Digital asset management system and method based on block chain

Abstract

The invention discloses a digital asset management system and method based on a block chain, and particularly relates to the technical field of digital asset management. Extracting a target transaction block set according to a transaction screening rule, and generating an abnormal transaction block set through an abnormal transaction behavior detection model; analyzing a transaction link path between transaction blocks in the abnormal transaction block set, constructing an on-chain abnormal path graph, and outputting a first-level abnormal risk result based on propagation intensity and aggregation degree evaluation; screening a reference block chain account book, generating a cross-chain abnormal difference set based on the characteristic difference, and outputting a second-level abnormal risk result; and evaluating the on-chain coverage rate of the abnormal path by using the integrity verification matrix, and quantifying the propagation range of the abnormal transaction in combination with the link distribution characteristics. And the first-level and second-level abnormal risk results and the integrity evaluation result are integrated, an on-chain early warning protocol is triggered through the smart contract, and a digital asset abnormal alarm is generated, so that the security of block chain digital asset management is improved, and the method is suitable for asset protection in a block chain transaction scene.

Description

Technical Field

[0001] The present invention relates to the technical field of digital asset management, and more specifically, to a digital asset management system and method based on blockchain. Background Art

[0002] Abnormal transaction behaviors in current blockchain networks, such as abnormal fund transfer paths, frequent transaction node aggregation, and irregular cross-chain operations, have become the main source of risk in digital asset management. Existing risk management methods mainly rely on single-dimensional data analysis, without comprehensive consideration of transaction characteristics, path characteristics, and cross-chain associations, and have low accuracy in risk identification of complex transaction behaviors. In addition, most methods lack real-time warning and response capabilities, and are unable to quickly respond to high-risk transactions, increasing the possibility of illegal transfer of digital assets.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a blockchain-based digital asset management system and method to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A digital asset management method based on blockchain, comprising the following steps:

[0007] In the target blockchain ledger, according to the preset transaction screening rules, a target transaction block set that meets the transaction frequency and transaction amount characteristics is extracted;

[0008] Based on the abnormal transaction behavior detection model, the transaction characteristics in the target transaction block set are analyzed to generate a normal transaction block set and an abnormal transaction block set;

[0009] Analyze the transaction link paths between transaction blocks in the abnormal transaction block set and build an abnormal path graph on the chain;

[0010] Based on the on-chain abnormal path diagram, the propagation intensity and abnormal aggregation degree between transaction chains are evaluated, and the first-level abnormal risk results are output;

[0011] Extract a reference blockchain ledger that is logically associated with the target blockchain ledger, and filter out the corresponding reference transaction block according to the path position of the abnormal transaction block in the target transaction block set;

[0012] Compare the characteristic differences between the abnormal transaction block and the reference transaction block, generate a cross-chain abnormal difference set, and output the secondary abnormal risk results based on the characteristic overlap;

[0013] Between the target blockchain ledger and the reference blockchain ledger, an integrity verification matrix of abnormal transaction blocks is constructed; based on the integrity verification matrix, the on-chain coverage of the abnormal path is calculated, and the abnormal integrity assessment result is output;

[0014] By combining the first-level abnormal risk results, second-level abnormal risk results and abnormal integrity assessment results, the on-chain early warning protocol is triggered through the smart contract to generate a digital asset abnormality alert.

[0015] In a preferred embodiment, in the target blockchain account book, according to the preset transaction screening rules, a target transaction block set that meets the transaction frequency and transaction amount characteristics is extracted, specifically:

[0016] Obtain the on-chain transaction data of the target blockchain ledger, and divide the transaction records according to the time window to obtain the on-chain transaction records;

[0017] Set transaction screening rules, including transaction frequency, transaction amount range, and transaction type;

[0018] Match the on-chain transaction records with the transaction screening rules and extract the target transaction blocks that meet the transaction screening rules;

[0019] The target transaction blocks are clustered and integrated according to the time sequence and characteristic indicators to form a target transaction block set.

[0020] In a preferred embodiment, based on the abnormal transaction behavior detection model, the transaction characteristics in the target transaction block set are analyzed to generate a normal transaction block set and an abnormal transaction block set, specifically:

[0021] Obtain the on-chain transaction characteristic data of each transaction block in the target transaction block set, including transaction amount distribution, transaction frequency, and transaction path topology;

[0022] Input the transaction characteristic data into the abnormal transaction behavior detection model, and evaluate the degree of characteristic matching according to the abnormal behavior feature library preset in the abnormal transaction behavior detection model;

[0023] According to the matching results, the transaction blocks are divided into normal transaction blocks and abnormal transaction blocks:

[0024] Mark the transaction blocks that match normal behavior characteristics as normal transaction blocks;

[0025] Marking transaction blocks matching abnormal behavior characteristics as abnormal transaction blocks;

[0026] The marking results are counted to generate normal transaction block sets and abnormal transaction block sets.

[0027] In a preferred embodiment, the transaction link paths between transaction blocks in the abnormal transaction block set are parsed to construct an abnormal path graph on the chain, specifically:

[0028] Extract the on-chain associated transaction records of each abnormal transaction block from the abnormal transaction block set, including the source address, target address, and transaction time;

[0029] Based on the extracted on-chain associated transaction records, the transaction links between abnormal transaction blocks are analyzed to build the transfer relationship between nodes;

[0030] Sort abnormal transaction links in chronological order and generate preliminary on-chain transaction paths;

[0031] According to the correlation frequency, number of jumps and link distribution of the preliminary on-chain transaction path, the preliminary on-chain transaction path is optimized to form an abnormal path set;

[0032] The abnormal path set is represented as a graph structure to construct an on-chain abnormal path graph.

[0033] In a preferred embodiment, based on the on-chain abnormal path diagram, the propagation intensity and abnormal aggregation degree between transaction chains are evaluated, and the first-level abnormal risk result is output, specifically:

[0034] Extract key characteristic data of each path from the abnormal path graph on the chain, including path length, transaction frequency, node association, and number of transfers;

[0035] Evaluate the transaction propagation intensity of abnormal paths based on the number of transfers and transaction frequency of abnormal paths;

[0036] The aggregation degree of the abnormal path is calculated based on the concentration of nodes in the abnormal path and the consistency of the transaction pattern;

[0037] Combine the propagation intensity and aggregation degree of abnormal paths to comprehensively score the abnormal path risks;

[0038] The abnormal paths whose comprehensive scores exceed the preset risk score threshold are marked as first-level abnormal paths, and the first-level abnormal risk results are output.

[0039] In a preferred embodiment, a reference blockchain ledger that is logically associated with the target blockchain ledger is extracted, and the corresponding reference transaction block is screened out according to the path position of the abnormal transaction block in the target transaction block set, specifically:

[0040] Extract reference blockchain ledgers related to the target blockchain ledger from the multi-chain database, calculate the logical correlation based on the on-chain characteristics of the ledger, and select the reference ledger with the highest logical correlation;

[0041] Based on the on-chain path position of the abnormal transaction block in the target transaction block set, the transaction blocks with the same or similar path position in the reference blockchain ledger are extracted to generate a preliminary reference transaction block set;

[0042] Match the path characteristics of the preliminary reference transaction block set with the path characteristics of the abnormal transaction block, remove the transaction blocks that do not meet the similarity conditions, and generate the final reference transaction block set;

[0043] The final reference transaction block set is marked as the reference transaction block corresponding to the abnormal transaction block in the target transaction block set.

[0044] In a preferred embodiment, the characteristic difference between the abnormal transaction block and the reference transaction block is compared to generate a cross-chain abnormal difference set, and the secondary abnormal risk result is output based on the characteristic overlap, specifically:

[0045] Extract characteristic data of abnormal transaction blocks from the target transaction block set;

[0046] Extracting characteristic data corresponding to the abnormal transaction block from the reference transaction block set, including transaction characteristic data of the same dimension;

[0047] Compare the characteristic data of the abnormal transaction block with the reference transaction block to obtain the difference in characteristic dimensions;

[0048] Summarize all feature differences and generate a cross-chain abnormal difference set;

[0049] Based on the cross-chain abnormal difference set, the characteristic overlap between the abnormal transaction block and the reference transaction block is obtained;

[0050] Marking abnormal transaction blocks with feature overlap lower than a preset threshold as secondary abnormal transaction blocks;

[0051] Summarize all secondary abnormal transaction blocks and generate secondary abnormal risk results.

[0052] In a preferred embodiment, an integrity verification matrix of abnormal transaction blocks is constructed between the target blockchain ledger and the reference blockchain ledger; based on the integrity verification matrix, the on-chain coverage of the abnormal path is calculated, and the abnormal integrity assessment result is output, specifically:

[0053] Extracting transaction path data related to abnormal transaction blocks in the target blockchain ledger and the reference blockchain ledger;

[0054] Map the path nodes and transaction relationships corresponding to the abnormal transaction blocks into rows and columns of the matrix;

[0055] Fill the matrix according to the transaction relationship between nodes to form the integrity verification matrix of abnormal transaction blocks;

[0056] According to the node coverage of abnormal transaction paths in the integrity verification matrix, calculate the on-chain coverage rate of abnormal paths;

[0057] Based on the calculation results of on-chain coverage and the distribution characteristics of abnormal paths, the impact range of abnormal transaction blocks propagating on the chain is evaluated;

[0058] The paths whose on-chain coverage is lower than the set threshold are marked as locally affected paths; the paths whose on-chain coverage is higher than the threshold are marked as fully affected paths, and the abnormal integrity assessment results are output.

[0059] In a preferred embodiment, the first-level abnormal risk results, the second-level abnormal risk results and the abnormal integrity assessment results are integrated to trigger the on-chain early warning protocol through the smart contract to generate a digital asset abnormal alarm, specifically:

[0060] Based on the first-level abnormal risk results, second-level abnormal risk results and abnormal integrity assessment results, a multi-dimensional risk scoring model is established to conduct comprehensive risk scoring on all abnormal transaction blocks and paths;

[0061] Based on the comprehensive risk score, abnormal transaction blocks and paths that exceed the preset alarm threshold are input into the smart contract;

[0062] The smart contract triggers the corresponding on-chain early warning protocol based on the scoring results and generates an abnormal digital asset alert.

[0063] On the other hand, the present invention provides a digital asset management system based on blockchain, including a transaction screening module, an anomaly detection module, a path parsing module, a primary risk assessment module, a reference ledger matching module, a secondary risk assessment module, an integrity verification module and an intelligent early warning module;

[0064] Transaction screening module: In the target blockchain ledger, according to the preset transaction screening rules, extract the target transaction block set that meets the transaction frequency and transaction amount characteristics;

[0065] Anomaly detection module: Based on the abnormal transaction behavior detection model, it performs feature analysis on the transaction characteristics in the target transaction block set to generate normal transaction block sets and abnormal transaction block sets;

[0066] Path parsing module: parses the transaction link paths between transaction blocks in the abnormal transaction block set and builds an abnormal path graph on the chain;

[0067] Level 1 risk assessment module: Based on the on-chain abnormal path diagram, it evaluates the propagation intensity and abnormal aggregation degree between transaction chains and outputs the level 1 abnormal risk results;

[0068] Reference ledger matching module: extracts reference blockchain ledgers that are logically associated with the target blockchain ledger, and selects corresponding reference transaction blocks according to the path position of the abnormal transaction blocks in the target transaction block set;

[0069] Secondary risk assessment module: Reference ledger matching module: compares the characteristic difference between the abnormal transaction block and the reference transaction block, generates a cross-chain abnormal difference set, and outputs the secondary abnormal risk result based on the characteristic overlap;

[0070] Integrity verification module: Integrity verification module: constructs an integrity verification matrix of abnormal transaction blocks between the target blockchain ledger and the reference blockchain ledger; based on the integrity verification matrix, calculates the on-chain coverage of the abnormal path and outputs the abnormal integrity assessment result;

[0071] Intelligent early warning module: Based on the first-level abnormal risk results, second-level abnormal risk results and abnormal integrity assessment results, the on-chain early warning protocol is triggered through smart contracts to generate digital asset abnormal alarms.

[0072] The technical effects and advantages of a blockchain-based digital asset management system and method of the present invention are as follows:

[0073] The target transaction block set is extracted through preset screening rules, and the normal and abnormal transaction blocks are quickly distinguished based on the abnormal transaction behavior detection model. By constructing an on-chain abnormal path diagram, combined with the propagation intensity and aggregation evaluation, the first-level abnormal risk results are output, which improves the accuracy of path risk identification; by screening the logically associated reference blockchain ledger, the characteristic differences between the abnormal transaction block and the reference transaction block are compared, and a cross-chain abnormal difference set is generated to further refine risk identification; combined with the integrity verification matrix of the abnormal transaction block, the on-chain coverage is quantified and the propagation range of the abnormal path is evaluated to ensure the comprehensiveness of risk assessment. The on-chain early warning protocol is triggered by the smart contract to generate digital asset abnormal alarms, realize real-time and efficient risk warning and response, and improve the security and intelligence level of blockchain asset management. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a schematic diagram of a digital asset management method based on blockchain in the present invention;

[0075] Figure 2 This is a structural schematic diagram of a blockchain-based digital asset management system of the present invention. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0077] Example 1

[0078] Figure 1The present invention provides a digital asset management method based on blockchain, which includes the following steps:

[0079] In the target blockchain ledger, according to the preset transaction screening rules, a target transaction block set that meets the transaction frequency and transaction amount characteristics is extracted;

[0080] Based on the abnormal transaction behavior detection model, the transaction characteristics in the target transaction block set are analyzed to generate a normal transaction block set and an abnormal transaction block set;

[0081] Analyze the transaction link paths between transaction blocks in the abnormal transaction block set and build an abnormal path graph on the chain;

[0082] Based on the on-chain abnormal path diagram, the propagation intensity and abnormal aggregation degree between transaction chains are evaluated, and the first-level abnormal risk results are output;

[0083] Extract a reference blockchain ledger that is logically associated with the target blockchain ledger, and filter out the corresponding reference transaction block according to the path position of the abnormal transaction block in the target transaction block set;

[0084] Compare the characteristic differences between the abnormal transaction block and the reference transaction block, generate a cross-chain abnormal difference set, and output the secondary abnormal risk results based on the characteristic overlap;

[0085] Between the target blockchain ledger and the reference blockchain ledger, an integrity verification matrix of abnormal transaction blocks is constructed; based on the integrity verification matrix, the on-chain coverage of the abnormal path is calculated, and the abnormal integrity assessment result is output;

[0086] By combining the first-level abnormal risk results, second-level abnormal risk results and abnormal integrity assessment results, the on-chain early warning protocol is triggered through the smart contract to generate a digital asset abnormality alert.

[0087] Specifically, in the target blockchain ledger, according to the preset transaction screening rules, a target transaction block set that meets the transaction frequency and transaction amount characteristics is extracted, including:

[0088] Obtain the on-chain transaction data of the target blockchain ledger, and divide the transaction records according to the time window to obtain the on-chain transaction records;

[0089] Set transaction screening rules, including transaction frequency, transaction amount range, and transaction type;

[0090] Match the on-chain transaction records with the transaction screening rules and extract the target transaction blocks that meet the transaction screening rules;

[0091] The target transaction blocks are clustered and integrated according to the time sequence and characteristic indicators to form a target transaction block set; specifically, the clustering integration includes: Time sequence: sorting the target transaction blocks according to the transaction occurrence time to ensure the continuity of the timeline during the analysis process; Characteristic indicators: clustering according to the attributes of the transaction blocks (such as amount distribution, transaction frequency, source address, etc.), integrating similar transaction blocks to simplify data analysis.

[0092] Specifically, based on the abnormal transaction behavior detection model, the transaction characteristics in the target transaction block set are analyzed to generate a normal transaction block set and an abnormal transaction block set, including:

[0093] Obtain the on-chain transaction characteristic data of each transaction block in the target transaction block set, including transaction amount distribution, transaction frequency, and transaction path topology; specifically, the transaction path topology is used to describe the transaction links and participating node relationships involved in the transaction block, such as the transfer path between the transaction source and target address;

[0094] Input the transaction characteristic data into the abnormal transaction behavior detection model, and evaluate the degree of characteristic matching according to the abnormal behavior feature library preset in the abnormal transaction behavior detection model; Specifically, the abnormal transaction behavior detection model is a system based on machine learning or rule engine, which is used to detect whether the transaction block conforms to the predefined abnormal characteristic pattern; the abnormal behavior feature library contains known abnormal transaction characteristics, such as: high-frequency small-amount transactions (high frequency but small amount); special link paths (such as frequent bypassing of core nodes); sudden surge in transaction volume (such as large amount transfers in a short period of time);

[0095] According to the matching results, the transaction blocks are divided into normal transaction blocks and abnormal transaction blocks:

[0096] Mark the transaction blocks that match normal behavior characteristics as normal transaction blocks;

[0097] Marking transaction blocks matching abnormal behavior characteristics as abnormal transaction blocks;

[0098] The marking results are counted to generate normal transaction block sets and abnormal transaction block sets.

[0099] Specifically, the transaction link paths between the transaction blocks in the abnormal transaction block set are parsed to build an abnormal path graph on the chain, including:

[0100] Extract the on-chain associated transaction records of each abnormal transaction block from the abnormal transaction block set, including the source address, target address, and transaction time;

[0101] Based on the extracted on-chain associated transaction records, the transaction links between abnormal transaction blocks are analyzed to build the transfer relationship between nodes. Specifically, transaction link analysis: analyze the source address and target address of each transaction, and connect them to form a complete transfer link. Transfer relationship between nodes: nodes refer to on-chain addresses, and transfer relationship refers to the connection relationship between one node sending a transaction to another node.

[0102] Sort abnormal transaction links in chronological order and generate preliminary on-chain transaction paths;

[0103] According to the correlation frequency, number of jumps and link distribution of the preliminary on-chain transaction path, the preliminary on-chain transaction path is optimized to form an abnormal path set;

[0104] The abnormal path set is represented by a graph structure to construct an on-chain abnormal path graph; specifically, the graph structure: nodes represent on-chain addresses, edges represent transaction relationships, and constitute a network graph of abnormal paths.

[0105] Specifically, based on the on-chain abnormal path diagram, the propagation intensity and abnormal aggregation degree between transaction chains are evaluated, and the first-level abnormal risk results are output, including:

[0106] Extract key characteristic data of each path from the abnormal path graph on the chain, including path length, transaction frequency, node association, and number of transfers;

[0107] Based on the number of transfers and transaction frequency of the abnormal path, the transaction propagation strength of the abnormal path is evaluated; specifically, the transaction propagation strength of the abnormal path is the product of the number of transfers and the transaction frequency of the abnormal path;

[0108] The aggregation degree of the abnormal path is calculated based on the concentration of nodes in the abnormal path and the consistency of transaction patterns. Specifically, the node concentration degree reflects whether the transaction activities in the abnormal path are concentrated in a few nodes. If the transactions are concentrated in certain core nodes, these nodes may be the key points of abnormal activities. Transaction pattern consistency measures the regularity of transaction behavior in the path, for example, whether the transfer amount remains stable or whether the transfer time interval is periodic. The aggregation degree is obtained by weighted summing the node concentration degree and the transaction pattern consistency. Among them, the node concentration degree is equal to the number of transactions of high-frequency nodes divided by the total number of all transactions in the path. The number of transactions of high-frequency nodes refers to the total number of transactions involved in a node with the most transactions in the path. The total number of transactions is the sum of all transactions in the path. The transaction pattern consistency is obtained by weighted summing the transaction amount consistency score and the time interval consistency score. The transaction amount consistency score is equal to one minus the standard deviation of the transaction amount divided by the maximum value of the transaction amount range. The time interval consistency score is equal to one minus the standard deviation of the transaction time interval divided by the time window size of the path.

[0109] Combined with the propagation strength and aggregation degree of the abnormal path, the abnormal path risk is comprehensively scored; specifically, the comprehensive score is obtained by weighted summation of the propagation strength and aggregation degree of the node abnormal path;

[0110] The abnormal paths whose comprehensive scores exceed the preset risk score threshold are marked as first-level abnormal paths, and the first-level abnormal risk results are output.

[0111] Specifically, a reference blockchain ledger that is logically associated with the target blockchain ledger is extracted, and the corresponding reference transaction block is screened out according to the path position of the abnormal transaction block in the target transaction block set, including:

[0112] Extract reference blockchain ledgers related to the target blockchain ledger from the multi-chain database, calculate the logical correlation according to the on-chain characteristics of the ledger, and select the reference ledger with the highest logical correlation; for example, assuming that the target blockchain ledger is an Ethereum ledger, the multi-chain database contains the following ledgers:

[0113] Polkadot ledger: address overlap is 15%, and the number of cross-link paths is 25;

[0114] A private chain ledger: the address overlap is 10%, and the number of cross-link paths is 15;

[0115] By calculating the logical association:

[0118] Polkadot ledger correlation = 15% + 25 = 40;

[0119] Private chain ledger correlation = 10% + 15 = 25.

[0120] Select the Polkadot ledger with the highest logical correlation as the reference ledger;

[0121] Based on the on-chain path position of the abnormal transaction block in the target transaction block set, the transaction blocks with the same or similar path position in the reference blockchain ledger are extracted to generate a preliminary reference transaction block set; specifically, the on-chain path position refers to the path characteristics of each transaction in the abnormal transaction block, such as the source address, target address and position order of the path node;

[0122] Match the path characteristics of the preliminary reference transaction block set with the path characteristics of the abnormal transaction block, remove the transaction blocks that do not meet the similarity conditions, and generate the final reference transaction block set; For example, the Polkadot ledger contains the following transactions: Transaction: A→B→C; Preliminary reference transaction block set: Transaction 1 (A→B→C) and Transaction 2 (X→W→Z); Transaction 1: The path characteristics are completely matched and retained; Transaction 2: The path node matching degree is low (only nodes X and Z overlap), removed;

[0123] The final reference transaction block set is marked as the reference transaction block corresponding to the abnormal transaction block in the target transaction block set.

[0124] Specifically, the feature difference between the abnormal transaction block and the reference transaction block is compared to generate a cross-chain abnormal difference set, and the secondary abnormal risk results are output based on the feature overlap, including:

[0125] Extracting characteristic data of abnormal transaction blocks from the target transaction block set; specifically, the characteristic data includes specific attributes of the abnormal transaction blocks, such as transaction amount, frequency, path topology, etc.;

[0126] Extracting characteristic data corresponding to the abnormal transaction block from the reference transaction block set, including transaction characteristic data of the same dimension;

[0127] Compare the characteristic data of the abnormal transaction block with the reference transaction block to obtain the difference in characteristic dimensions;

[0128] Summarize all feature differences and generate a cross-chain abnormal difference set;

[0129] Based on the cross-chain abnormal difference set, the characteristic overlap between the abnormal transaction block and the reference transaction block is obtained; specifically, it is calculated based on the absolute value of the difference and the preset allowable range. For example, if the absolute value of the difference is less than the preset range, it is considered that the characteristic overlaps and the characteristic overlap is 100%;

[0130] Marking abnormal transaction blocks with feature overlap lower than a preset threshold as secondary abnormal transaction blocks;

[0131] Summarize all secondary abnormal transaction blocks and generate secondary abnormal risk results.

[0132] Specifically, an integrity verification matrix of abnormal transaction blocks is constructed between the target blockchain ledger and the reference blockchain ledger; based on the integrity verification matrix, the on-chain coverage of the abnormal path is calculated, and the abnormal integrity assessment results are output, including:

[0133] Extracting transaction path data related to abnormal transaction blocks in the target blockchain ledger and the reference blockchain ledger; specifically, the transaction path data includes path nodes (such as transfer addresses) and transaction relationships (such as transfer amounts and times);

[0134] Map the path nodes and transaction relationships corresponding to the abnormal transaction blocks into rows and columns of a matrix; specifically, the rows and columns of the matrix represent the nodes in the path respectively; each element in the matrix indicates whether there is a transaction relationship between the nodes and the characteristics of the relationship (such as transaction amount);

[0135] Fill the matrix according to the transaction relationship between nodes to form the integrity verification matrix of abnormal transaction blocks;

[0136] According to the node coverage of the abnormal transaction path in the integrity verification matrix, the on-chain coverage rate of the abnormal path is calculated; specifically, the on-chain coverage rate is the number of covered path nodes divided by the total number of path nodes;

[0137] According to the calculation results of on-chain coverage, combined with the distribution characteristics of abnormal paths, the impact range of abnormal transaction blocks on the chain is evaluated; for example, path 1 has a coverage rate of 100%, a uniform distribution characteristic, and a small impact range; path 2 has a coverage rate of 67%, and the distribution is concentrated in D and E, and the impact range is local and significant;

[0138] The paths with on-chain coverage lower than the set threshold are marked as local impact paths; the paths with on-chain coverage higher than the threshold are marked as full-chain impact paths, and the abnormal integrity assessment results are output; illustratively, the threshold is set to 70%: path 1 has a coverage of 100%, marked as a full-chain impact path; path 2 has a coverage of 67%, marked as a local impact path; output abnormal integrity assessment results: full-chain impact path: path 1 (A→B→C); local impact path: path 2 (D→E→F).

[0139] Specifically, the first-level abnormal risk results, the second-level abnormal risk results, and the abnormal integrity assessment results are integrated to trigger the on-chain early warning protocol through the smart contract to generate digital asset abnormal alarms, including:

[0140] Based on the first-level abnormal risk results, second-level abnormal risk results and abnormal integrity assessment results, a multi-dimensional risk scoring model is established to conduct comprehensive risk scoring on all abnormal transaction blocks and paths;

[0141] Based on the comprehensive risk score, abnormal transaction blocks and paths that exceed the preset alarm threshold are input into the smart contract;

[0142] The smart contract triggers the corresponding on-chain early warning protocol based on the scoring results and generates an abnormal digital asset alert.

[0143] Example 2

[0144] The difference between Example 2 of the present invention and Example 1 is that this example introduces a digital asset management system based on blockchain.

[0145] Figure 2 A structural schematic diagram of a digital asset management system based on blockchain of the present invention is given, a digital asset management system based on blockchain, including a transaction screening module, an anomaly detection module, a path parsing module, a primary risk assessment module, a reference ledger matching module, a secondary risk assessment module, an integrity verification module and an intelligent early warning module;

[0146] Transaction screening module: In the target blockchain ledger, according to the preset transaction screening rules, extract the target transaction block set that meets the transaction frequency and transaction amount characteristics;

[0147] Anomaly detection module: Based on the abnormal transaction behavior detection model, it performs feature analysis on the transaction characteristics in the target transaction block set to generate normal transaction block sets and abnormal transaction block sets;

[0148] Path parsing module: parses the transaction link paths between transaction blocks in the abnormal transaction block set and builds an abnormal path graph on the chain;

[0149] Level 1 risk assessment module: Based on the on-chain abnormal path diagram, it evaluates the propagation intensity and abnormal aggregation degree between transaction chains and outputs the level 1 abnormal risk results;

[0150] Reference ledger matching module: extracts reference blockchain ledgers that are logically associated with the target blockchain ledger, and selects corresponding reference transaction blocks according to the path position of the abnormal transaction blocks in the target transaction block set;

[0151] Secondary risk assessment module: Reference ledger matching module: compares the characteristic difference between the abnormal transaction block and the reference transaction block, generates a cross-chain abnormal difference set, and outputs the secondary abnormal risk result based on the characteristic overlap;

[0152] Integrity verification module: Integrity verification module: constructs an integrity verification matrix of abnormal transaction blocks between the target blockchain ledger and the reference blockchain ledger; based on the integrity verification matrix, calculates the on-chain coverage of the abnormal path and outputs the abnormal integrity assessment result;

[0153] Intelligent early warning module: Based on the first-level abnormal risk results, second-level abnormal risk results and abnormal integrity assessment results, the on-chain early warning protocol is triggered through smart contracts to generate digital asset abnormal alarms.

[0154] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0155] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0156] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0157] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0158] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0159] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0161] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0162] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0163] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital asset management method based on blockchain, characterized in that: The steps include: In the target blockchain ledger, according to the preset transaction screening rules, a target transaction block set that meets the transaction frequency and transaction amount characteristics is extracted; Based on the abnormal transaction behavior detection model, the transaction characteristics in the target transaction block set are analyzed to generate a normal transaction block set and an abnormal transaction block set; Analyze the transaction link paths between transaction blocks in the abnormal transaction block set and build an abnormal path graph on the chain; Based on the on-chain abnormal path diagram, the propagation intensity and abnormal aggregation degree between transaction chains are evaluated, and the first-level abnormal risk results are output; Extract a reference blockchain ledger that is logically associated with the target blockchain ledger, and filter out the corresponding reference transaction block according to the path position of the abnormal transaction block in the target transaction block set; Compare the characteristic differences between the abnormal transaction block and the reference transaction block, generate a cross-chain abnormal difference set, and output the secondary abnormal risk results based on the characteristic overlap; Constructing an integrity verification matrix of abnormal transaction blocks between the target blockchain ledger and the reference blockchain ledger; Based on the integrity verification matrix, calculate the on-chain coverage of the abnormal path and output the abnormal integrity assessment result; By combining the first-level abnormal risk results, second-level abnormal risk results and abnormal integrity assessment results, the on-chain early warning protocol is triggered through the smart contract to generate a digital asset abnormality alert.

2. A blockchain-based digital asset management method according to claim 1, characterized in that: In the target blockchain ledger, according to the preset transaction screening rules, the target transaction block set that meets the transaction frequency and transaction amount characteristics is extracted, specifically: Obtain the on-chain transaction data of the target blockchain ledger, and divide the transaction records according to the time window to obtain the on-chain transaction records; Set transaction screening rules, including transaction frequency, transaction amount range, and transaction type; Match the on-chain transaction records with the transaction screening rules and extract the target transaction blocks that meet the transaction screening rules; The target transaction blocks are clustered and integrated according to the time sequence and characteristic indicators to form a target transaction block set.

3. A blockchain-based digital asset management method according to claim 1, characterized in that: Based on the abnormal transaction behavior detection model, the transaction characteristics in the target transaction block set are analyzed to generate normal transaction block sets and abnormal transaction block sets, specifically: Obtain the on-chain transaction characteristic data of each transaction block in the target transaction block set, including transaction amount distribution, transaction frequency, and transaction path topology; Input the transaction characteristic data into the abnormal transaction behavior detection model, and evaluate the degree of characteristic matching according to the abnormal behavior feature library preset in the abnormal transaction behavior detection model; According to the matching results, the transaction blocks are divided into normal transaction blocks and abnormal transaction blocks: Mark the transaction blocks that match normal behavior characteristics as normal transaction blocks; Marking transaction blocks matching abnormal behavior characteristics as abnormal transaction blocks; The marking results are counted to generate normal transaction block sets and abnormal transaction block sets.

4. A blockchain-based digital asset management method according to claim 1, characterized in that: The transaction link paths between transaction blocks in the abnormal transaction block set are analyzed to build an abnormal path diagram on the chain, specifically: Extract the on-chain associated transaction records of each abnormal transaction block from the abnormal transaction block set, including the source address, target address, and transaction time; Based on the extracted on-chain associated transaction records, the transaction links between abnormal transaction blocks are analyzed to build the transfer relationship between nodes; Sort abnormal transaction links in chronological order and generate preliminary on-chain transaction paths; According to the correlation frequency, number of jumps and link distribution of the preliminary on-chain transaction path, the preliminary on-chain transaction path is optimized to form an abnormal path set; The abnormal path set is represented as a graph structure to construct an on-chain abnormal path graph.

5. A blockchain-based digital asset management method according to claim 1, characterized in that: Based on the on-chain abnormal path diagram, the propagation intensity and abnormal aggregation degree between transaction chains are evaluated, and the first-level abnormal risk results are output, specifically: Extract key characteristic data of each path from the abnormal path graph on the chain, including path length, transaction frequency, node association, and number of transfers; Evaluate the transaction propagation intensity of abnormal paths based on the number of transfers and transaction frequency of abnormal paths; The aggregation degree of the abnormal path is calculated based on the concentration of nodes in the abnormal path and the consistency of the transaction pattern; Combine the propagation intensity and aggregation degree of abnormal paths to comprehensively score the abnormal path risks; The abnormal paths whose comprehensive scores exceed the preset risk score threshold are marked as first-level abnormal paths, and the first-level abnormal risk results are output.

6. A blockchain-based digital asset management method according to claim 1, characterized in that: Extract the reference blockchain ledger that is logically associated with the target blockchain ledger, and filter out the corresponding reference transaction block according to the path position of the abnormal transaction block in the target transaction block set, specifically: Extract reference blockchain ledgers related to the target blockchain ledger from the multi-chain database, calculate the logical correlation based on the on-chain characteristics of the ledger, and select the reference ledger with the highest logical correlation; Based on the on-chain path position of the abnormal transaction block in the target transaction block set, the transaction blocks with the same or similar path position in the reference blockchain ledger are extracted to generate a preliminary reference transaction block set; Match the path characteristics of the preliminary reference transaction block set with the path characteristics of the abnormal transaction block, remove the transaction blocks that do not meet the similarity conditions, and generate the final reference transaction block set; The final reference transaction block set is marked as the reference transaction block corresponding to the abnormal transaction block in the target transaction block set.

7. A blockchain-based digital asset management method according to claim 1, characterized in that: Compare the characteristic differences between the abnormal transaction block and the reference transaction block, generate a cross-chain abnormal difference set, and output the secondary abnormal risk results based on the characteristic overlap, specifically: Extract characteristic data of abnormal transaction blocks from the target transaction block set; Extracting characteristic data corresponding to the abnormal transaction block from the reference transaction block set, including transaction characteristic data of the same dimension; Compare the characteristic data of the abnormal transaction block with the reference transaction block to obtain the difference in characteristic dimensions; Summarize all feature differences and generate a cross-chain abnormal difference set; Based on the cross-chain abnormal difference set, the characteristic overlap between the abnormal transaction block and the reference transaction block is obtained; Marking abnormal transaction blocks with feature overlap lower than a preset threshold as secondary abnormal transaction blocks; Summarize all secondary abnormal transaction blocks and generate secondary abnormal risk results.

8. A blockchain-based digital asset management method according to claim 1, characterized in that: Between the target blockchain ledger and the reference blockchain ledger, an integrity verification matrix of abnormal transaction blocks is constructed; based on the integrity verification matrix, the on-chain coverage of the abnormal path is calculated, and the abnormal integrity assessment result is output, specifically: Extracting transaction path data related to abnormal transaction blocks in the target blockchain ledger and the reference blockchain ledger; Map the path nodes and transaction relationships corresponding to the abnormal transaction blocks into rows and columns of the matrix; Fill the matrix according to the transaction relationship between nodes to form the integrity verification matrix of abnormal transaction blocks; According to the node coverage of abnormal transaction paths in the integrity verification matrix, calculate the on-chain coverage rate of abnormal paths; Based on the calculation results of on-chain coverage and the distribution characteristics of abnormal paths, the impact range of abnormal transaction blocks propagating on the chain is evaluated; The paths whose on-chain coverage is lower than the set threshold are marked as locally affected paths; the paths whose on-chain coverage is higher than the threshold are marked as fully affected paths, and the abnormal integrity assessment results are output.

9. A blockchain-based digital asset management method according to claim 1, characterized in that: Based on the first-level abnormal risk results, the second-level abnormal risk results and the abnormal integrity assessment results, the on-chain early warning protocol is triggered through the smart contract to generate digital asset abnormality alarms, specifically: Based on the first-level abnormal risk results, second-level abnormal risk results and abnormal integrity assessment results, a multi-dimensional risk scoring model is established to conduct comprehensive risk scoring on all abnormal transaction blocks and paths; Based on the comprehensive risk score, abnormal transaction blocks and paths that exceed the preset alarm threshold are input into the smart contract; The smart contract triggers the corresponding on-chain early warning protocol based on the scoring results and generates an abnormal digital asset alert.

10. A digital asset management system based on blockchain, used to implement a digital asset management method based on blockchain as described in any one of claims 1 to 9, characterized in that: It includes transaction screening module, anomaly detection module, path parsing module, primary risk assessment module, reference ledger matching module, secondary risk assessment module, integrity verification module and intelligent early warning module; Transaction screening module: In the target blockchain ledger, according to the preset transaction screening rules, extract the target transaction block set that meets the transaction frequency and transaction amount characteristics; Anomaly detection module: Based on the abnormal transaction behavior detection model, it performs feature analysis on the transaction characteristics in the target transaction block set to generate normal transaction block sets and abnormal transaction block sets; Path parsing module: parses the transaction link paths between transaction blocks in the abnormal transaction block set and builds an abnormal path graph on the chain; Level 1 risk assessment module: Based on the on-chain abnormal path diagram, it evaluates the propagation intensity and abnormal aggregation degree between transaction chains and outputs the level 1 abnormal risk results; Reference ledger matching module: extracts reference blockchain ledgers that are logically associated with the target blockchain ledger, and selects corresponding reference transaction blocks according to the path position of the abnormal transaction blocks in the target transaction block set; Secondary risk assessment module: Reference ledger matching module: compares the characteristic difference between the abnormal transaction block and the reference transaction block, generates a cross-chain abnormal difference set, and outputs the secondary abnormal risk result based on the characteristic overlap; Integrity verification module: Integrity verification module: constructs an integrity verification matrix of abnormal transaction blocks between the target blockchain ledger and the reference blockchain ledger; Based on the integrity verification matrix, calculate the on-chain coverage of the abnormal path and output the abnormal integrity assessment result; Intelligent early warning module: Based on the first-level abnormal risk results, second-level abnormal risk results and abnormal integrity assessment results, the on-chain early warning protocol is triggered through smart contracts to generate digital asset abnormal alarms.

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