Security rating method and system based on cross-chain interaction of blockchain

Abstract

The application discloses a security rating method and system based on cross-chain interaction of blockchains, and belongs to the technical field of security rating. The method specifically comprises the following steps: collecting transaction data, node behavior data, and delay and abnormal characteristics of a cross-chain communication link of each blockchain network in a cross-chain interaction process, generating a cross-chain security entropy index, generating and managing reputation tokens through a smart contract based on the cross-chain security entropy index, dynamically rating the cross-chain interaction security of each blockchain according to generation, circulation and historical records of the reputation tokens, and recording the security rating results in a layered form in a blockchain account book. When an abnormal cross-chain security entropy index is detected, the value of the reputation tokens is automatically adjusted, a preset game mechanism is triggered, and the security strategy of the blockchain network is optimized. The application comprehensively analyzes cross-chain transactions, smart contract vulnerabilities and abnormal behaviors under the premise of ensuring privacy, and effectively improves the risk perception ability in the financial cross-chain interaction scene.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of security rating, and in particular to a security rating method and system based on cross-chain interaction of blockchains. BACKGROUND

[0002] In the current global financial system, security rating is an important tool for financial institutions, investors, and regulatory authorities to assess the credit of market participants, the risk of financial products, and the reliability of cross-border transactions. Traditional security rating methods mainly rely on centralized databases, authoritative evaluation models, and manual review methods.

[0003] However, with the acceleration of the digitalization and decentralization trend of the financial market, especially the widespread application of blockchain technology in digital asset transactions, cross-border payments, and decentralized finance, transaction participants often interact frequently across multiple blockchains. How to effectively collect, integrate, verify, and evaluate security in a multi-chain environment has become a challenge that existing technology cannot solve. Existing rating methods are usually limited to a single-chain perspective or rely solely on external audits, and cannot achieve dynamic risk perception and real-time security rating across chains. SUMMARY

[0004] To address the shortcomings of the prior art, the present application proposes a security rating method and system based on cross-chain interaction of blockchains, which can integrate trusted data in a multi-chain environment and dynamically extract cross-chain security features to achieve decentralized, fair, and tamper-proof rating results, improving the security and transparency of the financial transaction system.

[0005] To achieve the above-mentioned purposes, the present application provides the following technical solutions:

[0006] The security rating method based on cross-chain interaction of blockchains comprises:

[0007] In the cross-chain interaction process, transaction data, node behavior data, and delay and abnormal features of cross-chain communication links of each blockchain network are collected to generate cross-chain security entropy indicators;

[0008] Based on the cross-chain security entropy indicators, reputation tokens are generated and managed by smart contracts, and the reputation tokens are used to represent the security of each blockchain. The reputation tokens of different blockchains circulate under cross-chain interaction protocols;

[0009] According to the generation, circulation, and historical records of the reputation tokens, the cross-chain interaction security of each blockchain is dynamically rated, and the security rating results are recorded in a hierarchical form in the blockchain ledger;

[0010] When detecting an abnormal cross-chain security entropy indicator, the value of the reputation token is automatically adjusted and a preset game mechanism is triggered to optimize the security strategy of the blockchain network.

[0011] Specifically, the cross-chain security entropy index is generated by:

[0012] Collecting transaction time sequence information of different blockchain nodes in the cross-chain interaction process, and segmenting and dividing the information to generate basic time sequence segments;

[0013] Performing communication link anomaly detection on the basic time sequence segments, identifying delay mutations, repeated propagation and data loss characteristics therein, and mapping the characteristics into an anomaly vector;

[0014] Jointly encoding the anomaly vector and node behavior data to form a cross-chain interaction feature matrix;

[0015] Based on the cross-chain interaction feature matrix, the cross-chain data stream is distributed and analyzed to generate a cross-chain security entropy index, which is obtained according to the transaction data of each blockchain network, the node behavior data, and the delay and anomaly characteristics of the cross-chain communication link.

[0016] Specifically, based on the cross-chain security entropy index, a reputation token is generated and managed by a smart contract, including:

[0017] According to the time sequence of the cross-chain security entropy index, a dynamic threshold interval is set, and the dynamic threshold interval is mapped to an initial number of reputation tokens to be generated;

[0018] The initial number is bound to the calling result of the cross-chain witness node by calling the randomness commitment of the cross-chain witness node through the smart contract, forming a generation request of the reputation token;

[0019] In the cross-chain interaction protocol, the generation requests of the reputation tokens of different blockchain networks are mutually calibrated to exclude isolated or contradictory reputation token generation records, and a consensus-confirmed reputation token issuance sequence is obtained;

[0020] Based on the reputation token issuance sequence, a cross-chain circulating reputation token is created, and it is stored in a preset cross-chain account cluster in real time.

[0021] Specifically, based on the reputation token issuance sequence, a cross-chain circulating reputation token is created, including:

[0022] Based on the identifier in the reputation token issuance sequence, a unique cross-chain mapping mark is generated for each reputation token, and the cross-chain mapping mark is written into the original block segment of the cross-chain ledger;

[0023] According to the correlation degree of different cross-chain account clusters, the reputation tokens are distributed to the corresponding cross-chain account groups according to the mapping rules, and are synchronously written into the circulation table of the cross-chain ledger;

[0024] In the circulation table of the cross-chain ledger, a cross-chain transferable path is established for each reputation token, and the cross-chain transferable path is bound to the node identity in the cross-chain interaction protocol;

[0025] After completing the path binding, the cross-chain circulation order of the reputation token is recorded in sequence according to the ordering mechanism of the cross-chain ledger, and the cross-chain circulating reputation token is obtained.

[0026] Specifically, according to the generation, circulation and historical record of the reputation token, the cross-chain interaction security of each blockchain is dynamically rated, including:

[0027] The generation sequence of the reputation token in each blockchain is extracted, and a mapping table of the generation event and the corresponding in-chain node identity is established;

[0028] According to the mapping table, the transfer path of the reputation token between different blockchains is serialized to form a cross-chain circulation track set;

[0029] The aggregation mode and dispersion mode between nodes in the cross-chain circulation track set are identified and compared with the preset cross-chain account group to generate an inter-chain interaction relationship graph;

[0030] According to the inter-chain interaction relationship graph, the correlation strength of each blockchain in the cross-chain interaction process is calculated, and the generation event, circulation track and historical cumulative data are combined in layers;

[0031] The result of the layered combination is written into the rating index area in the cross-chain ledger, and the cross-chain interaction security of each blockchain is dynamically rated.

[0032] Specifically, the aggregation mode and dispersion mode between nodes in the cross-chain circulation track set are identified and compared with the preset cross-chain account group to generate an inter-chain interaction relationship graph, including:

[0033] The cross-chain transfer events in the cross-chain transfer track set are time-sequentially divided to establish a track segment set with time windows as units;

[0034] The node interaction frequency in the same time window is counted in the track segment set, and the node interaction frequency is divided into aggregation segments and dispersion segments;

[0035] The aggregation segments and dispersion segments are respectively matched and compared with the preset cross-chain account group to identify the node set associated with the preset cross-chain account group;

[0036] According to the node set and its corresponding aggregation segments and dispersion segments, an inter-chain interaction relationship graph is constructed, and the interaction edges and cross-chain paths between nodes are recorded in the inter-chain interaction relationship graph.

[0037] Specifically, according to the inter-chain interaction relationship diagram, the correlation strength of each blockchain in the cross-chain interaction process is calculated, and the generation event, circulation track and historical cumulative data are hierarchically combined, including:

[0038] The number and length of cross-chain paths between node pairs in the inter-chain interaction relationship diagram are extracted and mapped as a basic correlation degree unit;

[0039] According to the basic correlation degree unit, the generation event and circulation track in the same time window are aggregated to form a time-layered correlation strength matrix;

[0040] The correlation strength matrices of different time windows are stacked in order, and combined with the historical cumulative data to generate a cross-time dimension hierarchical combination;

[0041] Mark the repeated occurrence frequency of the cross-chain path in the hierarchical combination, and establish a correlation strength index table.

[0042] Specifically, the result of the hierarchical combination is written into the rating index area in the cross-chain ledger, and the cross-chain interaction security of each blockchain is dynamically rated, including:

[0043] The hierarchical combination result is divided according to the blockchain network dimension, and a corresponding rating label is generated for each dimension;

[0044] According to the rating label, a preset index generation rule is called to bind the rating label with the node identifier in the inter-chain interaction relationship diagram;

[0045] The binding result is written in the rating index area of the cross-chain ledger in time sequence, and a unique index number is established for each record;

[0046] According to the unique index number, a cross-chain interaction security rating table is generated, and a mapping relationship between the cross-chain interaction security rating table and the historical rating data in the cross-chain ledger is established.

[0047] Specifically, according to the unique index number, a cross-chain interaction security rating table is generated, and a mapping relationship between the cross-chain interaction security rating table and the historical rating data in the cross-chain ledger is established, including:

[0048] According to the unique index number, the hierarchical combination result is aggregated and divided, and an initial rating list with cross-chain nodes as index items is generated;

[0049] The initial rating list is compared with the index items of the historical rating data in the cross-chain ledger, and a node-level mapping table is established;

[0050] Identify the difference fragments of the rating result in the mapping table, and bind them with the circulation track corresponding to the cross-chain account group;

[0051] Based on the difference fragments and the binding information, a cross-chain interaction security rating table is generated, and a mapping relationship between the cross-chain interaction security rating table and historical rating data is registered in a cross-chain ledger.

[0052] The security rating system for cross-chain interaction based on a block chain is used to realize the security rating method for cross-chain interaction based on a block chain, and comprises a data acquisition module, a reputation token generation module, a security rating module and an optimization module.

[0053] The data acquisition module is used to acquire transaction data, node behavior data and delay and abnormal characteristics of a cross-chain communication link of each block chain network in a cross-chain interaction process, and generate a cross-chain security entropy index.

[0054] The reputation token generation module is used to generate and manage reputation tokens through a smart contract based on the cross-chain security entropy index.

[0055] The security rating module is used to dynamically rate the cross-chain interaction security of each block chain according to the generation, circulation and historical record of the reputation tokens, and record the security rating results in a hierarchical form in a block chain ledger.

[0056] The optimization module is used to automatically adjust the value of the reputation tokens and trigger a preset game mechanism to optimize the security strategy of the block chain network when detecting an abnormal cross-chain security entropy index.

[0057] Compared with the prior art, the beneficial effects of the present application are:

[0058] The present application proposes a security rating method and system for cross-chain interaction based on a block chain, acquires node behavior, transaction status and communication link characteristics in a cross-chain interaction process, constructs a cross-chain security entropy index, generates reputation tokens that can circulate across chains based on the index, combines the generation, circulation and historical record of the tokens to form an inter-chain interaction relationship graph and hierarchical combination data, and finally establishes a dynamic rating index in a ledger, thereby realizing quantitative evaluation of the security of different block chains in a cross-chain environment. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 A security rating method flowchart for cross-chain interaction based on a block chain is provided for the present application.

[0060] Figure 2 A general structure schematic diagram of a cross-chain ledger is provided for the present application.

[0061] Figure 3The cross-chain interaction security rating provided by the present application indicates the intention;

[0062] Figure 4 The security rating system architecture diagram based on the cross-chain interaction of the block chain provided by the present application. DETAILED DESCRIPTION

[0063] The present application will be described in detail below with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made. These are within the scope of protection of the present application.

[0064] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0065] It should be noted that the features in the embodiments of the present application can be combined with each other without conflict, and are within the scope of protection of the present application. In addition, although the functional modules are divided in the device schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the order in the flowchart. In addition, the "first", "second", "third" and the like used in the present application do not limit the data and execution order, but only distinguish the same items or similar items with basically the same function and effect.

[0066] Unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used in the present application are only for the purpose of describing the specific embodiments of the present application, and are not used to limit the present application. The term "and / or" used in the present application includes any and all combinations of one or more related listed items.

[0067] Embodiment 1:

[0068] Please refer to Figures 1-3 The present application provides an embodiment: a security rating method based on cross-chain interaction of block chain, comprising the following specific steps:

[0069] Step S1: Collecting transaction data, node behavior data and delay and abnormal characteristics of cross-chain communication link of each block chain network in the cross-chain interaction process, and generating cross-chain security entropy index.

[0070] The specific steps of step S1 are:

[0071] Step S101: Collect the transaction timing information of different blockchain nodes in the cross-chain interaction process and segment it to generate basic timing segments.

[0072] In this embodiment, for the transaction information generated by different blockchain nodes in the cross-chain interaction process, first, the occurrence time of the transaction is uniformly corrected; specifically, cross-chain interaction usually involves parallel processing of multiple chains, and the block time, confirmation delay and message relay path of each chain are different, so first the timestamps of different chains are converted into a relatively unified time reference through a preset synchronization strategy, and under the unified reference, each cross-chain transaction and its related events are sorted to form a continuous transaction timing set; then, the set is segmented according to the set time window and interaction characteristics, so that each segment can cover a continuous cross-chain activity, and a basic timing segment is generated.

[0073] Step S102: Perform communication link anomaly detection on the basic timing segment, identify the delay mutation, repeated propagation and data loss characteristics, and map the characteristics to an anomaly vector.

[0074] In this embodiment, when performing communication link anomaly detection on the basic timing segment, first, compare the message transmission intervals in the segment under the unified time reference, and if there is a mutation in the continuous interval, mark it as a delay anomaly; then cross-check the message hash values and node signatures in adjacent segments, and when the same message appears repeatedly in different paths or different time windows, it is identified as repeated propagation; further, by counting the matching relationship between the response message and the request message, if the corresponding response is missing within the specified time, it is determined that there is data loss; the above abnormal information is converted into a vector structure after detection, in which each type of abnormal event is mapped to a different feature dimension to obtain an anomaly vector.

[0075] Step S103: Jointly encode the anomaly vector and node behavior data to form a cross-chain interaction feature matrix.

[0076] In this embodiment, when jointly encoding the anomaly vector and the node behavior data, first, standardize each dimension of the anomaly vector so that it is consistent with the node behavior log in terms of time scale and event intensity; then, according to the unique identity of the node, align the anomaly vector with the behavior data of the corresponding node within the same time window, including transaction initiation frequency, consensus participation record and cross-chain call frequency; after alignment, through a set joint encoding rule, map the two types of data to a unified feature space, so that the anomaly features and node behaviors can form a comparable matrix structure; the final cross-chain interaction feature matrix not only covers the anomaly detection information completely, but also contains the activity performance of the node in the cross-chain process.

[0077] Step S104: Based on the cross-chain interaction feature matrix, perform distributed analysis on the cross-chain data stream to generate a cross-chain security entropy index. The cross-chain data stream is obtained based on the transaction data, node behavior data, and latency and anomaly characteristics of the cross-chain communication link of each blockchain network.

[0078] In this embodiment, when performing distributed analysis of cross-chain data streams based on the cross-chain interaction feature matrix, transaction data, node behavior data, and latency and anomaly characteristics of communication links from different sources are first mapped to the same probability distribution space to ensure the comparability of various indicators. Subsequently, the distribution space is multidimensionally segmented, and the message transmission balance, node participation uniformity, and concentration of abnormal events in the interaction process of different blockchain networks are statistically analyzed to obtain a statistical distribution describing the overall stability of cross-chain interaction. After completing the segmentation and statistics, the cross-chain security entropy index is generated by aggregating the dispersion and uncertainty levels of each distribution segment.

[0079] Step S2: Based on the cross-chain security entropy index, reputation tokens are generated and managed through smart contracts. These reputation tokens are used to characterize the security of each blockchain, and the reputation tokens of different blockchains circulate under the cross-chain interaction protocol.

[0080] like Figure 2 As shown, the specific steps of step S2 are as follows:

[0081] Step S201: Based on the time series of the cross-chain security entropy index, set a dynamic threshold range and map the dynamic threshold range to the initial quantity of reputation tokens to be generated.

[0082] In this embodiment, when setting a dynamic threshold range based on the time series of the cross-chain security entropy index, the entropy fluctuations within a continuous time window are first statistically analyzed to extract their magnitude and trend, constructing a baseline curve that reflects the stability of cross-chain interactions. Then, dynamic threshold ranges are set in the upper and lower segments of this baseline curve, allowing different levels of entropy distribution to be categorized into high-risk, medium-risk, or low-risk states. After the threshold range is determined, it is mapped to predefined token generation rules, so that different entropy ranges correspond to different orders of magnitude of reputation tokens to be generated. In this way, the abstract entropy index is transformed into an initial token quantity with measurable characteristics.

[0083] Step S202: By calling the randomness commitment of the cross-chain witness node through the smart contract, the initial quantity is bound to the call result of the cross-chain witness node to form a request for the generation of reputation tokens.

[0084] In the present embodiment, when the smart contract calls the randomness commitment of the cross-chain witness node, first, the generation request logic is triggered in the contract, and the calling instruction is sent to the preset cross-chain witness node group. Each witness node needs to generate an unpredictable random commitment value based on its own randomness source. Then, the smart contract binds the random commitment value with the initial token quantity calculated in advance, so that the quantity of token generation is coupled with the independent witness behavior of the node. Among the commitment results returned by multiple witness nodes, the smart contract further performs consistency checking, and only when the preset verification condition is met, the final effective reputation token generation request is formed. Thus, the initial quantity is processed in the process of binding randomness commitment, and is solidified as the trigger event of reputation token generation in the contract.

[0085] Step S203: Inter-alignment of reputation token generation requests of different blockchain networks in the cross-chain interaction protocol, exclusion of isolated or contradictory reputation token generation records, and obtaining of a consensus-confirmed reputation token issuance sequence.

[0086] In the present embodiment, when the cross-chain interaction protocol is used to align the reputation token generation requests of different blockchain networks, first, the generation requests submitted by each chain are classified and arranged in chronological order and the request source node, and a request index table is established. Then, the request contents of different chains are compared using the verification rules in the cross-chain interaction protocol, including the initial token quantity, random commitment value, and node signature information. If a request is found to have no corresponding item in the multi-chain data, it is determined to be an isolated record and is excluded. For conflicting requests with quantity differences or inconsistent random commitment values, a trusted result is selected through a multi-party voting mechanism or a weighted consensus algorithm, and the remaining records are marked as contradictory items and excluded. After the above processing is completed, the valid requests remaining are arranged in order to form a reputation token issuance sequence that is consensus-confirmed by multiple chains.

[0087] Step S204: Based on the reputation token issuance sequence, create cross-chain circulating reputation tokens, and store them in real time in the preset cross-chain account cluster.

[0088] The specific steps of step S204 are:

[0089] Step S2041: Generate a unique cross-chain mapping marker for each reputation token based on the identifier in the reputation token issuance sequence, and write the cross-chain mapping marker to the original block segment of the cross-chain ledger.

[0090] In this embodiment, when generating the cross-chain mapping label for each reputation token based on the identifier in the reputation token issuance sequence, first, the reputation token identifier in the issuance sequence is associated with the source chain of its generation request, the witness node, and the timestamp to form an index entry with multiple attributes; then, based on the index entry, a unique cross-chain mapping label is constructed for each reputation token through a mapping rule, so that it can maintain consistent identity pointing between different blockchain networks; after the label is generated, it is written into the original block segment of the cross-chain ledger together with the initial generation record of the reputation token.

[0091] Step S2042: According to the correlation degree of different cross-chain account clusters, the reputation tokens are distributed to the corresponding cross-chain account groups according to the mapping rule, and are synchronously written into the circulation table of the cross-chain ledger.

[0092] In this embodiment, when distributing reputation tokens according to the correlation degree of different cross-chain account clusters, first, the interaction records and node topology relationships of each account cluster are retrieved in the cross-chain ledger to calculate the correlation degree between different account clusters; then, according to the preset mapping rule, the reputation tokens with higher correlation degree are preferentially distributed to the corresponding cross-chain account groups, and the correspondence between the tokens and the generation identifiers is maintained unchanged during the distribution; after the distribution is completed, the token attribution is written into the circulation table of the cross-chain ledger in the form of a structured entry.

[0093] Step S2043: In the circulation table of the cross-chain ledger, an inter-chain transferable path is established for each reputation token, and the path is bound to the node identity in the cross-chain interaction protocol.

[0094] In this embodiment, when establishing an inter-chain transferable path for each reputation token in the circulation table of the cross-chain ledger, first, the potential target chain set of the token is determined according to its attribution information in the account group, and an initial candidate list of transferable paths is generated; then, the node identity data of each chain is called through the cross-chain interaction protocol to verify the key nodes on the candidate path, and the node identity is bound to the path segment one by one to ensure the identity consistency of the token in cross-chain circulation; after the node binding is completed, the confirmed path structure is registered in the circulation table, and an independent path index is assigned to the path, so that each reputation token has a traceable cross-chain migration trajectory.

[0095] Step S2044: After the path binding is completed, the cross-chain circulation order of the reputation tokens is recorded in sequence according to the ordering mechanism of the cross-chain ledger, and the cross-chain circulating reputation tokens are obtained.

[0096] In this embodiment, after completing the path binding, the circulation order of the reputation token is recorded according to the cross-chain ledger sorting mechanism. First, the token instances of the bound path are sorted according to the time stamp of block generation and the order of transaction submission, ensuring that the transfer events between different chains can be aligned under a unified time sequence. Then, according to the sorting result, the tokens are registered one by one into the circulation table, and the corresponding path index and node identity tag are attached to each registration entry to establish the complete cross-chain transfer link. On this basis, the continuous transfer entries of the same token are concatenated to form an ordered circulation sequence, and the migration trajectory of the token between multiple chains is fixed. The final circulation sequence is the cross-chain circulating reputation token.

[0097] As shown in Figure 2 , the original block segment storage area is used to record the basic generation data in cross-chain interaction, including the generation record of reputation token and cross-chain mapping mark. This area serves as the bottom layer data entry of the ledger, ensuring that the generation process of each reputation token can be fixed and stored. The circulation table storage area is used to track the dynamic circulation of reputation token in the cross-chain interaction process. This area includes account group allocation record, transferable path and circulation order, etc., thereby forming the cross-chain circulation trajectory of reputation token in the ledger, and the transfer path of the token between different blockchains can be recorded completely. The rating index area is used to establish a dynamic evaluation index of cross-chain security. This area contains the rating label, unique index number and historical mapping relationship of the node. Through this index area, the security evaluation result of cross-chain interaction can be quickly located and retrieved, and a mapping relationship with the existing historical rating information is formed. The historical data archiving area is connected with the above-mentioned areas, and is used for long-term archiving storage of historical interaction events and historical rating data, realizing the traceability ability of cross-chain ledger in the vertical time dimension. Through this archiving area, the latest rating result can be associated with historical data, thereby supporting trend analysis and security evolution tracking. In summary, Figure 2 , the overall structure of cross-chain ledger shown in , the overall structure of cross-chain ledger shown in

[0098] , the overall structure of cross-chain ledger shown in

[0099] As shown in Figure 3 , the specific steps of step S3 are as follows:

[0100] Step S301: Extract the generation sequence of reputation token in each blockchain, and establish a mapping table of generation events and corresponding chain node identity.

[0101] In this embodiment, when extracting the generation sequence of the reputation token in each blockchain, first, the issuance records in the cross-chain ledger are searched one by one, the generation time, source chain identifier and generation request number of each reputation token are collected, and a generation sequence in order is formed; then, based on the signature information attached in each token generation request, the corresponding in-chain node identity is parsed, and the node identity and the token generation event are corresponded; after the parsing is completed, a mapping table is constructed with the generation event as the primary key and the node identity as the index item, so that the generation process of each reputation token can be clearly corresponded to the specific node; and finally the mapping table is obtained.

[0102] Step S302: According to the mapping table, the transfer path of the reputation token between different blockchains is serialized to form a cross-chain circulation track set.

[0103] In this embodiment, when the transfer path of the reputation token between different blockchains is serialized according to the mapping table, first, the token cross-chain transfer events recorded in the ledger are called, and the starting node and the target node of each transfer are paired according to the correspondence between the token generation event and the node identity; then, the continuous cross-chain transfer events are concatenated in chronological order according to the timestamp as the primary sequence, so that the migration process of the token among multiple chains can be completely restored; in this process, the node identity, link identifier and transfer times involved in the path are marked one by one to avoid cross confusion of different token tracks; and finally the cross-chain circulation track set is generated.

[0104] Step S303: Identify the aggregation mode and dispersion mode between nodes in the cross-chain circulation track set, and compare them with the preset cross-chain account group to generate an inter-chain interaction relationship graph.

[0105] The specific steps of step S303 are:

[0106] Step S3031: Time-sequentially segment the cross-chain transfer events in the cross-chain circulation track set to establish a track segment set with time window as the unit.

[0107] In this embodiment, when the cross-chain transfer events in the cross-chain circulation track set are time-sequentially segmented, first, a unified time sequence axis is established according to the global timestamp of the token cross-chain transfer, and all events are sorted according to time; then, the sorted event sequence is divided into several continuous sections with a preset time window as the boundary, and each window section only contains the transfer events occurring within the time range; during the division process, concurrent transfer events occurring in the same time window are also aggregated to be uniformly represented in the form of a segment unit; and finally, the track segment set obtained has a time window as the minimum division granularity, and completely records the migration activities of the cross-chain token in different time periods.

[0108] Step S3032: Count the node interaction frequency in the same time window in the trajectory segment set, and divide into the aggregation segment and the dispersion segment according to the node interaction frequency.

[0109] In the embodiment, when counting the node interaction frequency in the same time window in the trajectory segment set, first, the transition events involved in each time window are traversed one by one, the occurrence times of each node as a sender or a receiver are recorded, and a node interaction frequency table is formed; then, the frequency table is thresholded, when the interaction frequency of a node or a node set exceeds the preset limit, the segment corresponding to the time window is marked as an aggregation segment; if the interaction frequency is uniformly distributed and there is no obvious concentration trend, the segment corresponding to the time window is marked as a dispersion segment; through this division process, the trajectory segment set presents different interaction mode classifications in the time dimension.

[0110] Step S3033: Match and correct the aggregation segment and the dispersion segment with the preset cross-chain account group respectively, and identify the node set associated with the preset cross-chain account group.

[0111] In the embodiment, when matching and correcting the aggregation segment and the dispersion segment with the preset cross-chain account group respectively, first, an account group feature dictionary is established according to the on-chain address, node identity identifier and cross-chain alias mapping recorded in the account group; then, the identity of the node set appearing in each segment is restored, the address identifier, signature header information and belonging chain marker of the node are aligned to a unified identification space, and a candidate association set is generated according to the appearance relationship in the time window; in the candidate association set, the address mapping and the alias mapping are compared item by item according to the account group feature dictionary, and the continuous appearance of the node in the adjacent time window is checked, and the node that only appears temporarily in a single window and does not meet the mapping consistency is removed; after the above comparison, the nodes that meet the mapping rule and the time sequence continuity are classified into the node set associated with the preset cross-chain account group, and the source segment identifier and the time window index are added to each set.

[0112] Step S3034: According to the node set and the corresponding aggregation segment and dispersion segment, construct an inter-chain interaction relationship graph, and record the interaction edges and cross-chain paths between nodes in the inter-chain interaction relationship graph.

[0113] In this embodiment, when constructing the inter-chain interaction relationship graph according to the node set and its corresponding aggregated segment and dispersed segment, first, each node set is taken as a vertex object in the graph, and its corresponding blockchain identifier and time window index are retained; then, for the high-frequency interaction nodes in the aggregated segment, a weighted edge is established in the graph to represent the close association of the nodes in the time window, and the low-frequency interaction nodes in the dispersed segment are recorded in the form of sparse edges to show the weak connection of the nodes in cross-chain interaction; in the process of generating edges, the cross-chain path information of the node pair involved in cross-chain transfer is separately marked, and the starting chain and target chain identifiers of the path are marked in the graph structure to ensure that the cross-chain directionality is completely recorded; finally, the inter-chain interaction relationship graph is formed, which can map different nodes, segments and cross-chain paths into a visual data structure.

[0114] Step S304: According to the inter-chain interaction relationship graph, the correlation strength of each blockchain in the cross-chain interaction process is calculated, and the generation event, circulation track and historical cumulative data are hierarchically combined.

[0115] The specific steps of step S304 are:

[0116] Step S3041: The number of cross-chain paths and path length between node pairs in the inter-chain interaction relationship graph are extracted and mapped into a basic correlation unit.

[0117] In this embodiment, when the number of cross-chain paths and path length between node pairs in the inter-chain interaction relationship graph are extracted, first, each node pair in the inter-chain interaction relationship graph is traversed to retrieve the path records involved in different time windows and cross-chain segments; then, the total number of paths in which the node pair appears in each segment is counted, and the number of hops of each path is accumulated to obtain the number dimension and length dimension characteristics of the node pair in cross-chain interaction; after the statistics are completed, the path number and path length are normalized according to a preset encoding rule, and the two are combined into a basic correlation unit to reflect the direct contact strength of the node pair in the cross-chain interaction process; finally, the basic correlation unit is obtained.

[0118] Step S3042: According to the basic correlation unit, the generation event and circulation track in the same time window are aggregated to form a time-layered correlation strength matrix.

[0119] In this embodiment, when aggregating generation events and circulation trajectories within the same time window based on the basic correlation unit, the generation events of all reputation tokens within the time window and their corresponding cross-chain circulation paths are first collected, and the node pairs involved and their corresponding basic correlation units are extracted. Then, with node pairs as rows and correlation values ​​consisting of the number of paths and path lengths as columns, all basic correlation units within the same time window are filled into the matrix structure one by one. During the filling process, duplicate node pairs are superimposed according to the cumulative rule, and missing node pairs are filled with zero values ​​to ensure the integrity of the matrix within the time window. The final result is a time-layered correlation strength matrix, which can simultaneously characterize the connection strength and distribution between nodes in cross-chain interactions.

[0120] Step S3043: Stack the correlation strength matrices of different time windows in sequence and combine them with historical accumulated data to generate a hierarchical combination across time dimensions.

[0121] In this embodiment, when the association strength matrices of different time windows are superimposed sequentially, the association strength matrices generated by each window are first arranged according to the chronological order of the time axis, and the association values ​​of the same node pairs in different time periods are accumulated layer by layer using node identity as a unified index. Then, historical accumulated data is introduced as a benchmark to fuse the long-term interaction relationships formed in the past period with the short-term interaction strength of the current period, so as to avoid the interference of fluctuations in a single time window on the overall result. During the fusion process, the matrices of different time periods are given decay weights to make the influence of recent data on the result more significant, while historical data is embedded as a stability correction factor. The resulting cross-time dimension layered combination can reflect the dynamic evolution of the relationship between nodes vertically and maintain the global consistency of cross-chain interaction strength horizontally.

[0122] Step S3044: Mark the frequency of recurrence of cross-chain paths in the hierarchical combination and establish an association strength index table.

[0123] In this embodiment, when marking the recurrence frequency of cross-chain paths in the hierarchical combination, the matrix after being superimposed across the time dimension is first searched one by one to identify the cross-chain paths between the same node pairs, and the cumulative number of times they are recorded in different time windows is counted. Then, these frequency data are appended to the corresponding path entries to form a path annotation set containing recurrence information. On this basis, the annotation set is indexed and organized according to the node pairs and the time dimension to establish an association strength index table with the node pairs as the primary key and the path recurrence frequency and time distribution as the index items. The final index table can structurally store the recurrence and persistence characteristics of cross-chain paths.

[0124] Step S305: write the result of the hierarchical combination into a rating index area in the cross-chain ledger, and dynamically rate the cross-chain interaction security of each blockchain.

[0125] The specific steps of step S305 are:

[0126] Step S3051: split the hierarchical combination result according to the blockchain network dimension, and generate a corresponding rating label for each dimension.

[0127] In this embodiment, when the hierarchical combination result is split according to the blockchain network dimension, first, the node pairs, path information and correlation strength data in the cross-time dimension hierarchical combination are classified into independent data blocks according to the network identifier; then, in each data block, the interaction intensity, abnormal distribution and path repetition of the network in different time windows are comprehensively counted to obtain a feature set representing the stability and activity of the chain in cross-chain interaction; after the feature set is constructed, it is converted into a corresponding rating label according to the preset grading rules, and a time index and a chain identifier are added to each label to ensure that the label result can reflect the interaction characteristics of a single chain and maintain comparability in the cross-chain dimension; finally, the rating label is generated.

[0128] Step S3052: according to the rating label, call the preset index generation rule to bind the rating label with the node identifier in the inter-chain interaction relationship graph.

[0129] In this embodiment, when the rating label is called to call the preset index generation rule, first, the node identifier corresponding to each blockchain network is retrieved in the inter-chain interaction relationship graph, and it is one-to-one corresponding with the rating label generated after hierarchical splitting; then, according to the index generation rule, the risk level, time index and chain identifier information in the rating label are encoded into index items and bound with the node identifier to form index items with dual attributes of node dimension and network dimension; in the binding process, multiple rating labels of the same node in different time windows are sequentially arranged to ensure that the dynamic evolution track of the node interaction characteristics is completely retained; finally, the result obtained is the bound index set.

[0130] Step S3053: write the binding result in the rating index area of the cross-chain ledger according to the time sequence, and establish a unique index number for each record.

[0131] In this embodiment, when writing the binding result in the rating index area of the cross-chain ledger in chronological order, the generated index set is first sorted according to the timestamp, ensuring that the rating labels and node binding entries in different time windows can form an ordered write sequence; then the sorted binding result is registered in the rating index area piece by piece, and an independent index number is assigned to each record during the registration process, which is generated by combining the timestamp, node identifier and chain identifier, ensuring its global uniqueness; after completing the registration, the index numbers of the same node in different time windows are chained in series, so that they can reflect the evolution trajectory of the cross-chain interaction rating result; finally, a unique index number system is formed, so that the rating result has traceability and positioning in the ledger.

[0132] Step S3054: generating a cross-chain interaction security rating table according to the unique index number, and establishing a mapping relationship between the cross-chain interaction security rating table and the historical rating data in the cross-chain ledger.

[0133] The specific steps of step S3054 are:

[0134] Step S30541: aggregating and dividing the hierarchical combination result according to the unique index number, and generating an initial rating list with cross-chain nodes as index items.

[0135] In this embodiment, when aggregating and dividing the hierarchical combination result according to the unique index number, first, the node pairs involved in the hierarchical combination and their associated strengths are classified according to the index number, so that the interaction information of the same node in different time windows can be concentrated under the same index entry; then, taking the cross-chain node as the core index item, the classified interaction strength, path repetition frequency and abnormal marker are aggregated, and divided into continuous rating fragments according to the time sequence; after completing the aggregation and division, an entry unit containing interaction features and security attributes is generated for each node, and an initial rating list is formed in the form of a list.

[0136] Step S30542: comparing the initial rating list with the index items of the historical rating data in the cross-chain ledger, and establishing a mapping table at the node level.

[0137] In the embodiment, when the initial rating list is compared with the index items of historical rating data in the cross-chain ledger, first, the node identifier is taken as the comparison primary key, the entries in the initial rating list are retrieved one by one, and the historical rating records of the corresponding nodes and their index items are called in the ledger; then the rating labels, interaction intensity and path characteristics of the same node in different time periods are compared, the same points and difference points are identified, and the time index is retained in the process to ensure that the comparison result can reflect the longitudinal evolution law; after the comparison is completed, a mapping table at the node level is generated, which takes the node as the core index, establishes a one-to-one correspondence between the initial rating list and the historical rating items, and adds a difference mark in the items; finally, the mapping table is formed.

[0138] Step S30543: Identifying the difference fragments of the rating results in the mapping table, and binding them with the circulation track corresponding to the cross-chain account group.

[0139] In the embodiment, when the difference fragments of the rating results are identified in the mapping table, first, the rating labels and interaction intensity of the same node in the initial rating list and the historical rating data are compared, the difference parts such as numerical mutation, grade promotion or decline are extracted, and the difference fragments are divided into a difference fragment set; then the difference fragments are cross-matched with the cross-chain account groups recorded in the ledger, the specific account set involved by the difference fragments is determined through the node identifier and the account group mapping relationship; after the positioning of the account set is completed, the circulation track corresponding to the account group is called again, and the difference fragments are bound with the related track to ensure that a direct corresponding relationship between the rating change and the cross-chain circulation behavior is established; finally, the binding result is obtained.

[0140] Step S30544: Based on the difference fragments and the binding information, a cross-chain interaction security rating table is generated, and a mapping relationship between the cross-chain interaction security rating table and the historical rating data is registered in the cross-chain ledger.

[0141] In this embodiment, when generating the cross-chain interaction security rating table based on the difference fragments and the binding information, first, the node rating changes extracted in the difference fragments are integrated with the bound cross-chain circulation track, and they are classified and summarized according to time sequence and blockchain network dimension to form a rating entry set with multi-dimensional index attributes; then, according to the preset tabular coding rule, each rating entry is converted into a standardized data unit, and in this process, the node identifier, account group information and time index are retained to ensure that the rating table has complete traceability logic; after the cross-chain interaction security rating table is constructed, it is registered in the rating index area of the cross-chain ledger, and a mapping relationship with historical rating data is established at the same time, so that the new rating result can be connected with the existing data; finally, the cross-chain interaction security rating table is generated, which not only reflects the security state of the node in a specific time window, but also forms a vertically comparable evaluation chain through the mapping of historical rating data, providing continuous data support for dynamic analysis and long-term trend analysis.

[0142] Step S4: When the cross-chain security entropy index is detected to be abnormal, the value of the reputation token is automatically adjusted and a preset game mechanism is triggered to optimize the security strategy of the blockchain network.

[0143] In this embodiment, when the cross-chain security entropy index is detected to be abnormal, first, the abnormal category is determined by real-time comparison with the reference threshold interval, and the value adjustment logic of the reputation token is triggered accordingly, and the measurement weight of the token is dynamically revised in the ledger; then, the revised token value is used as the game input parameter to start the preset game mechanism, in which different blockchain network nodes select strategies according to their risk and return preferences, and the system automatically records and evaluates the stability of each strategy combination; in the process of multiple iterations, the game mechanism gradually filters out the strategy set that can reduce the abnormal risk and solidifies it as a security parameter update scheme; finally, the update scheme is written into the security strategy configuration of the blockchain network, so that the network can realize adaptive optimization when facing cross-chain abnormalities, thereby maintaining the dynamic stability and sustainability of the cross-chain interaction environment.

[0144] In Figure 3In the cross-chain interaction security rating table, a unique index number is used as the core primary key to identify the uniqueness of each rating record, such as IDX-001, IDX-002, etc. The index number is generated by time information and node identification, ensuring that the rating results of different blockchains do not conflict. The blockchain network identification and node identification are used to indicate the specific blockchain and node identity of the rating object, such as node A1 and node A2 in chain A, node B3 in chain B, and node C1 in chain C. This identification method allows the rating table to cover different interaction subjects in a multi-chain environment. The rating label field is used to store the security level results generated according to the hierarchical combination and index generation rules, such as security level I, security level II, and security level III. The label can intuitively reflect the security performance of the node in cross-chain interaction. The interaction intensity indicator and path frequency field together represent the activity and stability of node interaction. The interaction intensity indicator (such as high, medium, and low) reflects the strength of the relationship between nodes in the interaction relationship graph, and the path frequency records the number of repeated occurrences of the node in the cross-chain circulation track, such as 3 times, 5 times, etc., which is used to further quantify the basis for security rating. In summary, Figure 3 The cross-chain interaction security rating table shown in the figure realizes the dynamic security quantitative evaluation of each blockchain node through the combination of unique index number, network identification, node identification, rating label, and interaction data.

[0145] Embodiment 2:

[0146] Please refer to Figure 4 Another embodiment provided by the present application is a security rating system based on cross-chain interaction of blockchains, which includes a data acquisition module, a reputation token generation module, a security rating module, and an optimization module.

[0147] The data acquisition module is used to collect transaction data, node behavior data, and delay and abnormal characteristics of cross-chain communication links of each blockchain network during cross-chain interaction, and generate a cross-chain security entropy index.

[0148] The reputation token generation module is used to generate and manage reputation tokens based on the cross-chain security entropy index through a smart contract.

[0149] The security rating module is used to dynamically rate the cross-chain interaction security of each blockchain based on the generation, circulation, and historical record of the reputation tokens, and record the security rating results in a hierarchical form in the blockchain ledger.

[0150] The optimization module is used to automatically adjust the value of the reputation tokens and trigger a preset game mechanism to optimize the security strategy of the blockchain network when detecting an abnormal cross-chain security entropy index.

[0151] In addition, parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of corresponding technical solutions in the prior art are not described in detail to avoid excessive repetition.

[0152] The specific embodiments described above are further explained in connection with the purposes, technical solutions, and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A security rating method based on blockchain cross-chain interaction, characterized in that, include: During cross-chain interaction, transaction data, node behavior data, and latency and anomaly characteristics of cross-chain communication links from various blockchain networks are collected to generate cross-chain security entropy indicators. Based on the cross-chain security entropy index, reputation tokens are generated and managed through smart contracts. These reputation tokens are used to characterize the security of each blockchain, and the reputation tokens of different blockchains circulate under the cross-chain interaction protocol. Based on the generation, circulation, and historical records of the reputation tokens, the security of cross-chain interactions of each blockchain is dynamically rated, and the security rating results are recorded in the blockchain ledger in a hierarchical manner. When an anomaly in the cross-chain security entropy metric is detected, the value of the reputation token is automatically adjusted and a preset game mechanism is triggered to optimize the blockchain network security strategy. The generation of cross-chain security entropy metrics includes: Collect transaction time sequence information of different blockchain nodes during cross-chain interaction, segment it into segments, and generate basic time sequence fragments; Communication link anomaly detection is performed on the basic time series segments to identify features such as delay mutations, repeated propagation, and data loss, and the features are mapped into anomaly vectors. The anomaly vector is jointly encoded with the node behavior data to form a cross-chain interaction feature matrix; Based on the cross-chain interaction feature matrix, the cross-chain data stream is analyzed in a distributed manner to generate a cross-chain security entropy index. The cross-chain data stream is obtained based on the transaction data, node behavior data, and latency and anomaly characteristics of the cross-chain communication link of each blockchain network. Based on the aforementioned cross-chain security entropy metric, reputation tokens are generated and managed via smart contracts, including: Based on the time series of the cross-chain security entropy index, a dynamic threshold range is set, and the dynamic threshold range is mapped to the initial amount of reputation tokens to be generated; By invoking the randomness commitment of the cross-chain witness node through a smart contract, the initial quantity is bound to the invocation result of the cross-chain witness node, thus forming a request to generate reputation tokens; In the cross-chain interaction protocol, reputation token generation requests from different blockchain networks are mutually calibrated to eliminate isolated or contradictory reputation token generation records and obtain a consensus-confirmed reputation token issuance sequence. Based on the reputation token issuance sequence, create cross-chain circulating reputation tokens and store them in a preset cross-chain account cluster in real time; Based on the aforementioned reputation token issuance sequence, a cross-chain circulating reputation token is created, including: A unique cross-chain mapping token is generated for each reputation token based on the identifier in the reputation token issuance sequence, and the cross-chain mapping token is written into the original block segment of the cross-chain ledger; Based on the correlation of different cross-chain account clusters, the reputation tokens are allocated to the corresponding cross-chain account groups according to the mapping rules and simultaneously written into the circulation table of the cross-chain ledger. In the circulation table of the cross-chain ledger, an inter-chain transferable path is established for each reputation token, and the inter-chain transferable path is bound to the node identity in the cross-chain interaction protocol. After the path binding is completed, the cross-chain circulation order of reputation tokens is recorded in sequence according to the sorting mechanism of the cross-chain ledger, and the cross-chain circulating reputation tokens are obtained.

2. The security rating method based on blockchain cross-chain interaction as described in claim 1, characterized in that, Based on the generation, circulation, and historical records of the reputation tokens, a dynamic rating is performed on the cross-chain interaction security of each blockchain, including: Extract the generation sequence of reputation tokens across various blockchains and establish a mapping table between generation events and the identities of corresponding in-chain nodes; According to the mapping table, the transfer paths of reputation tokens between different blockchains are serialized to form a cross-chain circulation trajectory set; The aggregation and dispersion patterns among nodes are identified in the cross-chain circulation trajectory set, and compared with the preset cross-chain account groups to generate an inter-chain interaction relationship diagram. Based on the inter-chain interaction diagram, the correlation strength of each blockchain in the cross-chain interaction process is calculated, and the generated events, circulation trajectories and historical accumulated data are combined in layers. The results of the layered combination are written into the rating index area of ​​the cross-chain ledger to dynamically rate the cross-chain interaction security of each blockchain.

3. The security rating method based on blockchain cross-chain interaction as described in claim 2, characterized in that, The aggregation and dispersion patterns among nodes in the cross-chain circulation trajectory set are identified and compared with preset cross-chain account groups to generate an inter-chain interaction relationship graph, including: The cross-chain transfer events in the cross-chain circulation trajectory set are divided into time sequences to establish a trajectory fragment set with time windows as units; The frequency of node interactions within the same time window is counted in the trajectory segment set, and the segments are divided into clustered segments and dispersed segments based on the frequency of node interactions. The aggregated and dispersed fragments are matched and verified against a preset cross-chain account group to identify the set of nodes associated with the preset cross-chain account group. Based on the node set and its corresponding clustered and dispersed segments, an inter-chain interaction graph is constructed, and the interaction edges and cross-chain paths between nodes are recorded in the inter-chain interaction graph.

4. The security rating method based on blockchain cross-chain interaction as described in claim 3, characterized in that, Based on the inter-chain interaction diagram, the correlation strength of each blockchain in the cross-chain interaction process is calculated, and the generation events, circulation trajectories, and historical accumulated data are layered and combined, including: Extract the number and length of cross-chain paths between node pairs from the inter-chain interaction graph and map them to basic correlation degree units. Based on the aforementioned basic correlation unit, the generated events and flow trajectories within the same time window are aggregated to form a time-layered correlation strength matrix; The correlation strength matrices of different time windows are stacked sequentially and combined with historical cumulative data to generate a hierarchical combination across time dimensions; In the hierarchical combination, the frequency of recurrence of cross-chain paths is marked, and an association strength index table is established.

5. The security rating method based on blockchain cross-chain interaction as described in claim 4, characterized in that, The results of the layered combination are written into the rating index area of ​​the cross-chain ledger to dynamically rate the cross-chain interaction security of each blockchain, including: The hierarchical combination results are segmented according to the blockchain network dimension, and a corresponding rating label is generated for each dimension; Based on the rating tags, a preset index generation rule is invoked to bind the rating tags to the node identifiers in the inter-chain interaction graph; The binding results are written sequentially in the rating index area of ​​the cross-chain ledger according to the time order, and a unique index number is created for each record; Based on the unique index number, a cross-chain interaction security rating table is generated, and a mapping relationship is established between the cross-chain interaction security rating table and the historical rating data in the cross-chain ledger.

6. The security rating method based on blockchain cross-chain interaction as described in claim 5, characterized in that, Based on the unique index number, a cross-chain interaction security rating table is generated, and a mapping relationship is established between the cross-chain interaction security rating table and the historical rating data in the cross-chain ledger, including: The hierarchical combination results are aggregated and segmented based on the unique index number, and an initial rating list is generated with cross-chain nodes as index items. The initial rating list is compared with the index entries of historical rating data in the cross-chain ledger, and a node-level mapping table is established. The difference segments in the rating results are identified in the mapping table and bound to the circulation trajectory corresponding to the cross-chain account group; Based on the difference fragments and binding information, a cross-chain interaction security rating table is generated, and the mapping relationship between the cross-chain interaction security rating table and historical rating data is registered in the cross-chain ledger.

7. A security rating system based on blockchain cross-chain interaction, used to implement the security rating method based on blockchain cross-chain interaction as described in any one of claims 1-6, characterized in that, include: Data acquisition module, reputation token generation module, security rating module, and optimization module; The data acquisition module is used to collect transaction data, node behavior data, and latency and anomaly characteristics of cross-chain communication links from various blockchain networks during cross-chain interaction, and generate cross-chain security entropy indicators. The reputation token generation module is used to generate and manage reputation tokens through smart contracts based on the cross-chain security entropy index. The security rating module is used to dynamically rate the cross-chain interaction security of each blockchain based on the generation, circulation and historical records of the reputation token, and record the security rating results in the blockchain ledger in a hierarchical manner. The optimization module is used to automatically adjust the value of reputation tokens and trigger a preset game mechanism when an abnormal cross-chain security entropy index is detected, thereby optimizing the blockchain network security strategy.

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