Block chain financial identity authentication system

Abstract

The invention relates to the technical field of block chain identity authentication, in particular to a block chain financial identity authentication system, which comprises a behavior feature acquisition module, an identity behavior portrait module, a contract response judgment module, a trust node scheduling module and a block behavior writing module. According to the method, dynamic features such as the keystroke rhythm, the sliding track and the touch pressure in user registration interaction are collected, the interval sequence and the deviation angle are extracted, the terminal model, the input frequency and the track flow direction are combined, a behavior feature matrix under the condition that the input mode is paired with the terminal is constructed, and identity tag exclusive uniqueness and environmental adaptability are achieved; in interactive verification, path offset and equipment information are fused, the authentication level is dynamically adjusted, strategy elasticity and responsiveness are improved, node screening and task distribution are optimized based on response time and consistency score, distributed authentication efficiency is improved, authentication results are encrypted, signed and issued and written into a block chain, and process traceability and data security are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchain identity authentication, and particularly relates to a blockchain financial identity authentication system. BACKGROUND

[0002] The technical field of blockchain identity authentication includes related technologies for managing and verifying user identity information based on blockchain technology. The core content of this technical field is to utilize the distributed ledger feature of blockchain to achieve decentralized storage and trusted verification of identity information. By chaining user identity data, consistency and non-tamperability among multiple nodes are ensured, and identity information can be securely shared between different systems. This technical field covers multiple aspects such as registration, authentication, management, and access control of digital identity. Common implementation methods include combining cryptographic methods for data encryption, using smart contracts to automatically execute identity verification logic, and using consensus mechanisms to ensure the credibility of the identity authentication process. This field also involves the design of multi-party identity interaction mechanisms and the application of privacy protection strategies to ensure user privacy while effectively verifying identity.

[0003] Among them, the blockchain financial identity authentication system refers to a system that provides identity authentication support for users in financial scenarios based on blockchain technology. It mainly aims to meet the trusted verification needs of user real identity in financial business, covering on-chain registration of user identity information, design of identity authentication process based on asymmetric encryption, consensus confirmation process of identity verification records, and use of smart contracts to set identity permission calling rules. By hashing and chaining user identity data, data integrity and tamper resistance are ensured, and digital signature mechanisms are combined for user identity confirmation and verification to ensure the uniqueness and traceability of the authentication process. The system also implements trusted sharing of identity authentication results through the consensus process between nodes to ensure information consistency and mutual trust in identity authentication between multiple financial institutions.

[0004] The prior art can realize the on-chain of identity information based on the blockchain and the decentralized verification, but mainly relies on the static identity data and the preset verification path for authentication, lacks the in-depth analysis of the user operation behavior, and is difficult to accurately identify the pseudo operation or unauthorized behavior. When facing highly similar identity registration information, only the user information hash value and the digital signature cannot establish the behavior dimension identification of the user, so that the multi-user on the same device exists the authentication overlap risk. In terms of authentication level response, the current scheme is mainly a fixed mechanism, which cannot make dynamic adjustment according to different terminals, input behavior differences and environmental fluctuations, causing the authentication mechanism response to be insufficient in some high-risk interactions and being easily bypassed. The consensus node scheduling is mainly based on the average response, without clear execution ability sorting, causing the node congestion in the high-frequency verification scene and the verification response delay. The identity behavior details are not enough in the block writing process, the authentication track is incomplete, and the subsequent traceability judgment is affected. Although the verification structure taking the digital signature as the core ensures the integrity of the original data, the process behavior chain is lacked, and there is a potential risk of insufficient authentication effectiveness, which is particularly prominent in sensitive scenes such as high-frequency financial transactions and cross-platform identity synchronization, and easily causes the identity impersonation and trust mismatch risk. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art and to provide a blockchain financial identity authentication system.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: a blockchain financial identity authentication system comprises: The behavior characteristic acquisition module acquires the input behavior data in the financial identity authentication registration page, acquires the keystroke rhythm, sliding track and touch pressure of the user, extracts the rhythm sequence and track offset, compares the track length and time length fluctuation, selects the high-frequency behavior characteristic combination, and generates a blockchain identity input behavior characteristic map; The identity behavior portrait module identifies the operation rhythm and device habit based on the blockchain identity input behavior characteristic map, combines the terminal model, typing frequency and track flow direction, extracts the input method and terminal pairing characteristics, and generates an identity behavior portrait label; The contract response judgment module extracts the user behavior data according to the identity behavior portrait label, analyzes the offset degree of the current input path and the original behavior label, combines the device fingerprint and access address, judges whether the authentication mechanism level needs to be adjusted according to the contract condition, and generates a contract dynamic verification level set; The trust node scheduling module calls the contract dynamic verification level set, retrieves the response time, verification frequency and consistency score in the consensus node pool, evaluates the node execution ability sorting, selects the node group at the front of the sorting for verification task distribution, and obtains a financial identity authentication node execution sequence.

[0007] As a further scheme of the present application, the blockchain identity input behavior feature graph comprises keystroke rhythm difference factors, trajectory angle offset indicators, input stability metrics, behavior pattern correlation degrees, input fluctuation frequencies, the blockchain identity behavior portrait label comprises terminal adaptation features, operation rhythm labels, input method preferences, behavior inertia parameters, device usage features, the contract dynamic verification level set comprises path deviation magnitudes, fingerprint consistency levels, address trust weights, authentication dynamic identifiers, contract response labels, and the financial identity authentication node execution sequence comprises response delay sequences, verification task numbers, node execution weights, dispatch scheduling parameters, and consensus priority labels.

[0008] As a further scheme of the present application, the behavior feature acquisition module comprises: The keystroke rhythm extraction submodule acquires input behavior data in the financial identity authentication registration page, extracts keystroke intervals and arranges them in order, analyzes interval fluctuation amplitudes, filters key keystroke time periods of the fluctuation amplitudes, and generates keystroke rhythm fluctuation feature graphs; The trajectory parameter recognition submodule extracts user input sliding trajectory data according to the keystroke rhythm fluctuation feature graphs, identifies path lengths and offset angles, analyzes trajectory length and input time length fluctuations, filters input behavior segments of offset amplitudes, and obtains trajectory length fluctuation intervals; The behavior feature construction submodule calls the trajectory length fluctuation intervals, combines touch pressure data, analyzes keystroke and sliding behaviors in differentiated time periods, filters associated behavior feature combinations, and generates blockchain identity input behavior feature graphs.

[0009] As a further scheme of the present application, the identity behavior portrait module comprises: The transaction signature analysis submodule extracts keystroke frequencies and trajectory coordinates in a blockchain transaction signing process based on the blockchain identity input behavior feature graphs, calculates transaction behavior entropy values, performs pattern matching with a signature benchmark library of a blockchain ledger record, and generates transaction signature rhythm patterns; The node verification feature submodule calls the transaction signature rhythm patterns, analyzes device protocol stack features and encryption key usage frequencies in blockchain node interaction logs, and obtains a node verification feature set; The trustworthiness evaluation submodule performs on-chain trustworthiness weighted identification on multi-dimensional behavior features through an identity authentication rule engine deployed by a smart contract based on the node verification feature set, and generates identity behavior portrait labels.

[0010] As a further scheme of the present application, the contract response judgment module comprises: The behavior deviation identification submodule identifies the operation path of the user in the blockchain financial identity authentication interaction according to the identity behavior portrait label, calculates the deviation track difference of the user from the registered behavior path, judges the path consistency deviation interval, and obtains the behavior path deviation feature; The device fingerprint verification submodule extracts the current device fingerprint and access address based on the behavior path deviation feature, compares the structure matching of the current device fingerprint and access address with the combination of the historical identity authentication fingerprints stored on the chain, judges the fingerprint stability level, and obtains the device trust identification factor; The authentication level adjustment submodule calculates the dynamic authentication complexity according to the device trust identification factor, matches the corresponding verification scheme according to the contract rules, and generates a contract dynamic verification level set.

[0011] As a further scheme of the present application, the trust node scheduling module comprises: The node capability scoring submodule calls the contract dynamic verification level set, extracts the average response time, identity authentication frequency and consistency score of the nodes in the consensus node pool, analyzes the execution stability and response capability indicators of the nodes, calculates the node execution capability score value, and obtains a blockchain authentication node capability indicator set; The authentication node dispatching submodule filters the node set with high indicators according to the blockchain authentication node capability indicator set, determines the task priority delivery order in combination with the current timeliness demand of the verification task and the node geographic network structure relationship, and obtains a financial identity authentication node execution sequence.

[0012] As a further scheme of the present application, the system further comprises a block behavior writing module: The block behavior writing module extracts the identity authentication result and contract verification process record based on the financial identity authentication node execution sequence, calls the issuance order and encryption strategy code, integrates the behavior feature abstract and contract execution record to write into the ledger, and obtains an identity authentication on-chain record; The identity authentication on-chain record comprises an authentication result abstract, an execution process label, a feature hash code, an issuance encryption identifier, and a ledger index entry.

[0013] As a further scheme of the present application, the block behavior writing module comprises: The identity authentication extraction submodule extracts the identity information and contract verification record based on the financial identity authentication node execution sequence, filters according to the identity trustworthiness and contract matching degree, and combines the identity authentication and contract verification data to obtain an identity authentication behavior feature; The issuance strategy calling submodule extracts the issuance order and encryption strategy code according to the identity authentication behavior feature, matches the identity authentication behavior feature and the issuance strategy, identifies the adaptability of the encryption strategy code in the issuance order, and generates an encryption adaptation strategy; The record storage generation submodule integrates the contract execution record and the identity authentication behavior characteristics according to the encryption adaptation strategy, analyzes the mapping relationship of the fields in the contract execution record, constructs the blockchain writing structure of the contract execution according to the mapping relationship, and generates the identity authentication on-chain record storage.

[0014] Compared with the prior art, the application has the advantages and positive effects that: In the application, by collecting the keystroke rhythm, sliding trajectory and touch pressure in user registration interaction, and extracting dynamic behavior characteristics such as interval sequence, offset angle and fluctuation amplitude, fine-grained analysis of individual input mode is realized, the sensitive capture ability of behavior level information is enhanced, and further combined with terminal model, input frequency and trajectory flow direction, normal operation habits and device use preferences are mined, a behavior characteristic matrix under the input mode and terminal pairing is constructed, so that the construction of the identity tag is more exclusive and unique and adaptive to the use environment. In the interactive behavior verification process, the path offset degree evaluation and device information are combined to judge, a dynamic judgment adjustment mechanism of authentication level is realized, so that the identity verification strategy is more flexible and scene responsive. The screening basis of the execution node is sorted and optimized according to the response time and consistency score, so as to ensure the accurate and efficient distribution of the verification task, and improve the execution consistency and response efficiency of the identity authentication in the distributed network. The authentication result is integrated after the feature abstraction, and then encrypted and issued, and written into the block account book, to form a complete and traceable verification record chain, so as to ensure the data integrity, security and traceability of the authentication process. The overall processing logic introduces dynamic behavior trajectory analysis, usage habit recognition and node scheduling strategy, and improves the individual distinguishability, strategy adaptability and trusted closed-loop of the whole verification process of the identity authentication. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The system flowchart of the application is shown in the figure; Figure 2 The behavior characteristic acquisition module flowchart in the application is shown in the figure; Figure 3 The identity behavior portrait module flowchart in the application is shown in the figure; Figure 4 The contract response judgment module flowchart in the application is shown in the figure; Figure 5 The trust node scheduling module flowchart in the application is shown in the figure; Figure 6 The block behavior writing module flowchart in the application is shown in the figure. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the application more clear and explicit, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application, and are not used to limit the application.

[0017] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0018] Please refer to Figure 1 A blockchain financial identity authentication system comprises: The behavior feature acquisition module acquires input behavior data in the financial identity authentication registration page, collects the keystroke rhythm, sliding trajectory and touch pressure of the user in the password entry area, extracts the keystroke interval sequence and trajectory offset angle, compares the fluctuation amplitude of the trajectory length and input duration, selects the high-frequency behavior feature combination, and generates a blockchain identity input behavior feature map; The identity behavior portrait module identifies the common operation rhythm and device usage habit based on the blockchain identity input behavior feature map, combines the terminal model, typing frequency and trajectory flow direction during registration, extracts the behavior parameter features of the input method and terminal pairing, and generates an identity behavior portrait label; The contract response judgment module extracts the behavior data of the user in the financial identity authentication interaction area according to the identity behavior portrait label, analyzes the offset degree of the current input path and the original behavior label, and combines the device fingerprint and access address to judge whether the authentication mechanism level needs to be adjusted according to the contract condition, and generates a contract dynamic verification level set; The trust node scheduling module calls the contract dynamic verification level set, retrieves the response time, verification frequency and consistency score in the consensus node pool, evaluates the node execution capability ranking, selects the node group at the front of the ranking for verification task distribution, and obtains a financial identity authentication node execution sequence; The block behavior writing module extracts the identity authentication result and contract verification process record based on the financial identity authentication node execution sequence, calls the issuance sequence and encryption strategy code, integrates the behavior feature abstract and contract execution record to write into the ledger, and obtains an identity authentication on-chain record.

[0019] The blockchain identity input behavior feature map includes keystroke rhythm difference factors, trajectory angle deviation indicators, input stability metrics, behavior pattern correlation degrees, and input fluctuation frequencies. The blockchain identity behavior portrait labels include terminal adaptation features, operation rhythm labels, input method preferences, behavior inertia parameters, and device usage features. The contract dynamic verification level set includes path deviation magnitudes, fingerprint consistency levels, address trust weights, authentication dynamic identifiers, and contract response labels. The financial identity authentication node execution sequence includes response delay sequencing, verification task numbering, node execution weights, dispatch scheduling parameters, and consensus priority labels. The identity authentication on-chain record includes authentication result summaries, execution process labels, feature hash encodings, issued encryption identifiers, and ledger index entries.

[0020] Please refer to Figure 2 The behavior feature collection module includes: The keystroke rhythm extraction submodule obtains input behavior data in the financial identity authentication registration page, extracts keystroke intervals and arranges them in order, analyzes interval fluctuation amplitudes, filters key keystroke time periods of fluctuation amplitudes, and generates keystroke rhythm fluctuation feature maps. First, input behavior data in the financial identity authentication registration page is obtained. The data can be obtained by recording the timestamp of each time the user presses the keyboard. When the user completes the input behavior, the time difference between each press and release of the key is calculated as the keystroke interval. The keystroke intervals are arranged in order to analyze their fluctuation amplitudes. In the analysis process, the calculation of the fluctuation amplitude involves obtaining the time difference of each pair of consecutive keystrokes and calculating the standard deviation of the time difference as a measure of the fluctuation amplitude. Each group of consecutive keystroke intervals (e.g., the time interval between two keystrokes) is counted, and the fluctuation is identified by calculating the trend of each pair of intervals. For example, assuming that the user inputs "1234" with keystroke intervals of 200ms, 180ms, and 210ms, the standard deviation of the time interval is calculated, and this value is taken as the fluctuation amplitude of the keystroke rhythm. In filtering the key keystroke time period, a threshold is set according to the keystroke interval fluctuation value, for example, when the fluctuation amplitude exceeds a certain threshold, it is determined that it is a key keystroke period. The period can reflect the irregularity or abnormal behavior of the user during input. Finally, the keystroke rhythm fluctuation feature map is generated.

[0021] The trajectory parameter recognition submodule extracts user input sliding trajectory data according to the keystroke rhythm fluctuation feature map, identifies path length and offset angle, analyzes trajectory length and input time length fluctuation, and filters input behavior segments of offset amplitude to obtain trajectory length fluctuation intervals. The key trajectory features are extracted from the user inputted sliding trajectory data. The user's inputted trajectory is analyzed, the path length and the deflection angle are calculated. The path length refers to the distance from the starting point to the ending point, and the deflection angle refers to the direction change angle of the trajectory in the sliding process. The calculation of the path length depends on the sum of the straight line distances between the continuous sliding points, and the calculation of the deflection angle is obtained by calculating the angle change between adjacent trajectory points one by one. In order to analyze the fluctuation of the trajectory length and the input time length, the starting time and the ending time of each sliding are recorded, the input time length is calculated, and the path length is compared. If the ratio between the input time length and the path length appears significant fluctuation, the time period is identified as the trajectory fluctuation interval. For example, if the user keeps a short path movement for a long time, the abnormal behavior can be identified by the ratio calculation. The fluctuation of the deflection amplitude is screened, and the relative deflection value of each trajectory point is monitored. When the deflection amplitude of a certain trajectory segment exceeds a certain set threshold, the trajectory segment is marked as abnormal. The trajectory length fluctuation interval is obtained through the identification of the fluctuation interval.

[0022] The behavior feature construction submodule calls the trajectory length fluctuation interval, combines the touch pressure data, analyzes the keystroke and sliding behavior in the differentiated time period, screens the associated behavior feature combination, and generates a blockchain identity input behavior feature map; The trajectory length fluctuation interval and the touch pressure data are combined to further analyze the keystroke and sliding behavior in the differentiated time period. Through the analysis of the touch pressure data, the pressure change of the user in the specific input behavior is determined, so as to infer the difference of the behavior feature. By comparing the pressure data in different time periods, the input behavior segment with large pressure fluctuation can be identified, and the associated behavior feature is extracted by combining the corresponding keystroke rhythm or sliding trajectory fluctuation. First, the touch pressure value at each moment is recorded, and the corresponding keystroke and trajectory data are compared. For example, when the user presses a certain key for a long time, the touch pressure will gradually increase. The behavior is identified by the pressure data fluctuation. The difference between the keystroke and sliding behavior in different time periods is analyzed. For example, when the user inputs rapidly or quickly slides, the pressure fluctuation and the keystroke rhythm fluctuation are associated. By screening the behavior feature combination in the differentiated time period, a blockchain identity input behavior feature map is generated.

[0023] Please refer to Figure 3 , the identity behavior portrait module includes: The transaction signature analysis submodule extracts the keystroke frequency and trajectory coordinates in the blockchain transaction signing process based on the blockchain identity input behavior feature map, and uses the formula: ; The transaction behavior entropy value is calculated, and the transaction signature rhythm pattern is generated by pattern matching with the signature benchmark library recorded in the blockchain ledger; Wherein, S represents the transaction behavior entropy value, T represents the average input time length of a single transaction, V represents the trajectory turning point density, B and U represent the longitudinal coordinate displacement rate, represents the timestamp interval of the kth transaction, and θ is the blockchain node type correction factor, and n is the number of transactions in the verification period; The transaction behavior entropy value is an index for quantifying the complexity and uncertainty of user input behavior during transaction, in the analysis of blockchain transaction signature, the entropy value is calculated by comprehensively evaluating the rhythm, trajectory change, keystroke frequency and other dynamic behavior characteristics of user input. Specifically, the higher the entropy value, the more complex and uncertain the user's input behavior, while the lower the entropy value, the simpler and more regular the user's behavior. The transaction behavior entropy value can not only help identify whether the user's signature behavior is normal, but also be used to distinguish between fake or abnormal transaction input behavior. By analyzing the entropy value of transaction behavior, it can be verified whether the transaction signature conforms to the specific operation mode, thereby improving the security and verifiability of the transaction, and the transaction behavior entropy value is a key measurement tool to ensure that each transaction input behavior is consistent with the preset normal user operation, thereby enhancing the security and anti-forgery capability of the blockchain; The blockchain transaction signature analysis is based on the behavior trajectory of user transaction input process, first, the dynamic data parameters of user in a complete transaction input behavior are collected, including keystroke frequency T per unit time, trajectory turning density V, trajectory longitudinal coordinate moving speed up value U and down value B, time interval of k consecutive transaction behaviors, and the node type influence factor θ of the device and network, all original data are processed by dimension conversion to ensure that each parameter has a unified calculation basis. For example, the keystroke frequency T is in units of "times per second", assuming that a user inputs 8 times per second, T=8, the trajectory turning density V is obtained by recording the number of direction changes per unit length of the touch path, for example, the path length is 10 cm, and the direction change number is 5, then V=0.5 (change / cm), B and U are 0.3 cm / s and 0.2 cm / s respectively, to avoid abnormal calculation values with a range of 0, the lower limit of the range is set to 0.01, θ represents the influence degree of node type on behavior fluctuation, for example, θ=1 for ordinary node, θ=1.2 for miner node, The time interval of each transaction is obtained by recording the time difference of multiple transactions, assuming that k=3, the time interval of each transaction is 2s, 3s, and 4s respectively, then , , All time dimensions are unified to seconds; The first part calculation: seconds, transformations / cm, cm / s, cm / s, cm / s; The first term is: ; The second term is calculated: , ; Then: ; The final result is: ; The value S is the behavior entropy value of this transaction signature operation behavior, the unit is dimensionless output, which is used to represent the operation rhythm intensity of this signature operation, and the value is compared in the preset rhythm intensity threshold interval, and the preset reference interval is [10, 20], which represents that the behavior intensity of the normal user should fall within the interval; The results show that S = 12.419 falls within the interval, so it can be considered that the transaction input behavior matches the normal user operation behavior in rhythm characteristics, and then a transaction signature rhythm pattern is generated as an intermediate result of identity recognition. Through the stereoscopic combination calculation of trajectory change density, input frequency and trajectory flow speed difference, the behavior rhythm characteristics are more different, thereby improving the discriminability of transaction behavior data.

[0024] The node verification feature submodule calls the transaction signature rhythm pattern, analyzes the device protocol stack features and the use frequency of encryption keys in the blockchain node interaction log, and obtains a node verification feature set; The device features and the use frequency of encryption keys of the blockchain node will be the main analysis object. According to the transmission protocol in the blockchain protocol stack, the device features are analyzed. By calling the transaction signature rhythm pattern generated in the previous step, the device protocol stack features related to each transaction operation can be extracted, and cross analysis is performed with the encryption key use frequency of the node. For example, the encryption key used by a certain node during transaction triggers a frequency of twice per minute, and the communication delay in the device protocol stack is 200 milliseconds. Through this information, a detailed verification feature set can be generated for the node. The feature set includes the characteristics of the node in different transaction periods, including communication delay, encryption key trigger frequency, and protocol stack data transmission quality, etc. The features will be summarized as the verification information of the node, and will be used as the basis for further identity verification, to ensure that the node behavior conforms to the feature mode of normal blockchain operation. The node verification feature set calculated and the signature rhythm pattern are used together to verify the credibility of the node in the entire blockchain network.

[0025] The credibility evaluation submodule generates an identity behavior portrait label by intelligently deploying an identity verification rule engine based on the node verification feature set, performing on-chain credibility weighting identification on multi-dimensional behavior features, and generating an identity behavior portrait label; The identity verification rule engine deployed by the smart contract performs weighted calculation on all collected features to build a multi-dimensional credibility model. In the calculation process, the feature values of each transaction are weighted based on the timestamp marker, and each feature is given a corresponding weight according to the rules of the smart contract. Specifically, assuming that the rule gives the frequency of using encryption keys and communication latency a weight of 60%, and gives the device protocol stack and signature rhythm pattern a weight of 40%, the weight values will be calculated in proportion, based on the transaction time distribution and the behavior characteristics of the node device, combined with the timestamp and device fingerprint, to obtain the credibility label of each transaction in real time. Assuming that in a certain verification process, the score of the signature rhythm pattern is 0.8, the frequency of using encryption keys is 0.9, and the communication latency is 0.7, the credibility of the transaction will be weighted based on the score value and the weight, and finally the credibility of the transaction is 0.83, which represents that the transaction conforms to the regular identity verification mode. The final output of the credibility label will generate auditable identity data for each node and transaction, ensuring that the operation behavior in the blockchain network has certain authenticity and reliability, and generating an identity behavior portrait label.

[0026] Please refer to Figure 4 , the contract response judgment module includes: The behavior deviation recognition submodule identifies the operation path of the user in the blockchain financial identity authentication interaction according to the identity behavior portrait label, calculates the deviation trajectory difference between the operation path and the registered behavior path, judges the path consistency deviation interval, and obtains the behavior path deviation feature. First, the user's entire history path data in the blockchain financial identity authentication interaction area is obtained, which is composed of the operation sequence of the user in the identity verification process, such as authentication page access order, input field order, authentication click behavior, etc. Through such a path, a stable behavior portrait of the user can be established, and then the actual operation path of the user in the current authentication process is obtained. The path is converted into a structured path sequence in the same way. By comparing the two paths, the element displacement number, path length difference and path order similarity between the two path sequences are calculated to form a path trajectory difference matrix. The Euclidean distance is used to calculate the sequence offset degree. For example, if the historical path is [1, 2, 3, 4] and the current path is [1, 3, 2, 4], the order of the two is exchanged at steps 2 and 3. By setting the step difference threshold δ = 1, the number of offset points is 2, and the offset ratio is determined by the proportion method. After calculating the offset trajectory, the offset value is compared with the offset tolerance threshold in the behavior portrait. The threshold can be set according to different financial business levels, such as 5% for high-sensitive business, 10% for medium-sensitive business, and 15% for low-sensitive business, to determine whether the current operation path is within the normal behavior range. If the offset value exceeds the tolerance interval, it is recorded as an abnormal offset trajectory, and structured data is formed and stored, for example, the average length of the user's authentication path is 8 steps, and the offset step length after offset detection is 3 steps. The behavior path offset rate is 37.5%, and the behavior path offset feature is obtained.

[0027] The device fingerprint verification submodule extracts the current device fingerprint and access address based on the behavior path offset feature, compares their structure matching with the stored historical identity authentication fingerprint combination on the chain, judges the stability level of the fingerprint, and obtains the device trust recognition factor. The device fingerprint data includes fields such as user terminal device model, browser version, timestamp, screen resolution, font stack order, etc., a device unique fingerprint string is generated by a hash function, the IP address of the access address and the geographical location information parsed therefrom are obtained, the device fingerprint set matching the identity in the historical authentication record on the chain is called, the current fingerprint structure is matched and compared with the historical fingerprint set, the consistency rate of the fields is compared, for example, 6 fields are completely matched out of 8 fingerprint fields, the matching rate is 75%, if the current matching rate is lower than the preset trust lower limit, such as the preset trust matching threshold is 80%, the device is determined as a non-trustworthy source, a secondary judgment is made in combination with the access geographical location, if the current access IP parsing location and the historical login address exist a difference at the provincial level or above, such as the previous one is "Guangzhou, Guangdong Province" and the current one is "Zhengzhou, Henan Province", the record is an address location offset event, the device source is comprehensively judged to be trustworthy or not in combination with the location offset level classification table (such as provincial change is serious offset, municipal level is medium offset), a device trust recognition factor is constructed in combination with the fingerprint matching rate and the geographical change level, for example: if the device fingerprint matching rate is 75% and the address level offset is provincial, the trust recognition factor is determined as "low trust".

[0028] The authentication level adjustment submodule calculates the dynamic authentication complexity according to the device trust recognition factor, matches the corresponding verification scheme according to the contract rule, and generates a contract dynamic verification level set. ; The dynamic authentication complexity is calculated, the corresponding verification scheme is matched according to the contract rule, and the contract dynamic verification level set is generated. Wherein, R represents the dynamic authentication complexity, A represents the current path behavior vector length, H represents the portrait original path vector length, C represents the device recognition consistency, J represents the behavior offset signal strength, E represents the authentication frequency, F represents the current access geographical location code, and G represents the geographical location code recorded on the chain. The dynamic authentication complexity refers to the difference between the user's current behavior path and the historical portrait, the matching trustworthiness of the device fingerprint, the change of the authentication frequency, and the geographical offset information of the access address in the process of the blockchain financial identity authentication, and the potential identity authenticity risk in the identity authentication scene is comprehensively evaluated, so as to quantify a continuous risk measurement value, the higher the value, the greater the deviation between the current authentication behavior and the historical record on the chain, the more untrustworthy the device environment, and the more abnormal the access behavior, a higher level of authentication mechanism needs to be configured to enhance the identity verification accuracy. The dynamic authentication complexity integrates various heterogeneous data sources through a unified normalization processing method, has cross-dimension comparability, and can realize dynamic adjustment of the identity authentication security level according to real-time data collection, is a key indicator to realize intelligent and risk-oriented identity verification process. According to the behavior path offset feature and the device trust identification factor, the normalized numerical participation items in both are called to jointly measure, first define the current path length , image path length , then the path length difference is 1 step, which is set to after normalization processing, wherein the normalized reference value 10 represents the maximum path step range in the identity authentication process, and then the device recognition consistency is obtained from the fingerprint field matching degree, such as the current fingerprint matching field number is 6, the total field number is 8, and the matching degree is calculated as , that is , which is in the [0, 1] standard normalized range; behavior offset intensity represents the number of offset points in the path, for example, from the path sequence [1, 2, 3, 4, 5, 6, 7, 8] to [1, 3, 2, 4, 6, 5, 7, 8], there are 3 offsets, and the total length of the identity path is 8, which is normalized to ; authentication frequency refers to the number of authentication requests per unit time of the user, which is set to 5 times per hour, and the standardized reference is 20 times per hour, so ; the access geographic code is , the historical geographic code is , and the difference between the two is 330000, in order to avoid dimensional disturbance of the geographic code value in the result, the code difference needs to be dimensionally processed, which can be divided by the maximum administrative area code difference (assuming the maximum code difference is 999999) to be normalized to ; Substitute all normalized parameters into the formula: ; By jointly introducing behavior path offset, device trust degree and address offset degree, and through normalization to realize the dimensional unification of each participation item, the comparability between different feature dimensions is ensured, and the final authentication complexity score R reflects the balanced role of each influencing factor. The result shows that the comprehensive risk score under the current identity authentication situation is 0.5840, according to the complexity level division interval set by the system, such as [0.0-0.3] for low risk, [0.3-0.6] for medium risk, and [0.6-1.0] for high risk, the result falls between medium and high risk critical point, and needs to enter the auxiliary verification path to generate a set of contract dynamic verification levels.

[0029] Please refer to Figure 5 , the trust node scheduling module comprises: The node capability scoring submodule calls the contract dynamic verification level set, extracts the average response time, identity verification frequency and consistency score of the nodes in the consensus node pool, analyzes the execution stability and response capability indicators of the nodes, and uses the formula: ; The node execution capability score value is calculated to obtain the blockchain authentication node capability indicator set; wherein x is the node standard response time, y is the node real-time response time, z is the number of node completed verification records, m is the total number of node cumulative tasks, l is the node original consistency score value, q is the consistency reference value set by the contract, and P represents the node execution capability score value; The node execution capability score value refers to in the process of blockchain financial identity authentication, in order to realize the reasonable scheduling of authentication tasks among multiple consensus nodes, according to the comprehensive performance of each node in the dimensions of response time, verification activity and verification result consistency, the value type index is obtained by normalization calculation after unified dimension, which reflects the bearing capacity and stability of a single node to the identity authentication task under the current state, the higher the score value, the more stable the node response in the near future, the more concentrated the verification frequency, and the closer the consistency to the standard, which can be preferentially selected into the authentication task execution sequence, the calculation of this index realizes the reasonable scheduling of distributed load of financial identity authentication task in the blockchain network by fusing multiple heterogeneous performance parameters, and constructs a multi-dimensional evaluation system; The verification level assigned by the current contract is called, and the corresponding node capability requirement parameter group is determined, and then three key indicators of each candidate node, average response time, historical verification frequency and consistency score, are extracted from the consensus node pool of the blockchain network. First, the standard response time of the node is obtained , which is set according to the authentication level, such as 200ms for L3 level authentication task; Then the real-time response time y of the node is detected, which is obtained by taking the arithmetic mean of the node's recent 20 response data, for example, the response time sequence is [205, 215, 198, 220, 210], and the mean value is the real-time response time ms; The response time difference is calculated as ms, which is normalized to the interval [0, 1], and the normalization reference is set to the maximum tolerance response difference of 500ms, and ; Next, the verification frequency z is extracted, which is defined as the number of valid identity authentications completed by the node in the past 1 hour, for example, node A has completed 180 identity authentications, and ; The total number of block processing tasks undertaken by the node in the past 24 hours is recorded , which is reported by the block generator module in real time; Node consistency score Indicates the effective adoption rate of the node submission result in the consensus process, for example, if the node has 94 times of final consensus adoption in nearly 100 times of submission, the score is , preset consistency reference value , indicating the theoretical maximum value, the difference value is , for unified scale, the reference value interval is set to [0.8, 1], that is ; Substitute the above data into the calculation formula: ; The node execution capability score value of node A is 0.1059, which is a dimensionless score index, used for cross-node sorting. The system sets the scheduling threshold of the contract dynamic scheduling to 0.08. If the score is greater than or equal to the value, it is determined that the node is available, and the score will enter the scheduling queue to participate in the sorting; Wherein, x represents the system set node standard response time, unit: millisecond, y represents the real-time average response time of the node in the current task cycle, unit: millisecond, Indicates the response time error and is normalized to express the response performance difference, z represents the number of identity authentication completed by the node per unit time, unit: times, m represents the total number of tasks processed by the node, unit: times, l is the adoption rate of the number of node submission results, unit: [0, 1] proportion value, q is the system set consistency reference value, which is 1, The consistency score difference is normalized to eliminate the influence of dimension deviation, and P is the node comprehensive execution capability score value. By introducing the square root operation of the response error, the recognition ability of the response stability difference is enhanced. By normalizing, the performance indicators in multiple dimensions are unified and fused, which improves the distinguishability and adaptability of the node capability score in the task scheduling process. The results show that the current state of node A meets the scheduling execution standard, and its capability value can be used for subsequent screening, and finally the blockchain authentication node capability index set is obtained.

[0030] The authentication node dispatching sub-module screens the node set with high index score according to the blockchain authentication node capability index set, determines the task priority issuing order in combination with the current timeliness demand of the verification task and the node geographic network structure relationship, and obtains the financial identity authentication node execution sequence; The ability values calculated by each node are read in sequence, and sorted according to the numerical value, and the top ten nodes are retained in the sorting process, and the ability values of the top ten nodes are all higher than the set scheduling threshold, such as the ability value interval of the top ten nodes is between [0.3366, 0.8723], indicating that the response ability and consistency of this part of the node are stable, and the nodes with high ranking are screened from them, and then the verification level requirement of the authentication task to be processed in the current blockchain network is combined to judge the required response time limit of the task, such as the dynamic verification level is L3 level, which requires that the response time is not more than 300ms, the geographical deployment information and network delay data of the sorted nodes are called in sequence, and the nodes with delay less than 200ms and located in the business center node position are preferentially selected, 4 nodes are selected from the top ten nodes as the target execution group, and the authentication execution sequence is generated, for example, the sequence [N8, N3, N1, N7] is generated according to the response speed sorting, that is, the node N8 has the fastest response speed and is preferentially executed, and the node N7 is the second, and finally an ordered scheduling task list is formed, and the financial identity authentication node execution sequence is obtained.

[0031] Please refer to Figure 6 The block behavior writing module includes: The identity authentication extraction submodule extracts identity information and contract verification records based on the financial identity authentication node execution sequence, filters according to identity credibility and contract matching degree, and obtains identity authentication behavior characteristics combined with identity authentication and contract verification data; Extract each item of data containing financial identity information, and extract the corresponding verification record from the contract verification process. For the data, the matching degree of the authentication information and the verification record is analyzed by combining the content in the contract verification record. The matching process is based on the correlation between the credibility of the identity authentication information and the behavior and identity characteristics in the contract verification record. The credibility is determined by comparing the similarity value calculated by the historical authentication data and the authentication model, and the behavior matching degree is obtained by calculating the difference value between the frequency of the behavior and the predicted frequency of the model. Assuming that the identity information of a user is matched with the contract verification record, and the identity information credibility is 0.85, and the contract verification behavior matching degree is 0.90, then the authentication behavior matching degree of the user is 0.88, and through this value, the appropriate behavior characteristic summary can be effectively obtained, which contains the final behavior characteristics, such as the related attributes of the identity and the behavior mode, and finally the identity authentication behavior characteristics are obtained. The characteristics will be further used for encryption and evidence storage operation.

[0032] The issuance policy calling submodule extracts the issuance sequence and encryption policy code according to the identity authentication behavior characteristics, matches the identity authentication behavior characteristics with the issuance policy, identifies the adaptability of the encryption policy code in the issuance sequence, and generates an encryption adaptation policy. According to the signing order and the encryption policy code, the characteristics are processed, and the adaptability of the behavior characteristic summary in the signing order to each field in the encryption policy code is calculated. Each item in the encryption policy code has specific encryption parameters, such as encryption bit length, encryption round, etc. The parameters need to be selected according to the behavior characteristics of the user and the needs of the contract execution. The encryption adaptability calculation is performed by comparing the behavior characteristics and the setting parameters in the encryption policy. A threshold value is set. If the adaptability of the behavior characteristics is greater than a predetermined reference value, it is considered that the strategy has strong adaptability. Assuming that the adaptability standard of the encryption policy is set to 0.85, and the actual encryption adaptability calculation value is 0.88, it is indicated that the strategy is suitable for the behavior characteristics. Through calculation, the encryption adaptation strategy is finally generated, which is the encryption method for the behavior characteristics and provides technical support for subsequent evidence storage operations.

[0033] The evidence storage record generation submodule integrates the contract execution record and the identity authentication behavior characteristics according to the encryption adaptation strategy, analyzes the mapping relationship of the fields in the contract execution record, constructs the blockchain writing structure of the contract execution according to the mapping relationship, and generates the identity authentication on-chain evidence storage record. The contract execution record is compared with the identity authentication behavior characteristics in the field mapping, and it is ensured that all necessary information is correctly stored in the blockchain. The mapping relationship of the fields is dynamically adjusted according to the encryption policy and the identity authentication characteristics. The coincidence degree of each field is judged, that is, the matching degree between the fields in the contract execution record and the identity authentication characteristics. According to the matching degree, it is determined which fields need to be strengthened for encryption processing. After the contract execution record is encrypted by the encryption adaptation strategy, a set of evidence storage structure template for writing the blockchain is established. The template will include identity authentication information, encryption policy and detailed record of contract execution. Through the process, all authentication and contract data can be structured and stored in the blockchain to generate the identity authentication on-chain evidence storage record.

[0034] The above is only a preferred embodiment of the present application, and does not limit the form of the present application. Any skilled person in the art can modify or change the above disclosed technical content to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present application still belongs to the protection scope of the present application.

Claims

1. A blockchain financial identity authentication system, characterized in that, The system comprises: The behavior feature acquisition module acquires input behavior data in the financial identity authentication registration page, collects user keystroke rhythm, sliding track and touch pressure, extracts rhythm sequence and track offset, compares track length and time length fluctuation, screens high-frequency behavior feature combination, and generates a blockchain identity input behavior feature map; The identity behavior portrait module identifies operation rhythm and device habit based on the blockchain identity input behavior feature map, combines terminal model, typing frequency and track flow direction, extracts input method and terminal pairing features, and generates an identity behavior portrait label; The contract response judgment module extracts user behavior data according to the identity behavior portrait label, analyzes the offset degree of the current input path and the original behavior label, combines device fingerprints and access addresses, judges whether the authentication mechanism level needs to be adjusted according to the contract conditions, and generates a contract dynamic verification level set; The trust node scheduling module calls the contract dynamic verification level set, retrieves response time, verification frequency and consistency score in the consensus node pool, evaluates node execution capability ranking, screens the top-ranked node group for verification task distribution, and obtains a financial identity authentication node execution sequence. 2.The blockchain financial identity authentication system of claim 1, wherein, The blockchain identity input behavior feature map includes keystroke rhythm difference factor, track angle offset indicator, input stability measure, behavior pattern correlation degree and input fluctuation frequency. The blockchain identity behavior portrait label includes terminal adaptation feature, operation rhythm label, input method preference, behavior inertia parameter and device use feature. The contract dynamic verification level set includes path deviation magnitude, fingerprint consistency level, address trust weight, authentication dynamic identifier and contract response label. The financial identity authentication node execution sequence includes response delay ranking, verification task number, node execution weight, dispatching scheduling parameter and consensus priority label. 3.The blockchain financial identity authentication system of claim 1, wherein, The behavior feature acquisition module comprises: The keystroke rhythm extraction submodule acquires input behavior data in the financial identity authentication registration page, extracts keystroke intervals and arranges them in order, analyzes interval fluctuation amplitude, screens key keystroke time periods of fluctuation amplitude, and generates a keystroke rhythm fluctuation feature map; The track parameter identification submodule extracts user input sliding track data based on the keystroke rhythm fluctuation feature map, identifies path length and offset angle, analyzes track length and input time length fluctuation, and screens input behavior segments of offset amplitude to obtain a track length fluctuation interval; The behavior feature construction submodule calls the track length fluctuation interval, combines touch pressure data, analyzes keystroke and sliding behavior in differentiated time periods, screens associated behavior feature combination, and generates a blockchain identity input behavior feature map. 4.The blockchain financial identity authentication system of claim 3, wherein, The identity behavior portrait module comprises: The transaction signature analysis submodule extracts keystroke frequency and track coordinates in the blockchain transaction signing process based on the blockchain identity input behavior feature map, calculates transaction behavior entropy value, and performs pattern matching with a signature benchmark library of a blockchain account record to generate a transaction signature rhythm pattern; The node verification feature submodule calls the transaction signature rhythm mode, analyzes the device protocol stack features and the use frequency of encryption keys in the blockchain node interaction log, and obtains a node verification feature set; The credibility evaluation submodule performs on-chain credibility weighted identification on multi-dimensional behavior features based on the node verification feature set through an identity verification rule engine deployed by a smart contract, and generates an identity behavior portrait label. 5.The blockchain financial identity authentication system of claim 4, wherein, The contract response judgment module includes: The behavior deviation identification submodule identifies the operation path of the user in the blockchain financial identity authentication interaction according to the identity behavior portrait label, calculates the deviation trajectory difference with the registered behavior path, judges the path consistency deviation interval, and obtains the behavior path deviation feature; The device fingerprint verification submodule extracts the current device fingerprint and access address based on the behavior path deviation feature, compares the structure matching of the fingerprint combination with the historical identity authentication stored on the chain, judges the fingerprint stability level, and obtains the device credibility identification factor; The authentication level adjustment submodule calculates the dynamic authentication complexity according to the device credibility identification factor, matches the corresponding verification scheme according to the contract rules, and generates a contract dynamic verification level set. 6.The blockchain financial identity authentication system of claim 5, wherein, The trust node scheduling module includes: The node capability scoring submodule extracts the average response time, identity authentication frequency and consistency score of the nodes in the consensus node pool based on the contract dynamic verification level set, analyzes the execution stability and response capability indicators of the nodes, calculates the node execution capability score value, and obtains a blockchain authentication node capability indicator set; The authentication node dispatching submodule filters the node set with high indicator scores based on the blockchain authentication node capability indicator set, determines the task priority delivery order in combination with the current timeliness demand of the verification task and the node geographic network structure relationship, and obtains a financial identity authentication node execution sequence.

7. The blockchain financial identity authentication system of claim 1, wherein, The system further includes a block behavior writing module: The block behavior writing module extracts the identity authentication result and contract verification process record based on the financial identity authentication node execution sequence, calls the issuance order and encryption strategy code, integrates the behavior feature abstract and contract execution record to write into the ledger, and obtains an identity authentication on-chain record; The identity authentication on-chain record includes an authentication result abstract, an execution process label, a feature hash code, an issuance encryption identifier, and a ledger index entry. 8.The blockchain financial identity authentication system of claim 7, wherein, The block behavior writing module includes: The identity authentication extraction submodule extracts identity information and contract verification records based on the financial identity authentication node execution sequence, filters according to identity credibility and contract matching degree, and combines identity authentication and contract verification data to obtain identity authentication behavior features; The issuance strategy calling submodule extracts the issuance order and encryption strategy code according to the identity authentication behavior features, matches the identity authentication behavior features with the issuance strategy, identifies the adaptability of the encryption strategy code in the issuance order, and generates an encryption adaptation strategy; The record generation submodule integrates the contract execution record and the identity authentication behavior features according to the encryption adaptation strategy, analyzes the mapping relationship of the fields in the contract execution record, constructs the blockchain writing structure of the contract execution according to the mapping relationship, and generates the identity authentication on-chain record.

Images