcy management information; when the comprehensive score does not exceed a first preset threshold, it is stored in a light node storage level; when the comprehensive score exceeds the first preset threshold and does not exceed a second preset threshold, it is stored in a full node storage level; when the comprehensive score exceeds the second preset threshold and does not exceed a third preset threshold, it is stored in a super node storage level.

[0051] Furthermore, the consensus verification module includes:

[0052] The historical data collection module is used to collect historical records and performance indicator data of each node and perform preprocessing;

[0053] The feature extraction module is used to extract and summarize the key features that affect node reputation and performance from the preprocessed data. The key features include consensus success rate, CPU performance, bandwidth, storage capacity, and participation consistency;

[0054] A training model module is used to identify the intrinsic relationship between the key features through a random forest model, and use the trained model to predict the reputation value and performance score of the node data;

[0055] The weight calculation module is used to calculate the weight of each node based on the reputation value and performance score according to the dynamic weight model. Specifically, the dynamic weight model is:

[0056]

[0057] Among them, w i represents the weight of the node, s i Represents the reputation value of the consensus node, P i Indicates the performance score of the consensus node;

[0058] The update weight module is used to dynamically adjust the weight of each node based on a preset time interval. The weight of the consensus node is used to verify transactions within each shard through a dynamic consensus algorithm.

[0059] Furthermore, the hash verification module includes:

[0060] Generate a hash chain module, which is used after consensus verification to set the initial value H0 of the hash chain and use the wave hash chain generation formula to calculate each hash value in sequence. Specifically, the wave hash chain generation formula is:

[0061] H i =H(H i-1 ⊕T i ⊕WTF(i))

[0062] Among them, H i Represents the current hash value, ⊕ represents the XOR operator, H is the hash function, T i is the current bankruptcy management information, WTF(i) is the wave function;

[0063] WTF(i)=sin(k·i)+cos(k·i)

[0064] Where i is the index of the current hash chain, indicating the position of the calculated hash value in the hash chain, and k is a constant used to adjust the periodicity and amplitude of the wave function;

[0065] The comparison and verification module is used to recalculate and compare the value of the hash chain when new data is added to the hash chain after each transaction.

[0066] The beneficial effects of the present invention are: enhanced data security, multiple encryption operations ensure the high security of bankruptcy management information during transmission and storage. Even if one layer of encryption is compromised, the remaining layers can still provide protection, which significantly increases the difficulty of data theft and tampering; improved system performance and scalability, data sharding storage optimizes the big data processing mechanism. By sharding data of different types or categories and distributing them on different nodes, the storage and access efficiency is significantly improved, the burden of single-point storage is reduced, and the overall system performance is improved. At the same time, the sharding mechanism also enhances the scalability of the system, enabling the system to handle massive data and high-frequency transactions; optimizes the consensus mechanism, dynamically adjusts the weights of consensus nodes, and can adjust the participating nodes in the consensus process in real time, allocating more responsibility for consensus verification to efficient and reliable nodes, thereby improving the overall verification speed and accuracy. Independent consensus within the shard further improves the processing capacity of the system, allowing multiple shards to process transactions in parallel, improving consensus efficiency; enhances data consistency and anti-tampering capabilities. The innovative hash chain generated by the wave function greatly improves the security and anti-attack capabilities of the blockchain. The nonlinearity and high complexity of the wave function make the hash chain structure difficult to tamper with. At the same time, multiple verifications of the hash chain further ensure the integrity and consistency of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a schematic diagram of a bankruptcy information management method based on blockchain according to the present invention. DETAILED DESCRIPTION

[0068] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein may be combined with each other.

[0069] The following description sets forth numerous specific details to facilitate a thorough understanding of the present invention. The embodiments described are merely a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0071] One embodiment of the present invention provides a bankruptcy information management method based on blockchain, the method comprising:

[0072] Collecting bankruptcy management information during the bankruptcy management process and encrypting the bankruptcy management information multiple times;

[0073] The encrypted bankruptcy management information is packaged and uploaded to the blockchain, and the sharding mechanism stores different types of data in shards.

[0074] Dynamically adjust the weight of consensus nodes and verify transactions within each shard through a dynamic consensus algorithm;

[0075] After consensus verification, an innovative hash chain is constructed by using the wave function and further verification is performed.

[0076] The working principle and effect of the above technical solution are as follows: bankruptcy administration information is collected and encrypted multiple times. During the bankruptcy administration process, the information collected includes sensitive information such as creditor lists, bankruptcy application materials, financial statements, and liquidation progress. This information is first encrypted to ensure its security during transmission and storage. The encryption process not only uses standard symmetric and asymmetric encryption techniques, but also employs multiple encryption techniques to enhance data protection. Multiple encryption refers to the process of performing one or more additional encryption operations on the encrypted data after the initial encryption, increasing the complexity and security of decryption. The encrypted bankruptcy administration information is then packaged and uploaded to the blockchain. After encryption, this information is packaged into transaction data on the blockchain and then disseminated across the blockchain network. Each node in the blockchain verifies and packages this transaction information, forming a new block and adding it to the chain. During this process, to ensure data fragmentation, data is classified and partitioned, with different types of data being stored in separate shards. This includes sharding by type (such as financial information, legal documents, progress reports, etc.) or according to other pre-set policies. The system dynamically adjusts the weights of consensus nodes to improve the efficiency and reliability of the consensus process. Dynamic weight adjustments are based on factors such as node uptime, processing power, and historical performance. A dynamic consensus algorithm is then used to verify transactions within each shard, giving more reliable and high-performing nodes greater influence in the consensus process, ensuring verification efficiency and accuracy. This algorithm can be adjusted as needed, allowing the system to quickly adapt to varying network conditions and node performance fluctuations. An innovative hash chain is constructed using the Wave function. Once the consensus process is complete, the blockchain system generates a hash chain using the Wave function. The Wave function is a nonlinear function with high complexity and attack resistance, effectively preventing data tampering. After generating the hash chain, the system conducts further data verification, ensuring the integrity and consistency of the data chain through multiple checksums. This hash chain not only enhances system security but also strengthens the tamper-proofness of blockchain data. Multiple encryption operations ensure high security during the transmission and storage of bankruptcy management information. Even if one layer of encryption is compromised, the remaining layers still provide protection, significantly increasing the difficulty of data theft and tampering. Furthermore, system performance and scalability are improved, and sharded data storage optimizes big data processing.By sharding data of different types or categories and distributing it across different nodes, storage and access efficiency is significantly improved, reducing the burden on single-point storage and boosting overall system performance. Sharding also enhances system scalability, enabling the system to handle massive amounts of data and high-frequency transactions. The consensus mechanism is optimized, dynamically adjusting the weights of consensus nodes, enabling real-time adjustments to participating nodes in the consensus process and assigning more responsibility for consensus verification to efficient and reliable nodes, thereby improving overall verification speed and accuracy. Independent consensus within shards further enhances the system's processing power, allowing multiple shards to process transactions in parallel and improving consensus efficiency. Data consistency and tamper resistance are enhanced. The innovative hash chain generated by the wave function significantly improves the security and anti-attack capabilities of the blockchain. The nonlinearity and high complexity of the wave function make the hash chain structure difficult to tamper with. Furthermore, multiple verifications of the hash chain further ensure data integrity and consistency. This ensures the authenticity and reliability of on-chain data and prevents tampering, enabling the realization of an efficient, secure, and highly scalable bankruptcy management system. While ensuring data security and confidentiality, the system effectively improves system performance and data tamper resistance through technologies such as sharded storage, dynamic consensus, and innovative hash chains, ensuring the rights and interests of all participants and data integrity. This system design can better address the various complexities and challenges of bankruptcy management, achieving efficient, secure, and reliable business processing.

[0077] One embodiment of the present invention collects bankruptcy management information during the bankruptcy management process and encrypts the bankruptcy management information multiple times, including:

[0078] Obtain bankruptcy management information T during the bankruptcy management process, symmetrically encrypt the bankruptcy management information T to generate a symmetric key K, and use the symmetric key K to encrypt the bankruptcy management information T to generate a ciphertext EK(T): EK(T) = EncryptSym(T, K);

[0079] Encrypt the symmetric key K using an asymmetric encryption algorithm to generate a pair of keys, namely a public key and a private key, and encrypt the symmetric key K using the public key: EPK(K)=EncryptAsym(K,PK);

[0080] Perform homomorphic encryption on the bankruptcy management information T to generate HC(T): HC(T)=HomomorphicEncrypt(T);

[0081] Then generate the zero-knowledge proof ZK of the bankruptcy management information T: ZK=GenerateProof(T).

[0082] The working principle and effect of the above technical solution are as follows: bankruptcy management information is obtained and subjected to multi-layer encryption. First, information on the bankruptcy management process is collected from different sources, denoted as T. To ensure the security of data during transmission and storage, the following encryption operations are performed: Symmetric encryption: A symmetric key K is generated using a symmetric encryption algorithm (such as AES). Then, K is used to encrypt the bankruptcy management information T, generating the ciphertext EK(T). This encryption method is fast and efficient, but requires secure key distribution. To securely transmit the symmetric key K, an asymmetric encryption algorithm (such as RSA) is used to generate a pair of keys: a public key and a private key. The symmetric key K is encrypted using the public key PK to generate EPK(K): EPK(K) = EncryptAsym(K,PK). Asymmetric encryption is very effective in the key exchange process, although it is slow. To perform calculations on encrypted data, a homomorphic encryption algorithm is used to encrypt the bankruptcy management information T, generating HC(T): HC(T) = HomomorphicEncrypt(T). Homomorphic encryption allows computations to be performed directly on encrypted data, improving confidentiality and security. To prove certain properties or characteristics of T without revealing T itself, a zero-knowledge proof ZK is generated: ZK = GenerateProof(T). Zero-knowledge proofs ensure the correctness and integrity of data without revealing the actual data. To improve data security and confidentiality, multi-layered encryption is employed to provide multiple layers of protection during transmission and storage. Not only is the content encrypted, but the symmetric key is also asymmetrically encrypted to ensure its secure transmission. Homomorphic encryption allows direct operations on encrypted data, ensuring data confidentiality during computations. Zero-knowledge proofs ensure the correctness and integrity of data without revealing the actual data content.

[0083] In one embodiment of the present invention, the encrypted bankruptcy management information is packaged and uploaded to the blockchain, and a sharding mechanism is used to store different types of data in shards, including:

[0084] Package and upload the encrypted bankruptcy management information to the blockchain;

[0085] After uploading to the blockchain, the entire encrypted bankruptcy management information is divided according to defined data categories to generate multiple sharded data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple sharded data sets include asset information shards, creditor information shards, and debtor information shards;

[0086] Within each shard, a storage hierarchy is defined, including a light node storage hierarchy, a full node storage hierarchy, and a super node storage hierarchy.

[0087] Based on historical bankruptcy management information access data, the importance, access frequency, data size, and data sensitivity of bankruptcy management information are marked;

[0088] A comprehensive score of the data is calculated based on the importance, access frequency, data size and data sensitivity of the bankruptcy management information. When the comprehensive score does not exceed the first preset threshold, it is stored in the light node storage level. When the comprehensive score exceeds the first preset threshold and does not exceed the second preset threshold, it is stored in the full node storage level. When the comprehensive score exceeds the second preset threshold and does not exceed the third preset threshold, it is stored in the super node storage level.

[0089] Scoring criteria are set for each attribute. Importance (Importance, I): 1: Minor, 2: General, 3: Important, 4: Very Important, 5: Extremely Important. Access Frequency (A): 1: Several times a year, 2: Several times a quarter, 3: Several times a month, 4: Several times a week, 5: Several times a day. Data Size (D): 1: Less than 1MB, 2: 1MB-10MB, 3: 10MB-100MB, 4: 100MB-1GB, 5: More than 1GB. Data Sensitivity (S): 1: General data, 2: Lower sensitivity data, 3: Moderately sensitive data, 4: Higher sensitivity data, 5: Highly sensitive data. Bankruptcy management information is labeled based on the access data and scoring criteria of historical bankruptcy management information, and a comprehensive scoring formula is designed, taking into account the weight of each attribute.

[0090] Score=α·I+β·A+γ·D+δ·S,

[0091] Where Score represents the score, and α, β, γ, and δ represent weight coefficients. For example, set the following weights: α = 0.4 (importance), β = 0.3 (access frequency), γ = 0.2 (data size), δ = 0.1 (data sensitivity), and determine the score threshold for each storage layer. Assume the following thresholds: Light Node Layer: Total score ≤ 2, Full Node Layer: 2 < Total score ≤ 3.5, Super Node Layer: Total score > 3.5.

[0092] The working principle and effect of the above technical solution are as follows: once the above multi-layer encryption operation is completed, all encrypted data are packaged and uploaded to the blockchain. At the same time, a sharding mechanism is applied to divide the encrypted data according to predefined categories and store them in different shards: Data classification: The encrypted bankruptcy management information is divided according to data categories (asset information, creditor information, debtor information), generating multiple sharded data sets: asset information shards, creditor information shards, and debtor information shards. Within each shard, storage levels are defined, including light node storage level, full node storage level, and super node storage level. Based on historical access data, the importance, access frequency, data size, and data sensitivity of the data are evaluated, and the comprehensive score of the data is calculated. Depending on the comprehensive score, the data is stored in nodes of different levels: if the comprehensive score is less than or equal to the first threshold, it is stored in the light node level; if the comprehensive score is between the first and second thresholds, it is stored in the full node level; if the comprehensive score is between the second and third thresholds, it is stored in the super node level. Optimize data storage and management. Sharding is used to classify and store data, rationally dividing various types of data into different sharded datasets, improving data management efficiency. Different storage tiers are divided based on the data's comprehensive score, ensuring that high-frequency, sensitive data is stored on nodes with higher security levels, while low-frequency, non-sensitive data is stored on lightweight nodes, optimizing resource allocation and utilization. Enhance system scalability and performance. Sharding can distribute data storage pressure and improve storage and query efficiency. Dynamic adjustment of storage tiers ensures the system can adapt to changing data needs and changes, enhancing system flexibility and scalability. Ensure data integrity and tamper-resistance. The immutability of blockchain, combined with multi-layer encryption technology, makes uploaded data difficult to tamper with, ensuring data integrity. Zero-knowledge proofs further enhance the data verification process, ensuring data authenticity and tamper-resistance. These technical approaches enable a secure and efficient bankruptcy management information storage and processing system. This not only protects sensitive information, but also optimizes the data management process and improves the overall performance and security of the system.

[0093] One embodiment of the present invention dynamically adjusts the weight of consensus nodes and performs transaction verification within each shard using a dynamic consensus algorithm, including:

[0094] Collect historical records and performance indicator data of each node and perform preprocessing;

[0095] Extract and summarize the key features that affect node reputation and performance from the preprocessed data. These key features include consensus success rate, CPU performance, bandwidth, storage capacity, and participation consistency.

[0096] The random forest model is used to identify the intrinsic relationship between the key features, and the trained model is used to predict the reputation value and performance score of the node data;

[0097] The weight of each node is calculated based on the reputation value and performance score according to the dynamic weight model. Specifically, the dynamic weight model is:

[0098]

[0099] Among them, w i represents the weight of the node, s i Represents the reputation value of the consensus node, P i Indicates the performance score of the consensus node;

[0100] The weight of each node is dynamically adjusted based on a preset time interval, and transactions are verified within each shard through a dynamic consensus algorithm based on the weight of the consensus node.

[0101] "Participation consistency" refers to the stability and reliability of a node in the blockchain consensus process. Specifically, it measures whether the node can participate in the consensus process continuously, punctually, and reliably, and whether its performance is stable. It includes the following aspects: Uptime: The proportion of time a node remains online in the network; Response Time: The speed at which a node responds to consensus requests. Nodes with high participation consistency should respond within the specified time; Stability: Whether the node's operating status is stable, and the proportion of times it stops or restarts during the consensus process; Participation Rate: The frequency of a node's participation in the consensus process. Nodes with high participation consistency should participate in the consensus process consistently and stably; Network Latency: The communication delay between the node and other nodes. Nodes with high participation consistency should have low network latency.

[0102] The working principle and effect of the above technical solution are as follows: collecting and preprocessing node history and performance indicators. This data is collected from each node participating in the blockchain network. This data includes historical behavior records, processing capabilities, network bandwidth, and storage capacity during the consensus process. The collected data first needs to be preprocessed to ensure consistency, completeness, and accuracy. Preprocessing steps include data cleaning, missing value filling, and data standardization. After preprocessing, key features that may affect node reputation and performance are extracted from the data. These key features include: consensus success rate (i.e., the proportion of nodes that successfully participate in the consensus process); CPU performance (i.e., the processing power of the node); bandwidth (i.e., the node's network bandwidth, which determines its data transmission speed); and storage capacity (i.e., the amount of storage space available). A random forest model is used to identify feature relationships and predict node reputation and performance scores. The extracted key features are analyzed using the random forest model to identify the inherent relationships between them. Random forest is an integrated learning algorithm that performs training and prediction by constructing multiple decision trees. First, the random forest model is trained with historical data to enable it to identify the main features that affect node reputation and performance. Then, the trained model is used to predict new node data to obtain the reputation value and performance score of each node. Based on the predicted node reputation value and performance score, the weight of each node is dynamically adjusted according to the preset time interval to adapt to network changes. Based on the adjusted node weight, transactions are verified within each shard through the dynamic consensus algorithm. The dynamic consensus algorithm allocates voting weight or other consensus parameters according to the weight of each node, ensuring that nodes with high reputation and high performance have a greater influence in the consensus process.Enhance data security and system stability. By collecting and analyzing the historical records and performance indicators of nodes, the reputation value and performance score of each node can be accurately evaluated and predicted to ensure a more reliable and stable consensus process. Dynamically adjust the weight mechanism to ensure that high-trust and high-performance nodes play a greater role in the consensus process, reduce the negative impact of low-quality nodes on the system, and thus improve the overall stability and security of the system. Through dynamic weight models and random forest analysis, a comprehensive investigation and evaluation of key features is achieved, making resource allocation more scientific and reasonable. Performance nodes obtain greater weights, which can optimize the utilization efficiency of computing resources, improve the system transaction verification and processing speed, and thus improve the overall performance of the system. Effectively respond to system changes and optimize adaptive capabilities, dynamically adjust node weights at preset time intervals, and the system can promptly adapt to changes in network environment and node performance. Continuously optimizing its adaptive capabilities, the consensus participation strategy is adjusted based on the latest data, improving the flexibility and efficiency of the consensus algorithm. This allows the system to maintain efficient operation despite varying loads and changes in node behavior. The consensus process is also transparent and fair. Through a transparent node evaluation and weight distribution mechanism, transparency is enhanced, allowing each node to have a clear understanding and understanding of its own performance and system allocation. The dynamic weight adjustment mechanism performs a comprehensive assessment based on multiple key characteristics, making weight distribution more equitable and scientific, ensuring the fairness of system operation. The dynamic consensus algorithm verifies transactions within each shard, ensuring that high-quality nodes within the shard play a greater role and improving transaction verification accuracy. With the dynamic adjustment of shard weights, the efficiency of intra-shard transaction processing is optimized, improving the overall effectiveness of the sharding mechanism. This technical solution enables the construction of an efficient, reliable, and flexible blockchain system that provides better resource management and consensus-building mechanisms in complex business environments such as bankruptcy management, ensuring system security, performance, and stability.

[0103] In one embodiment of the present invention, after consensus verification, an innovative hash chain is constructed using a wave function and further verification is performed, including:

[0104] After consensus verification, the initial value H0 of the hash chain is set, and each hash value is calculated in sequence using the wave hash chain generation formula. Specifically, the wave hash chain generation formula is:

[0105] H i =H(H i-1 ⊕T i ⊕WTF(i))

[0106] Among them, H i Indicates the current hash value, H i-1 represents the previous hash value, ⊕ represents the XOR operator, H is the hash function, T iis the current bankruptcy management information, WTF(i) is the wave function;

[0107] WTF(i)=sin(k·i)+cos(k·i)

[0108] Where i is the index of the current hash chain, indicating the position of the calculated hash value in the hash chain, and k is a constant used to adjust the periodicity and amplitude of the wave function;

[0109] When new data is added to the hash chain after each transaction, the value of the hash chain is recalculated and compared.

[0110] The working principle and effects of the above technical solution are as follows:

[0111] The hash calculation process is as follows:

[0112] 1. First, calculate the initial value H0 of the hash chain;

[0113] 2. For the i-th transaction data T i , calculate the wave function WTF(i).

[0114] 3. The previous hash value H i-1 、Current transaction data T i Perform XOR operation with wave function WTF(i).

[0115] 4. Apply the hash function H to the result of the XOR operation to obtain the current hash value H i .

[0116] Repeat steps 2-4 until all hash values on the current hash chain are calculated.

[0117] 4. Update and comparison of dynamic hash chains

[0118] Every time new transaction data is added to the blockchain, the hash chain is recalculated and compared with the previous one. Any anomalies or inconsistencies in the hash chain indicate possible data tampering or errors, requiring processing and correction. This approach ensures the integrity and security of every transaction data. Improve data security and integrity. Through the generation and calculation of wave hash chains, the hash value of each transaction data is associated with the previous hash value, the current data, and the wave function, increasing the difficulty of data tampering. Even if one data is tampered with, all subsequent hash values will change, immediately indicating data anomalies and ensuring data integrity. Enhance the reliability of data verification. The hash chain calculation method of mathematical functions (such as wave functions) is adopted, so that each hash value depends not only on the hash value of the previous data, but also considers the current data and the results of the mathematical function, making data verification more reliable. The application of this functional relationship makes the generation of hash chains not only dependent on single data content, but also considers periodic changes, providing multiple verification dimensions. Improve the dynamic response capability of the system. When new transaction data is added, the system can quickly recalculate the hash chain and perform comparative verification, allowing the system to dynamically respond and detect data integrity in real time. Compared with traditional static chains, wave hash chains take into account dynamic changes in data and are more flexible. Provide anti-tampering evidence. Abnormal or unauthorized data modifications can only be quickly detected when they directly affect specific data. This provides a lightweight and effective tamper-proofing method. The long-term hash chain history can serve as evidence of data integrity, providing a reliable basis for traceability and dispute resolution. Parameter adjustment enhances flexibility: the constant k in the wave function can be adjusted according to specific needs, varying the periodicity and amplitude of the calculation results, thus enhancing the flexibility and adaptability of the hash chain. This technical solution utilizes a wave-like hash chain generation formula, combined with data XOR and hash functions, to design an efficient, secure, and flexible bankruptcy information management method. This solution not only enhances the security and data integrity of bankruptcy management information, but also improves the overall system's dynamic responsiveness and data verification reliability, making the bankruptcy management process more transparent and efficient.

[0119] One embodiment of the present invention provides a bankruptcy information management system based on blockchain, the system comprising:

[0120] An encryption module, used for collecting bankruptcy management information during the bankruptcy management process and encrypting the bankruptcy management information multiple times;

[0121] The shard storage module is used to package the encrypted bankruptcy management information and upload it to the blockchain. The sharding mechanism stores different types of data in shards.

[0122] The consensus verification module is used to dynamically adjust the weight of consensus nodes and verify transactions within each shard through a dynamic consensus algorithm;

[0123] The hash verification module is used to build an innovative hash chain after consensus verification by using the wave function for further verification.

[0124] The working principle and effect of the above technical solution are as follows: bankruptcy administration information is collected and encrypted multiple times. During the bankruptcy administration process, the information collected includes sensitive information such as creditor lists, bankruptcy application materials, financial statements, and liquidation progress. This information is first encrypted to ensure its security during transmission and storage. The encryption process not only uses standard symmetric and asymmetric encryption techniques, but also employs multiple encryption techniques to enhance data protection. Multiple encryption refers to the process of performing one or more additional encryption operations on the encrypted data after the initial encryption, increasing the complexity and security of decryption. The encrypted bankruptcy administration information is then packaged and uploaded to the blockchain. After encryption, this information is packaged into transaction data on the blockchain and then disseminated across the blockchain network. Each node in the blockchain verifies and packages this transaction information, forming a new block and adding it to the chain. During this process, to ensure data fragmentation, data is classified and partitioned, with different types of data being stored in separate shards. This includes sharding by type (such as financial information, legal documents, progress reports, etc.) or according to other pre-set policies. The system dynamically adjusts the weights of consensus nodes to improve the efficiency and reliability of the consensus process. Dynamic weight adjustments are based on factors such as node uptime, processing power, and historical performance. A dynamic consensus algorithm is then used to verify transactions within each shard, giving more reliable and high-performing nodes greater influence in the consensus process, ensuring verification efficiency and accuracy. This algorithm can be adjusted as needed, allowing the system to quickly adapt to varying network conditions and node performance fluctuations. An innovative hash chain is constructed using the Wave function. Once the consensus process is complete, the blockchain system generates a hash chain using the Wave function. The Wave function is a nonlinear function with high complexity and attack resistance, effectively preventing data tampering. After generating the hash chain, the system conducts further data verification, ensuring the integrity and consistency of the data chain through multiple checksums. This hash chain not only enhances system security but also strengthens the tamper-proofness of blockchain data. Multiple encryption operations ensure high security during the transmission and storage of bankruptcy management information. Even if one layer of encryption is compromised, the remaining layers still provide protection, significantly increasing the difficulty of data theft and tampering. Furthermore, system performance and scalability are improved, and sharded data storage optimizes big data processing.By sharding data of different types or categories and distributing it across different nodes, storage and access efficiency is significantly improved, reducing the burden on single-point storage and boosting overall system performance. Sharding also enhances system scalability, enabling the system to handle massive amounts of data and high-frequency transactions. The consensus mechanism is optimized, dynamically adjusting the weights of consensus nodes, enabling real-time adjustments to participating nodes in the consensus process and assigning more responsibility for consensus verification to efficient and reliable nodes, thereby improving overall verification speed and accuracy. Independent consensus within shards further enhances the system's processing power, allowing multiple shards to process transactions in parallel and improving consensus efficiency. Data consistency and tamper resistance are enhanced. The innovative hash chain generated by the wave function significantly improves the security and anti-attack capabilities of the blockchain. The nonlinearity and high complexity of the wave function make the hash chain structure difficult to tamper with. Furthermore, multiple verifications of the hash chain further ensure data integrity and consistency. This ensures the authenticity and reliability of on-chain data and prevents tampering, enabling the realization of an efficient, secure, and highly scalable bankruptcy management system. While ensuring data security and confidentiality, the system effectively improves system performance and data tamper resistance through technologies such as sharded storage, dynamic consensus, and innovative hash chains, ensuring the rights and interests of all participants and data integrity. This system design can better address the various complexities and challenges of bankruptcy management, achieving efficient, secure, and reliable business processing.

[0125] In one embodiment of the present invention, a bankruptcy information management system based on blockchain is provided, wherein the encryption module includes:

[0126] Obtain bankruptcy management information T during the bankruptcy management process, symmetrically encrypt the bankruptcy management information T to generate a symmetric key K, and use the symmetric key K to encrypt the bankruptcy management information T to generate a ciphertext EK(T): EK(T) = EncryptSym(T, K);

[0127] Encrypt the symmetric key K using an asymmetric encryption algorithm to generate a pair of keys, namely a public key and a private key, and encrypt the symmetric key K using the public key: EPK(K)=EncryptAsym(K,PK);

[0128] Perform homomorphic encryption on the bankruptcy management information T to generate HC(T): HC(T)=HomomorphicEncrypt(T);

[0129] Then generate the zero-knowledge proof ZK of the bankruptcy management information T: ZK=GenerateProof(T).

[0130] The working principle and effect of the above technical solution are as follows: bankruptcy management information is obtained and subjected to multi-layer encryption. First, information on the bankruptcy management process is collected from different sources, denoted as T. To ensure the security of data during transmission and storage, the following encryption operations are performed: Symmetric encryption: A symmetric key K is generated using a symmetric encryption algorithm (such as AES). Then, K is used to encrypt the bankruptcy management information T, generating the ciphertext EK(T). This encryption method is fast and efficient, but requires secure key distribution. To securely transmit the symmetric key K, an asymmetric encryption algorithm (such as RSA) is used to generate a pair of keys: a public key and a private key. The symmetric key K is encrypted using the public key PK to generate EPK(K): EPK(K) = EncryptAsym(K,PK). Asymmetric encryption is very effective in the key exchange process, although it is slow. To perform calculations on encrypted data, a homomorphic encryption algorithm is used to encrypt the bankruptcy management information T, generating HC(T): HC(T) = HomomorphicEncrypt(T). Homomorphic encryption allows computations to be performed directly on encrypted data, improving confidentiality and security. To prove certain properties or characteristics of T without revealing T itself, a zero-knowledge proof ZK is generated: ZK = GenerateProof(T). Zero-knowledge proofs ensure the correctness and integrity of data without revealing the actual data. To improve data security and confidentiality, multi-layered encryption is employed to provide multiple layers of protection during transmission and storage. Not only is the content encrypted, but the symmetric key is also asymmetrically encrypted to ensure its secure transmission. Homomorphic encryption allows direct operations on encrypted data, ensuring data confidentiality during computations. Zero-knowledge proofs ensure the correctness and integrity of data without revealing the actual data content.

[0131] In one embodiment of the present invention, a bankruptcy information management system based on blockchain is provided, wherein the shard storage module includes:

[0132] The upload module is used to package and upload the encrypted bankruptcy management information to the blockchain;

[0133] A data partitioning module is used to partition the entire encrypted bankruptcy management information according to defined data categories after uploading to the blockchain to generate multiple sharded data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple sharded data sets include asset information shards, creditor information shards, and debtor information shards;

[0134] A storage hierarchy definition module is used to define a storage hierarchy within each shard, including a light node storage hierarchy, a full node storage hierarchy, and a super node storage hierarchy;

[0135] A labeling module is used to label the importance, access frequency, data size and data sensitivity of bankruptcy management information based on historical bankruptcy management information access data;

[0136] A storage module is used to calculate a comprehensive score of the data based on the importance, access frequency, data size and data sensitivity of the bankruptcy management information; when the comprehensive score does not exceed a first preset threshold, it is stored in a light node storage level; when the comprehensive score exceeds the first preset threshold and does not exceed a second preset threshold, it is stored in a full node storage level; when the comprehensive score exceeds the second preset threshold and does not exceed a third preset threshold, it is stored in a super node storage level.

[0137] The working principle and effect of the above technical solution are as follows: once the above multi-layer encryption operation is completed, all encrypted data are packaged and uploaded to the blockchain. At the same time, a sharding mechanism is applied to divide the encrypted data according to predefined categories and store them in different shards: Data classification: The encrypted bankruptcy management information is divided according to data categories (asset information, creditor information, debtor information), generating multiple sharded data sets: asset information shards, creditor information shards, and debtor information shards. Within each shard, storage levels are defined, including light node storage level, full node storage level, and super node storage level. Based on historical access data, the importance, access frequency, data size, and data sensitivity of the data are evaluated, and the comprehensive score of the data is calculated. Depending on the comprehensive score, the data is stored in nodes of different levels: if the comprehensive score is less than or equal to the first threshold, it is stored in the light node level; if the comprehensive score is between the first and second thresholds, it is stored in the full node level; if the comprehensive score is between the second and third thresholds, it is stored in the super node level. Optimize data storage and management. Through the sharding mechanism, data is classified and stored. Various types of data are rationally divided into different sharded data sets, improving data management efficiency. Different storage levels are divided according to the comprehensive score of the data, ensuring that high-frequency and sensitive data is stored on nodes with higher security levels, while low-frequency and non-sensitive data is stored on light nodes, optimizing resource allocation and utilization.

[0138] Enhance the system's scalability and performance. The use of a sharded storage mechanism can disperse data storage pressure and improve storage and query efficiency. Dynamic adjustment of storage levels ensures that the system can adapt to different data needs and changes, improving the system's flexibility and scalability. Ensure data integrity and tamper-proofing. The immutability of blockchain combined with multi-layer encryption technology makes uploaded data difficult to tamper with, ensuring data integrity. The use of zero-knowledge proofs further enhances the data verification process, thereby ensuring the absoluteness and tamper-proofing of data. Through these technical means, a secure and efficient bankruptcy management information storage and processing system can be implemented. This not only protects sensitive information, but also optimizes the data management process and improves the overall performance and security of the system.

[0139] In one embodiment of the present invention, a bankruptcy information management system based on blockchain is provided, wherein the consensus verification module includes:

[0140] The historical data collection module is used to collect historical records and performance indicator data of each node and perform preprocessing;

[0141] The feature extraction module is used to extract and summarize the key features that affect node reputation and performance from the preprocessed data. The key features include consensus success rate, CPU performance, bandwidth, storage capacity, and participation consistency;

[0142] A training model module is used to identify the intrinsic relationship between the key features through a random forest model, and use the trained model to predict the reputation value and performance score of the node data;

[0143] The weight calculation module is used to calculate the weight of each node based on the reputation value and performance score according to the dynamic weight model. Specifically, the dynamic weight model is:

[0144]

[0145] Among them, w i represents the weight of the node, s i Represents the reputation value of the consensus node, P i Indicates the performance score of the consensus node;

[0146] The update weight module is used to dynamically adjust the weight of each node based on a preset time interval. The weight of the consensus node is used to verify transactions within each shard through a dynamic consensus algorithm.

[0147] The working principle and effect of the above technical solution are as follows: collecting and preprocessing node history and performance indicators. This data is collected from each node participating in the blockchain network. This data includes historical behavior records, processing capabilities, network bandwidth, and storage capacity during the consensus process. The collected data first needs to be preprocessed to ensure consistency, completeness, and accuracy. Preprocessing steps include data cleaning, missing value filling, and data standardization. After preprocessing, key features that may affect node reputation and performance are extracted from the data. These key features include: consensus success rate (i.e., the proportion of nodes that successfully participate in the consensus process); CPU performance (i.e., the processing power of the node); bandwidth (i.e., the node's network bandwidth, which determines its data transmission speed); and storage capacity (i.e., the amount of storage space available). A random forest model is used to identify feature relationships and predict node reputation and performance scores. The extracted key features are analyzed using the random forest model to identify the inherent relationships between them. Random forest is an integrated learning algorithm that trains and predicts by building multiple decision trees. First, the random forest model is trained with historical data to enable it to identify the main features that affect node reputation and performance. Then, the trained model is used to predict new node data to obtain the reputation value and performance score of each node. Based on the predicted node reputation value and performance score, the weight of each node is dynamically adjusted according to the preset time interval to adapt to network changes. Based on the adjusted node weight, transactions are verified within each shard through the dynamic consensus algorithm. The dynamic consensus algorithm allocates voting weights or other parameters participating in the consensus according to the weight of each node, ensuring that nodes with high reputation and high performance have a greater influence in the consensus process. Enhance data security and system stability.

[0148] By collecting and analyzing the historical records and performance indicators of nodes, the reputation value and performance score of each node can be accurately evaluated and predicted to ensure a more reliable and stable consensus process; the dynamic adjustment weight mechanism ensures that high-trust and high-performance nodes play a greater role in the consensus process, reducing the negative impact of low-quality nodes on the system, thereby improving the overall stability and security of the system; through dynamic weight models and random forest analysis, a comprehensive investigation and evaluation of key features is achieved, making resource allocation more scientific and reasonable; performance nodes are given greater weights, which can optimize the utilization efficiency of computing resources, improve the system transaction verification and processing speed, and thus improve the overall performance of the system; effectively respond to system changes and optimize adaptive capabilities, dynamically adjust node weights at preset time intervals, and the system can adapt to network changes in a timely manner. The system continuously optimizes its adaptive capabilities based on changes in the network environment and node performance, adjusts consensus participation strategies based on the latest data, and improves the flexibility and efficiency of the consensus algorithm, so that the system can still operate efficiently in the face of different loads and changes in node behavior; improves the transparency and fairness of the consensus process. Through a transparent node evaluation and weight distribution mechanism, the transparency of the consensus process is improved, so that each node has a clear understanding and cognition of its own performance and system distribution. The dynamic adjustment weight mechanism conducts a comprehensive evaluation based on multiple key features, making the weight distribution more fair and scientific, ensuring the fairness of system operation, and optimizing the accuracy and efficiency of transaction verification within the shard: the dynamic consensus algorithm verifies transactions within each shard, ensuring that high-quality nodes within the shard play a greater role and improving the accuracy of transaction verification;

[0149] With the dynamic adjustment of intra-shard weights, the processing efficiency of intra-shard transactions is optimized, and the overall effectiveness of the sharding mechanism is improved.

[0150] Through this technical solution, an efficient, reliable and flexible blockchain system can be built. In complex business environments such as bankruptcy management, it can provide better resource management and consensus-building mechanisms to ensure the security, performance and stability of the system.

[0151] In one embodiment of the present invention, a bankruptcy information management system based on blockchain, the hash verification module includes:

[0152] Generate a hash chain module, which is used after consensus verification to set the initial value H0 of the hash chain and use the wave hash chain generation formula to calculate each hash value in sequence. Specifically, the wave hash chain generation formula is:

[0153] H i =H(H i-1 ⊕T i ⊕WTF(i))

[0154] Among them, H i Represents the current hash value, ⊕ represents the XOR operator, H is the hash function, Ti is the current bankruptcy management information, WTF(i) is the wave function;

[0155] WTF(i)=sin(k·i)+cos(k·i)

[0156] Where i is the index of the current hash chain, indicating the position of the calculated hash value in the hash chain, and k is a constant used to adjust the periodicity and amplitude of the wave function;

[0157] The comparison and verification module is used to recalculate and compare the value of the hash chain when new data is added to the hash chain after each transaction.

[0158] The working principle and effects of the above technical solution are as follows:

[0159] The hash calculation process is as follows:

[0160] 1. First, calculate the initial value H0 of the hash chain;

[0161] 2. For the i-th transaction data T i , calculate the wave function WTF(i).

[0162] 3. The previous hash value H i-1 、Current transaction data T i Perform XOR operation with wave function WTF(i).

[0163] 4. Apply the hash function H to the result of the XOR operation to obtain the current hash value H i .

[0164] Repeat steps 2-4 until all hash values on the current hash chain are calculated.

[0165] 4. Update and comparison of dynamic hash chains

[0166] Every time new transaction data is added to the blockchain, the hash chain is recalculated and compared with the previous one. Any anomalies or inconsistencies in the hash chain indicate possible data tampering or errors, requiring processing and correction. This approach ensures the integrity and security of every transaction data. Improve data security and integrity. Through the generation and calculation of wave hash chains, the hash value of each transaction data is associated with the previous hash value, the current data, and the wave function, increasing the difficulty of data tampering. Even if one data is tampered with, all subsequent hash values will change, immediately indicating data anomalies and ensuring data integrity. Enhance the reliability of data verification. The hash chain calculation method of mathematical functions (such as wave functions) is adopted, so that each hash value depends not only on the hash value of the previous data, but also considers the current data and the results of the mathematical function, making data verification more reliable. The application of this functional relationship makes the generation of hash chains not only dependent on single data content, but also considers periodic changes, providing multiple verification dimensions. Improve the dynamic response capability of the system. When new transaction data is added, the system can quickly recalculate the hash chain and perform comparative verification, allowing the system to dynamically respond and detect data integrity in real time. Compared with traditional static chains, wave hash chains take into account dynamic changes in data and are more flexible. Provide anti-tampering evidence. Abnormal or unauthorized data modifications can only be quickly detected when they directly affect specific data. This provides a lightweight and effective tamper-proofing method. The long-term hash chain history can serve as evidence of data integrity, providing a reliable basis for traceability and dispute resolution. Parameter adjustment enhances flexibility: the constant k in the wave function can be adjusted according to specific needs, varying the periodicity and amplitude of the calculation results, thus enhancing the flexibility and adaptability of the hash chain. This technical solution utilizes a wave-like hash chain generation formula, combined with data XOR and hash functions, to design an efficient, secure, and flexible bankruptcy information management method. This solution not only enhances the security and data integrity of bankruptcy management information, but also improves the overall system's dynamic responsiveness and data verification reliability, making the bankruptcy management process more transparent and efficient.

[0167] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A bankruptcy information management method based on blockchain, characterized in that: The method comprises: Collecting bankruptcy management information during the bankruptcy management process and encrypting the bankruptcy management information multiple times; The encrypted bankruptcy management information is packaged and uploaded to the blockchain, and the sharding mechanism stores different types of data in shards. Dynamically adjust the weight of consensus nodes and verify transactions within each shard through a dynamic consensus algorithm; After consensus verification, an innovative hash chain is constructed using the wave function and further verification is performed; After consensus verification, an innovative hash chain is constructed using the wave function and further verification is performed, including: After consensus verification, the initial value H0 of the hash chain is set, and each hash value is calculated in sequence using the wave hash chain generation formula. Specifically, the wave hash chain generation formula is: Among them, H i Indicates the current hash value, represents the XOR operator, H is the hash function, T i is the current bankruptcy management information, WTF(i) is the wave function; WTF(i)=sin(k·i)+cos(k·i) Where i is the index of the current hash chain, indicating the position of the calculated hash value in the hash chain, and k is a constant used to adjust the periodicity and amplitude of the wave function; When new data is added to the hash chain after each transaction, the value of the hash chain is recalculated and compared.

2. The bankruptcy information management method based on blockchain according to claim 1, characterized in that: Collect bankruptcy management information during the bankruptcy management process and encrypt the bankruptcy management information multiple times, including: Obtain bankruptcy management information T during the bankruptcy management process, symmetrically encrypt the bankruptcy management information T to generate a symmetric key K, and use the symmetric key K to encrypt the bankruptcy management information T to generate a ciphertext EK(T): EK(T) = EncryptSym(T, K); Encrypt the symmetric key K using an asymmetric encryption algorithm to generate a pair of keys, namely a public key and a private key, and encrypt the symmetric key K using the public key: EPK(K)=EncryptAsym(K,PK); Perform homomorphic encryption on the bankruptcy management information T to generate HC(T): HC(T)=HomomorphicEncrypt(T); Then generate the zero-knowledge proof ZK of the bankruptcy management information T: ZK=GenerateProof(T).

3. The bankruptcy information management method based on blockchain according to claim 1, characterized in that: The encrypted bankruptcy management information is packaged and uploaded to the blockchain. The sharding mechanism stores different types of data in shards, including: Package and upload the encrypted bankruptcy management information to the blockchain; After uploading to the blockchain, the entire encrypted bankruptcy management information is divided according to defined data categories to generate multiple sharded data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple sharded data sets include asset information shards, creditor information shards, and debtor information shards; Within each shard, a storage hierarchy is defined, including a light node storage hierarchy, a full node storage hierarchy, and a super node storage hierarchy. Based on historical bankruptcy management information access data, the importance, access frequency, data size, and data sensitivity of bankruptcy management information are marked; A comprehensive score of the data is calculated based on the importance, access frequency, data size and data sensitivity of the bankruptcy management information. When the comprehensive score does not exceed the first preset threshold, it is stored in the light node storage level. When the comprehensive score exceeds the first preset threshold and does not exceed the second preset threshold, it is stored in the full node storage level. When the comprehensive score exceeds the second preset threshold and does not exceed the third preset threshold, it is stored in the super node storage level.

4. The bankruptcy information management method based on blockchain according to claim 1, characterized in that: Dynamically adjust the weight of consensus nodes and verify transactions within each shard through a dynamic consensus algorithm, including: Collect historical records and performance indicator data of each node and perform preprocessing; Extract and summarize the key features that affect node reputation and performance from the preprocessed data. These key features include consensus success rate, CPU performance, bandwidth, storage capacity, and participation consistency. The random forest model is used to identify the intrinsic relationship between the key features, and the trained model is used to predict the reputation value and performance score of the node data; The weight of each node is calculated based on the reputation value and performance score according to the dynamic weight model. Specifically, the dynamic weight model is: Among them, w i represents the weight of the node, s i Represents the reputation value of the consensus node, P i Indicates the performance score of the consensus node; The weight of each node is dynamically adjusted based on a preset time interval, and transactions are verified within each shard through a dynamic consensus algorithm based on the weight of the consensus node.

5. A bankruptcy information management system based on blockchain, characterized in that: The system comprises: An encryption module, used for collecting bankruptcy management information during the bankruptcy management process and encrypting the bankruptcy management information multiple times; The shard storage module is used to package the encrypted bankruptcy management information and upload it to the blockchain. The sharding mechanism stores different types of data in shards. The consensus verification module is used to dynamically adjust the weight of consensus nodes and verify transactions within each shard through a dynamic consensus algorithm; The hash verification module is used to build an innovative hash chain using wave functions after consensus verification and conduct further verification; The hash verification module includes: Generate a hash chain module, which is used after consensus verification to set the initial value H0 of the hash chain and use the wave hash chain generation formula to calculate each hash value in sequence. Specifically, the wave hash chain generation formula is: Among them, H i Represents the current hash value, ⊕ represents the XOR operator, H is the hash function, T i is the current bankruptcy management information, WTF(i) is the wave function; WTF(i)=sin(k·i)+cos(k·i) Where i is the index of the current hash chain, indicating the position of the calculated hash value in the hash chain, and k is a constant used to adjust the periodicity and amplitude of the wave function; The comparison and verification module is used to recalculate and compare the value of the hash chain when new data is added to the hash chain after each transaction.

6. The blockchain-based bankruptcy information management system according to claim 5, characterized in that: The encryption module includes: Obtain bankruptcy management information T during the bankruptcy management process, symmetrically encrypt the bankruptcy management information T to generate a symmetric key K, and use the symmetric key K to encrypt the bankruptcy management information T to generate a ciphertext EK(T): EK(T) = EncryptSym(T, K); Encrypt the symmetric key K using an asymmetric encryption algorithm to generate a pair of keys, namely a public key and a private key, and encrypt the symmetric key K using the public key: EPK(K)=EncryptAsym(K,PK); Perform homomorphic encryption on the bankruptcy management information T to generate HC(T): HC(T)=HomomorphicEncrypt(T); Then generate the zero-knowledge proof ZK of the bankruptcy management information T: ZK=GenerateProof(T).

7. The blockchain-based bankruptcy information management system according to claim 5, characterized in that: The shard storage module includes: The upload module is used to package and upload the encrypted bankruptcy management information to the blockchain; A data partitioning module is used to partition the entire encrypted bankruptcy management information according to defined data categories after uploading to the blockchain to generate multiple sharded data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple sharded data sets include asset information shards, creditor information shards, and debtor information shards; A storage hierarchy definition module is used to define a storage hierarchy within each shard, including a light node storage hierarchy, a full node storage hierarchy, and a super node storage hierarchy; A labeling module is used to label the importance, access frequency, data size and data sensitivity of bankruptcy management information based on historical bankruptcy management information access data; A storage module is used to calculate a comprehensive score of the data based on the importance, access frequency, data size and data sensitivity of the bankruptcy management information; when the comprehensive score does not exceed a first preset threshold, it is stored in a light node storage level; when the comprehensive score exceeds the first preset threshold and does not exceed a second preset threshold, it is stored in a full node storage level; when the comprehensive score exceeds the second preset threshold and does not exceed a third preset threshold, it is stored in a super node storage level.

8. The blockchain-based bankruptcy information management system according to claim 5, characterized in that: The consensus verification module includes: The historical data collection module is used to collect historical records and performance indicator data of each node and perform preprocessing; The feature extraction module is used to extract and summarize the key features that affect node reputation and performance from the preprocessed data. The key features include consensus success rate, CPU performance, bandwidth, storage capacity, and participation consistency; A training model module is used to identify the intrinsic relationship between the key features through a random forest model, and use the trained model to predict the reputation value and performance score of the node data; The weight calculation module is used to calculate the weight of each node based on the reputation value and performance score according to the dynamic weight model. Specifically, the dynamic weight model is: Among them, w i represents the weight of the node, s i Represents the reputation value of the consensus node, P i Indicates the performance score of the consensus node; The update weight module is used to dynamically adjust the weight of each node based on a preset time interval. The weight of the consensus node is used to verify transactions within each shard through a dynamic consensus algorithm.

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