Block chain-based production breaking information management method and system

Abstract

The invention provides a block chain-based production breaking information management method and system, and the method comprises the steps: collecting production breaking management information in a production breaking management process, and carrying out the encryption of the production breaking management information for multiple times; the encrypted production breaking management information is packaged and linked, and a fragmentation mechanism carries out fragmentation storage on different types of data; dynamically adjusting the weight of the consensus node, and carrying out transaction verification in each fragment through a dynamic consensus algorithm; after consensus verification, an innovative Hash chain is constructed by using a wave function, and further verification is carried out. Through the method and the corresponding system, the system performance and the tamper-proof capability of the data can be effectively improved, and the rights and interests of the participants and the integrity of the data are ensured.

Description

Technical Field

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

[0002] In modern economic activities, bankruptcy management is a complex process involving the interests of multiple parties. Traditional bankruptcy management methods rely on centralized management systems and manual processes, and there are many problems and challenges, which seriously affect the efficiency, transparency and security of bankruptcy management. 1. Traditional bankruptcy management systems usually rely on centralized databases for data storage. This centralized storage method has obvious security vulnerabilities, including the risk 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) have problems such as high energy consumption, low efficiency and over-reliance on a single indicator, which cannot guarantee 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 chain, and the sharding mechanism stores different types of data in shards;

[0007] Dynamically adjust the weight of consensus nodes and verify transactions in 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, the bankruptcy management information during the bankruptcy management process is collected and the bankruptcy management information is encrypted multiple times, including:

[0010] Obtain bankruptcy management information T in 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 chain, and the sharding mechanism stores different types of data in shards, including:

[0015] Pack 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 shard data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple shard data sets include asset information shards, creditor information shards, and debtor information shards;

[0017] In each shard, a storage hierarchy is defined, which includes a light node storage hierarchy, a full node storage hierarchy, and a super node storage hierarchy;

[0018] Based on the historical access data of bankruptcy management information, 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 adjusted dynamically, and transaction verification is performed in 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 in the preprocessed data, including: consensus success rate, CPU performance, bandwidth, storage capacity, and participation consistency;

[0023] The intrinsic relationship between the key features is identified through a random forest model, and the reputation value and performance score of the node data are predicted using the trained model;

[0024] According to the dynamic weight model, the weight of each node is calculated based on the reputation value and performance score. 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 by using the wave function and further verification is performed, including:

[0029] After consensus verification, set the initial value H of the hash chain 0 , use the wave hash chain generation formula to calculate each hash value in turn. 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 bankruptcy information management system based on blockchain, the system comprising:

[0036] An encryption module, used to collect bankruptcy management information during the bankruptcy management process and encrypt the bankruptcy management information multiple times;

[0037] The shard storage module is used to package the encrypted bankruptcy management information onto the chain. 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 in 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 and conduct further verification.

[0040] Furthermore, the encryption module includes:

[0041] Obtain bankruptcy management information T in 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 shard data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple shard 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 in each shard, wherein the storage hierarchy includes 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 the access data of historical bankruptcy management information;

[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 the 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 the node reputation and performance in 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, and to verify transactions in each shard through a dynamic consensus algorithm based on the weight of the consensus node.

[0059] Furthermore, the hash verification module includes:

[0060] Generate a hash chain module for consensus verification and set the initial value H of the hash chain 0 , use the wave hash chain generation formula to calculate each hash value in turn. 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 as follows: data security is enhanced, and multiple encryption operations ensure the high security of bankruptcy management information during transmission and storage. Even if one layer of encryption is breached, the remaining layers can still provide protection, which significantly increases the difficulty of data theft and tampering; system performance and scalability are improved, and data sharding storage optimizes the big data processing mechanism. By sharding data of different types or categories and storing them in a distributed manner 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 the responsibility of consensus verification more to efficient and reliable nodes, thereby improving the overall verification speed and accuracy. Independent consensus within the shard further improves the processing power 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 described in the present invention. DETAILED DESCRIPTION

[0068] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0069] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[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 the specification of the present invention herein 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 is 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 chain, and the sharding mechanism stores different types of data in shards;

[0074] Dynamically adjust the weight of consensus nodes and verify transactions in 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 management information is collected and encrypted multiple times. During the bankruptcy management process, the collected information includes sensitive information such as creditor lists, bankruptcy application materials, financial statements, and liquidation progress. First, this information will be encrypted to ensure its security during transmission and storage. The encryption process not only uses standard symmetric and asymmetric encryption technology, but also uses multiple encryption technology to improve the level of data protection. Multiple encryption means that after the initial encryption is completed, the encrypted data is encrypted once or more to increase the complexity and security of decryption. The encrypted bankruptcy management information is packaged on the chain. After encryption, the bankruptcy management information will be packaged into transaction data on the blockchain and then propagated through the blockchain network. Each node in the blockchain verifies and packages these transaction information to form a new block and add the new block to the chain. In this process, in order to ensure the sharding storage of data, different types of data are stored in shards by classifying and dividing the data, which includes sharding by type (such as financial information, legal documents, progress reports, etc.) or by other preset strategies. Dynamically adjust the weight of the consensus node. In order to improve the efficiency and reliability of the consensus process, the system dynamically adjusts the weight of the node. Factors such as the node's online time, processing power, and historical performance will serve as the basis for dynamic weight adjustment. Then, a dynamic consensus algorithm is used to verify transactions within each shard, so that more reliable and high-performance nodes have greater influence in the consensus process, ensuring verification efficiency and accuracy. This algorithm can be adjusted on demand, allowing the system to quickly adapt to different network conditions and node performance changes. Use the wave function to build an innovative hash chain. When the consensus process is completed, the blockchain system will use the wave function to generate a hash chain. The wave function is a nonlinear function with high complexity and anti-attack ability, which can effectively prevent data tampering. After generating the hash chain, the system conducts further data verification and ensures the integrity and consistency of the data chain through multiple checks. Such a hash chain not only increases the security of the system, but also strengthens the tamper-proof ability of blockchain data. 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 broken, the remaining layers can still provide protection, which significantly increases the difficulty of data theft and tampering; improve system performance and scalability, and data shard 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; optimize the consensus mechanism, dynamically adjust the weight of the consensus node, and be able to adjust the participating nodes in the consensus process in real time, and assign the responsibility of consensus verification to more 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, and improving consensus efficiency; enhance 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 be tampered with. At the same time, through multiple verifications of the hash chain, the integrity and consistency of the data are further ensured. In this way, the authenticity and reliability of the chain data can be ensured, tampering can be prevented, and an efficient, secure, and scalable bankruptcy management system can be realized. While ensuring data security and confidentiality, the system performance and data tamper-proof capabilities are effectively improved through technologies such as shard storage, dynamic consensus, and innovative hash chains, ensuring the rights and interests of all participants and the integrity of data. Such a system design can better cope with the various complexities and challenges in bankruptcy management and achieve efficient, secure, and reliable business processing.

[0077] An 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 in 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: Obtain bankruptcy management information and perform multi-layer encryption on it. First, collect information in the bankruptcy management process from different sources, denoted as T. In order to ensure the security of data during transmission and storage, the following encryption operations are performed: Symmetric encryption: Use a symmetric encryption algorithm (such as AES) to generate a symmetric key K. Then, use K to encrypt the bankruptcy management information T to generate a ciphertext EK(T). This encryption method is fast and efficient, but it requires secure key distribution. In order 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. Use the public key PK to encrypt the symmetric key K to generate EPK(K): EPK(K) = EncryptAsym(K,PK). Asymmetric encryption is very effective in the key exchange process, although it is slow. In order to calculate the data in an encrypted state, the bankruptcy management information T is encrypted using a homomorphic encryption algorithm to generate HC(T): HC(T) = HomomorphicEncrypt(T). Homomorphic encryption allows computing operations to be performed directly on encrypted data, which improves confidentiality and security. To prove certain properties or characteristics of T without revealing the information of T itself, a zero-knowledge proof ZK is generated: ZK = GenerateProof(T). Zero-knowledge proof ensures the correctness and integrity of the data without revealing the actual data. To improve data security and confidentiality, multi-layer encryption methods are used. Bankruptcy management information can be protected in multiple ways during transmission and storage. Not only is the content encrypted, but the symmetric key is also processed by asymmetric encryption to ensure its secure transmission. Homomorphic encryption allows direct operations on encrypted data to ensure the confidentiality of data during the calculation process. Zero-knowledge proof technology ensures the correctness and integrity of data without exposing the actual data content.

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

[0084] Pack 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 shard data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple shard data sets include asset information shards, creditor information shards, and debtor information shards;

[0086] In each shard, a storage hierarchy is defined, which includes a light node storage hierarchy, a full node storage hierarchy, and a super node storage hierarchy;

[0087] Based on the historical access data of bankruptcy management information, 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] Set scoring criteria for each attribute. Importance (Importance, I): 1: Minor, 2: General, 3: Important, 4: Very important, 5: Extremely important. Access frequency (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 (Data Size, D): 1: Less than 1MB, 2: 1MB-10MB, 3: 10MB-100MB, 4: 100MB-1GB, 5: More than 1GB. Data sensitivity (Sensitivity, S): 1: General data, 2: Lower sensitivity data, 3: Medium sensitivity data, 4: Higher sensitivity data, 5: Highly sensitive data. According to the access data and scoring criteria of historical bankruptcy management information, bankruptcy management information is labeled, and a comprehensive scoring formula is designed, considering the weight of each attribute.

[0090] Score=α·I+β·A+γ·D+δ·S,

[0091] Among them, Score represents the score, α, β, γ and δ represent weight coefficients. For example, the following weights are set: α = 0.4 (importance) β = 0.3 (access frequency) γ = 0.2 (data size) δ = 0.1 (data sensitivity), and the score threshold of each storage layer is determined. Assume that the thresholds are as follows: 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, the 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 the data category (asset information, creditor information, debtor information), and multiple shard data sets are generated: asset information shards, creditor information shards and debtor information shards. In each shard, a storage level is defined, including a light node storage level, a full node storage level and a super node storage level. According to 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. According to different comprehensive scores, it 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 threshold and the second threshold, it is stored in the full node level; if the comprehensive score is between the second threshold and the third threshold, 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 reasonably divided into different sharding data sets, which improves data management efficiency. Different storage levels are divided according to the comprehensive score of the data, ensuring that high-frequency and sensitive data are stored on nodes with higher security levels, while low-frequency and insensitive data are stored on light nodes, which optimizes resource allocation and use. Enhance the scalability and performance of the system. The use of the sharding storage mechanism can disperse the pressure of data storage and improve the efficiency of storage and query. Dynamically adjust the storage level to ensure that the system can adapt to different data needs and changes, and improve the flexibility and scalability of the system. Ensure the integrity and tamper-proof ability of data. The immutability of blockchain combined with multi-layer encryption technology makes it difficult to tamper with the uploaded data, ensuring the integrity of the data. The use of zero-knowledge proof further enhances the data verification process, thereby ensuring the absoluteness and tamper-proof ability of the data. Through these technical means, a safe and efficient bankruptcy management information storage and processing system can be realized. 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 in each shard through 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 in the preprocessed data, including: consensus success rate, CPU performance, bandwidth, storage capacity, and participation consistency;

[0096] The intrinsic relationship between the key features is identified through a random forest model, and the reputation value and performance score of the node data are predicted using the trained model;

[0097] According to the dynamic weight model, the weight of each node is calculated based on the reputation value and performance score. 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 nodes in the process of participating in the blockchain consensus. Specifically, it refers to whether the node can participate in the consensus process continuously, on time, and reliably, and whether its performance is stable. It includes the following aspects: Uptime: the proportion of time the node remains online in the network, Response Time: the speed at which the node responds to consensus requests. Nodes with high participation consistency should respond within the specified time, Stability: whether the running status of the node is stable, and the proportion of stoppages or restarts in the consensus process, Participation Rate: the frequency of the node participating in the consensus process. Nodes with high participation consistency should participate in the consensus process stably and persistently, and Network Latency: the communication delay between the node and other nodes. Nodes with high participation consistency should have lower network latency.

[0102] The working principle and effect of the above technical solution are: collect and preprocess the historical records and performance indicators of the nodes, collect the historical records and performance indicator data from each node participating in the blockchain network, these data include the historical behavior records, processing capabilities, network bandwidth and storage capacity of the nodes in the consensus process, etc. The collected data first needs to be preprocessed to ensure the consistency, completeness and accuracy of the data. The preprocessing steps include data cleaning, missing value filling, data standardization, etc. After the preprocessing is completed, extract the key features that may affect the reputation and performance of the nodes from the data. These key features include: consensus success rate, that is, the proportion of nodes successfully participating in the consensus process, CPU performance, that is, the processing power of the node, bandwidth, that is, the network bandwidth of the node, which determines its data transmission speed, storage capacity, that is, the size of the storage space of the node, identify the feature relationship through the random forest model and predict the node reputation value and performance score, and use the random forest model to analyze the extracted key features and identify the internal relationship 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, transaction verification is performed in 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. By collecting and analyzing the historical records and performance indicators of nodes, the reputation and performance score of each node can be accurately evaluated and predicted to ensure a more reliable and stable consensus process. The dynamic weight adjustment 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 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 the network environment and node performance without any delays. Continuously optimize the adaptive ability, adjust the consensus participation strategy according to the latest data, improve the flexibility and efficiency of the consensus algorithm, so that the system can still maintain efficient operation when facing different loads and node behavior changes; improve the transparency and fairness of the consensus process, through transparent node evaluation and weight distribution mechanism, improve the transparency of the consensus process, so that each node has a clear understanding and cognition of its own performance and system allocation, and dynamically adjust the weight mechanism based on multiple key features for comprehensive evaluation, making the weight distribution more fair and scientific, ensuring the fairness of the 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; with the dynamic adjustment of weights within the shard, the processing efficiency of transactions within the shard is optimized, and the overall effectiveness of the sharding mechanism is improved. Through this technical solution, an efficient, reliable and flexible blockchain system can be built, which can provide better resource management and consensus-reaching mechanisms in complex business environments such as bankruptcy management, and ensure the security, performance and stability of the system.

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

[0104] After consensus verification, set the initial value H of the hash chain 0 , use the wave hash chain generation formula to calculate each hash value in turn. 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, Ti is 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 effect of the above technical solution are:

[0111] The hash calculation process is as follows:

[0112] 1. First, calculate the initial value H of the hash chain 0 ;

[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 XOR operation is performed with the wave function WTF(i).

[0115] 4. Apply the hash function H to the result of the XOR operation to get 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 value of the hash chain is recalculated and compared with the previous hash chain. If there is an anomaly or inconsistency in the hash chain, it means that the data may have been tampered with or an error has occurred, and needs to be processed and corrected. This method ensures the integrity and security of each 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, current data and wave functions, which increases the difficulty of tampering with data. Even if one data is tampered with, all subsequent hash values ​​will change, immediately prompting data anomalies to ensure 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 on the current data and the results of mathematical functions, making data verification more reliable. The application of this functional relationship makes the generation of hash chains not only dependent on a single data content, but also takes into account 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, so that the system can respond dynamically and detect the integrity of the data in real time. Compared with traditional static chains, wave hash chains take into account the dynamic changes of 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 anti-tampering method. The long-term hash chain history can be used as evidence of data integrity, providing a reliable basis for traceability and disputes. Parameter adjustment improves flexibility. The constant k in the wave function can be adjusted according to specific needs to change the periodicity and amplitude of the calculation results, making the flexibility and adaptability of the hash chain enhanced. This technical solution uses 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 improves the security and data integrity of bankruptcy management information, but also improves the overall dynamic response capability of the system and the reliability of data verification, making the bankruptcy management process more transparent and efficient.

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

[0120] An encryption module, used to collect bankruptcy management information during the bankruptcy management process and encrypt the bankruptcy management information multiple times;

[0121] The shard storage module is used to package the encrypted bankruptcy management information onto the chain. 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 in 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 and conduct further verification.

[0124] The working principle and effect of the above technical solution are as follows: bankruptcy management information is collected and encrypted multiple times. During the bankruptcy management process, the collected information includes sensitive information such as creditor lists, bankruptcy application materials, financial statements, and liquidation progress. First, this information will be encrypted to ensure its security during transmission and storage. The encryption process not only uses standard symmetric and asymmetric encryption technology, but also uses multiple encryption technology to improve the level of data protection. Multiple encryption means that after the initial encryption is completed, the encrypted data is encrypted once or more to increase the complexity and security of decryption. The encrypted bankruptcy management information is packaged on the chain. After encryption, the bankruptcy management information will be packaged into transaction data on the blockchain and then propagated through the blockchain network. Each node in the blockchain verifies and packages these transaction information to form a new block and add the new block to the chain. In this process, in order to ensure the sharding storage of data, different types of data are stored in shards by classifying and dividing the data, which includes sharding by type (such as financial information, legal documents, progress reports, etc.) or by other preset strategies. Dynamically adjust the weight of the consensus node. In order to improve the efficiency and reliability of the consensus process, the system dynamically adjusts the weight of the node. Factors such as the node's online time, processing power, and historical performance will serve as the basis for dynamic weight adjustment. Then, a dynamic consensus algorithm is used to verify transactions within each shard, so that more reliable and high-performance nodes have greater influence in the consensus process, ensuring verification efficiency and accuracy. This algorithm can be adjusted on demand, allowing the system to quickly adapt to different network conditions and node performance changes. Use the wave function to build an innovative hash chain. When the consensus process is completed, the blockchain system will use the wave function to generate a hash chain. The wave function is a nonlinear function with high complexity and anti-attack ability, which can effectively prevent data tampering. After generating the hash chain, the system conducts further data verification and ensures the integrity and consistency of the data chain through multiple checks. Such a hash chain not only increases the security of the system, but also strengthens the tamper-proof ability of blockchain data. 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 broken, the remaining layers can still provide protection, which significantly increases the difficulty of data theft and tampering; improve system performance and scalability, and data shard 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; optimize the consensus mechanism, dynamically adjust the weight of the consensus node, and be able to adjust the participating nodes in the consensus process in real time, and assign the responsibility of consensus verification to more 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, and improving consensus efficiency; enhance 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 be tampered with. At the same time, through multiple verifications of the hash chain, the integrity and consistency of the data are further ensured. In this way, the authenticity and reliability of the chain data can be ensured, tampering can be prevented, and an efficient, secure, and scalable bankruptcy management system can be realized. While ensuring data security and confidentiality, the system performance and data tamper-proof capabilities are effectively improved through technologies such as shard storage, dynamic consensus, and innovative hash chains, ensuring the rights and interests of all participants and the integrity of data. Such a system design can better cope with the various complexities and challenges in bankruptcy management and achieve efficient, secure, and reliable business processing.

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

[0126] Obtain bankruptcy management information T in 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: Obtain bankruptcy management information and perform multi-layer encryption on it. First, collect information in the bankruptcy management process from different sources, denoted as T. In order to ensure the security of data during transmission and storage, the following encryption operations are performed: Symmetric encryption: Use a symmetric encryption algorithm (such as AES) to generate a symmetric key K. Then, use K to encrypt the bankruptcy management information T to generate a ciphertext EK(T). This encryption method is fast and efficient, but it requires secure key distribution. In order 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. Use the public key PK to encrypt the symmetric key K to generate EPK(K): EPK(K) = EncryptAsym(K,PK). Asymmetric encryption is very effective in the key exchange process, although it is slow. In order to calculate the data in an encrypted state, the bankruptcy management information T is encrypted using a homomorphic encryption algorithm to generate HC(T): HC(T) = HomomorphicEncrypt(T). Homomorphic encryption allows computing operations to be performed directly on encrypted data, which improves confidentiality and security. To prove certain properties or characteristics of T without revealing the information of T itself, a zero-knowledge proof ZK is generated: ZK = GenerateProof(T). Zero-knowledge proof ensures the correctness and integrity of the data without revealing the actual data. To improve data security and confidentiality, multi-layer encryption methods are used. Bankruptcy management information can be protected in multiple ways during transmission and storage. Not only is the content encrypted, but the symmetric key is also processed by asymmetric encryption to ensure its secure transmission. Homomorphic encryption allows direct operations on encrypted data to ensure the confidentiality of data during the calculation process. Zero-knowledge proof technology ensures the correctness and integrity of data without exposing the actual data content.

[0131] In one embodiment of the present invention, a bankruptcy information management system based on blockchain, 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 shard data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple shard 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 in each shard, wherein the storage hierarchy includes 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 the access data of historical bankruptcy management information;

[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, the 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 the data category (asset information, creditor information, debtor information), and multiple shard data sets are generated: asset information shards, creditor information shards and debtor information shards. In each shard, a storage level is defined, including a light node storage level, a full node storage level and a super node storage level. According to 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. According to different comprehensive scores, it 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 threshold and the second threshold, it is stored in the full node level; if the comprehensive score is between the second threshold and the third threshold, 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 reasonably divided into different sharding data sets, which improves data management efficiency. Different storage levels are divided according to the comprehensive score of the data, ensuring that high-frequency and sensitive data are stored on nodes with higher security levels, while low-frequency and insensitive data are stored on light nodes, which optimizes resource allocation and use.

[0138] Enhance the scalability and performance of the system. The use of shard storage mechanism can disperse the pressure of data storage and improve the efficiency of storage and query. Dynamically adjust the storage level to ensure that the system can adapt to different data needs and changes, and improve the flexibility and scalability of the system. Ensure the integrity and tamper-proof ability of the data. The immutability of the blockchain combined with multi-layer encryption technology makes it difficult to tamper with the uploaded data, ensuring the integrity of the data. The use of zero-knowledge proof further enhances the data verification process, thereby ensuring the absoluteness and tamper-proof ability of the data. Through these technical means, a safe and efficient bankruptcy management information storage and processing system can be realized. 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, the consensus verification module includes:

[0140] The historical data collection module is used to collect the 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 the node reputation and performance in 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, and to verify transactions in each shard through a dynamic consensus algorithm based on the weight of the consensus node.

[0147] The working principle and effect of the above technical solution are: collect and preprocess the historical records and performance indicators of the nodes, collect the historical records and performance indicator data from each node participating in the blockchain network, these data include the historical behavior records, processing capabilities, network bandwidth and storage capacity of the nodes in the consensus process, etc. The collected data first needs to be preprocessed to ensure the consistency, completeness and accuracy of the data. The preprocessing steps include data cleaning, missing value filling, data standardization, etc. After the preprocessing is completed, extract the key features that may affect the reputation and performance of the nodes from the data. These key features include: consensus success rate, that is, the proportion of nodes successfully participating in the consensus process, CPU performance, that is, the processing power of the node, bandwidth, that is, the network bandwidth of the node, which determines its data transmission speed, storage capacity, that is, the size of the storage space of the node, identify the feature relationship through the random forest model and predict the node reputation value and performance score, and use the random forest model to analyze the extracted key features and identify the internal relationship 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 in 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 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 can be achieved, making resource allocation more scientific and reasonable. Performance nodes are given greater weights, which can optimize the utilization efficiency of computing resources, improve 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. Changes in network environment and node performance, continuously optimize adaptive capabilities, adjust consensus participation strategies according to the latest data, improve the flexibility and efficiency of the consensus algorithm, so that the system can still maintain efficient operation when facing different loads and changes in node behavior; improve the transparency and fairness of the consensus process. Through transparent node evaluation and weight allocation mechanisms, the transparency of the consensus process is improved, so that each node has a clear understanding and cognition of its own performance and system allocation. The dynamic adjustment weight mechanism conducts a comprehensive evaluation based on multiple key features, making the weight allocation 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 performance of the sharding mechanism is improved.

[0150] Through this technical solution, an efficient, reliable and flexible blockchain system can be built, which can provide better resource management and consensus-building mechanisms in complex business environments such as bankruptcy management, ensuring 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 for consensus verification and set the initial value H of the hash chain 0 , use the wave hash chain generation formula to calculate each hash value in turn. Specifically, the wave hash chain generation formula is:

[0153] H i =H(H i-1 ⊕T i ⊕WTF(i))

[0154] Among them, H irepresents 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;

[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 effect of the above technical solution are:

[0159] The hash calculation process is as follows:

[0160] 1. First, calculate the initial value H of the hash chain 0 ;

[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 XOR operation is performed with the wave function WTF(i).

[0163] 4. Apply the hash function H to the result of the XOR operation to get 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 value of the hash chain is recalculated and compared with the previous hash chain. If there is an anomaly or inconsistency in the hash chain, it means that the data may have been tampered with or an error has occurred, and needs to be processed and corrected. This method ensures the integrity and security of each 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, current data and wave functions, which increases the difficulty of tampering with data. Even if one data is tampered with, all subsequent hash values ​​will change, immediately prompting data anomalies to ensure 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 on the current data and the results of mathematical functions, making data verification more reliable. The application of this functional relationship makes the generation of hash chains not only dependent on a single data content, but also takes into account 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, so that the system can respond dynamically and detect the integrity of the data in real time. Compared with traditional static chains, wave hash chains take into account the dynamic changes of 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 anti-tampering method. The long-term hash chain history can be used as evidence of data integrity, providing a reliable basis for traceability and disputes. Parameter adjustment improves flexibility. The constant k in the wave function can be adjusted according to specific needs to change the periodicity and amplitude of the calculation results, making the flexibility and adaptability of the hash chain enhanced. This technical solution uses 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 improves the security and data integrity of bankruptcy management information, but also improves the overall dynamic response capability of the system and the reliability of data verification, making the bankruptcy management process more transparent and efficient.

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

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 chain, and the sharding mechanism stores different types of data in shards; Dynamically adjust the weight of consensus nodes and verify transactions in each shard through a dynamic consensus algorithm; After consensus verification, an innovative hash chain is constructed by using the wave function and further verification is performed.

2. According to claim 1, a bankruptcy information management method based on blockchain is 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 in 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. According to the blockchain-based bankruptcy information management method of claim 1, it is characterized in that: The encrypted bankruptcy management information is packaged and uploaded to the chain. The sharding mechanism stores different types of data in shards, including: Pack 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 shard data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple shard data sets include asset information shards, creditor information shards, and debtor information shards; In each shard, a storage hierarchy is defined, which includes a light node storage hierarchy, a full node storage hierarchy, and a super node storage hierarchy; Based on the historical access data of bankruptcy management information, 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. According to the bankruptcy information management method based on blockchain in claim 1, it is characterized in that: Dynamically adjust the weight of consensus nodes and verify transactions in 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 in the preprocessed data, including: consensus success rate, CPU performance, bandwidth, storage capacity, and participation consistency; The intrinsic relationship between the key features is identified through a random forest model, and the reputation value and performance score of the node data are predicted using the trained model; According to the dynamic weight model, the weight of each node is calculated based on the reputation value and performance score. 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. According to the blockchain-based bankruptcy information management method of claim 1, it is characterized in that: After consensus verification, an innovative hash chain is constructed by 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.

6. A bankruptcy information management system based on blockchain, characterized in that: The system comprises: An encryption module, used to collect bankruptcy management information during the bankruptcy management process and encrypt the bankruptcy management information multiple times; The shard storage module is used to package the encrypted bankruptcy management information onto the chain. 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 in each shard through a dynamic consensus algorithm; The hash verification module is used to build an innovative hash chain after consensus verification by using the wave function and conduct further verification.

7. A bankruptcy information management system based on blockchain according to claim 6, characterized in that: The encryption module comprises: Obtain bankruptcy management information T in 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).

8. A bankruptcy information management system based on blockchain according to claim 6, 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 shard data sets, wherein the defined data categories include asset information, creditor information, and debtor information, and the multiple shard 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 in each shard, wherein the storage hierarchy includes 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 the access data of historical bankruptcy management information; 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.

9. A bankruptcy information management system based on blockchain according to claim 6, characterized in that: The consensus verification module includes: The historical data collection module is used to collect the 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 the node reputation and performance in 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, and to verify transactions in each shard through a dynamic consensus algorithm based on the weight of the consensus node.

10. A bankruptcy information management system based on blockchain according to claim 6, characterized in that: The hash verification module includes: Generate a hash chain module, which is used to set the initial value H0 of the hash chain after consensus verification, and use the wave hash chain generation formula to calculate each hash value in turn. 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; 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.

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