Management method of financial service platform based on Internet

Abstract

The invention relates to the technical field of platform management, and discloses an internet-based financial service platform management method, which comprises the following steps: collecting static identity data, dynamic behavior data, biological characteristics and environmental parameters of a user in real time, and generating a multi-dimensional dynamic identity chain after desensitization aggregation; the attack-defense engine simulates an attack strategy by using a diffusion model, analyzes causal contradictions and topology aggregation coefficients in combination with a space-time diagram convolutional network, marks high-risk nodes, and iteratively optimizes model parameters everyday through reinforcement learning; quantum signature and alliance chain verification are adopted in the transaction link, transactions involving high-risk countries or transactions with the fund abnormal transfer probability exceeding 95% are automatically frozen, and compliance channels are switched; the biological monitoring module analyzes the physiological signal in real time and triggers a multi-mode challenge; and aggregating edge node data through a federated learning framework, and automatically rolling back to a historical optimal version when the false alarm rate of the updated global risk control model exceeds the standard. According to the invention, efficient management of the financial service platform can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of platform management, and in particular to a management method for an Internet-based financial service platform. Background Art

[0002] Currently, Internet financial service platforms mainly rely on traditional identity authentication and static rule engines for risk control, suffering from problems such as single data dimension and insufficient real-time performance. Existing systems are difficult to effectively integrate cross-platform behavior data, biometric features, and environmental parameters, resulting in limited accuracy and timeliness of dynamic identity recognition. In particular, they lack the ability to accurately identify new fraud means such as decentralized small transfers, and privacy protection mostly uses traditional encryption technologies, unable to cope with the security challenges in the era of quantum computing.

[0003] There are also obvious shortcomings in the existing technology regarding risk model iteration and cross-border transaction compliance verification. Most platforms adopt isolated risk control models, lacking an adaptive update mechanism and unable to quickly respond to the evolution of attack strategies. At the same time, the real-time interception of high-risk transactions and the efficiency of switching compliance channels are low, making it difficult to meet the dynamic needs of global financial operations while ensuring security.

[0004] As can be seen from the above, how to achieve the efficient management of financial service platforms remains to be solved. Summary of the Invention

[0005] In order to achieve the efficient management of financial service platforms, the present application provides a management method and system for an Internet-based financial service platform.

[0006] In a first aspect, the present application provides a management method for an Internet-based financial service platform, adopting the following technical solution:

[0007] A management method for an Internet-based financial service platform includes:

[0008] Real-time collecting static identity data and dynamic behavior data of users across platforms, where the static identity data includes the hash value of the ID number and device fingerprints, and the dynamic behavior data includes bank transfer timestamp sequences, e-commerce shopping frequencies, and social media sentiment keywords. Synchronously obtaining real-time biometric signals and environmental parameters of users, where the biometric signals include the power density of the gamma band of brain waves and the time-domain characteristics of fingertip photoplethysmogram, and the environmental parameters include the GPS positioning error radius and the Wi-Fi signal strength fluctuation value. Desensitizing and aggregating multi-source heterogeneous data through a zero-knowledge proof protocol, aligning behavior time series according to 50-millisecond time slices based on spatio-temporal grid technology, and generating a multi-dimensional dynamic identity chain integrating identity, behavior, and biometric features. The multi-source heterogeneous data includes static identity data, dynamic behavior data, real-time biometric signals, and environmental parameters;

[0009] Build an attack - defense engine. The attack engine generates virtual user portraits based on diffusion models and simulates decentralized small - value transfer attack strategies. The defense engine uses spatio - temporal graph convolutional networks to analyze the causal temporal contradictions and topological clustering coefficients of dynamic identity chains. When it is detected that the time when the first user recommends the second user to register is later than the time of the second user's first transaction, or the number of registered users under the same IP exceeds 5 and the transaction correlation degree is greater than 70%, it is marked as a high - risk node. Dynamically adjust the parameters of the attack and defense models through reinforcement learning, with the number of daily iterations exceeding 100 times, generating real - time risk scores and incremental parameter packages for the defense model;

[0010] Perform quantum - resistant signatures on transaction data based on quantum - secure protocols, generate one - time session keys, call consortium chain nodes, and verify high - risk lists and SWIFT codes. When a transaction involves a country with a high risk control level or the probability of abnormal fund transfer predicted by quantum Monte Carlo simulation exceeds 95%, automatically freeze the funds and generate quantum - signed transaction instructions, while dynamically routing to an alternative compliance channel;

[0011] Real - time monitor users' biological signals. When it is detected that the power of the gamma band of the brain wave drops abnormally or the rising slope of the pulse wave increases steeply, switch to a brain - wave single - modality biometric token and trigger a silent alarm. If the same token is used 3 times, force the user to pass a multi - modality challenge test, which includes random math problem voice verification and electroencephalogram signal problem - solving synchronization detection, generate a dynamic biometric token, and record security event logs;

[0012] Aggregate the anonymized behavior data of edge nodes, use differential privacy to protect local gradients, and update the global risk control model in the federated learning framework through a secure multi - party computing protocol. The global risk control model is jointly constructed by a spatio - temporal graph convolutional network and a lightweight graph attention network; when the false - alarm rate of the new version of the global risk control model rises by more than 5%, automatically roll back to the historical optimal version, which is used to construct a model evolution tree and support transaction traceability analysis, and is used to generate an optimized global risk control model and a version - difference comparison report.

[0013] Optionally, for the biometric dynamic fusing mechanism, the method further includes:

[0014] When a user registers, encode the brain - wave feature vector into a quantum state, perform a Bell state measurement on the entangled pair generated by the quantum random number generator, and generate a quantum biometric label Q bio =Hash(|ψ>), where t is the timestamp, |ψ> is the quantum state generated by quantum encoding of the user's brain - wave feature vector, |ψ>=α|0<+β|1>, where α and β are complex numbers and satisfy |α| 2 +|β| 2 =1, is the tensor product operator, which represents binding the user's quantum state with quantum random numbers. QRNG(t) generates random numbers based on quantum physical processes (such as the quantum random walk of photons), and its quantum state is uniquely determined at timestamp t. Hash is a quantum-resistant hash function that maps the combination of quantum states to a biometric tag of a fixed length;

[0015] Real-time monitor the decoherence time of the quantum state. If the decoherence time is less than 50 microseconds, it is determined that a quantum attack has occurred and the iris backup authentication is initiated;

[0016] When the bionic pulse attack feature appears in the detected pulse wave signal, trigger the self-destruction protocol to erase the edge device key and broadcast the fusing event to the blockchain.

[0017] Optionally, for the permission management of the identity graph, the method further includes:

[0018] Real-time monitor the change in the entropy value of the user behavior chain. When it is detected that the entropy value drops by more than 40% due to high-frequency sensitive operations, automatically trigger the three-level permission downgrading policy, and the three-level permission downgrading policy includes closing the large-amount transfer interface and restricting social finance functions;

[0019] Synchronously analyze the return rate data of the user on the associated e-commerce platform. If the return rate exceeds 30%, compress the credit loan limit to 50% of the original limit;

[0020] When the user passes the multi-modal liveness verification and the behavior entropy value returns to the baseline level, the gradient restores the original permission configuration and generates a permission change log.

[0021] Optionally, during the execution of the risk decision, the method further includes:

[0022] Deploy a lightweight model at the edge node to process low-risk transactions, and control the response delay within 80 milliseconds;

[0023] High-risk transactions are routed to the cloud quantum decision cluster in real time, and a 128Qubit processor is called to perform Monte Carlo simulation and generate quantum-resistant signature instructions;

[0024] When the attack traffic of the regional network surges by more than 100,000 QPS, automatically enable the cross-regional load migration and core business downgrading guarantee mechanism to ensure that the availability of the transfer function is not less than 99.99%;

[0025] Optionally, for the attack-defense engine, the method further includes:

[0026] Based on GAN, generate a virtual normal user group that conforms to the power-law distribution and fraud roles with early morning high-frequency operation characteristics, and inject them into the real-time transaction flow at a ratio of 10% for targeted stress testing;

[0027] When the false negative rate of the defense model for new fraudster roles exceeds 2%, incremental training is automatically triggered and an adversarial feature analysis report is generated, synchronously optimizing the graphic complexity of topological gesture verification and the arithmetic difficulty level of voice challenges.

[0028] Optionally, the dynamic identity chain construction further includes:

[0029] Forcing the implantation of behavior verification anchors when the user initiates a critical operation, requiring continuous drawing of specific topological gesture graphics and answering random voice arithmetic questions, and interrupting the transaction link when the gesture trajectory deviation exceeds 15% or the voice response delay exceeds 3 seconds;

[0030] Synchronously comparing the temporal distribution characteristics of the user's historical behavior chain, and initiating a manual review process and generating a risk behavior comparison report if the KL divergence of the current behavior chain exceeds 1.5.

[0031] In a second aspect, the present application provides a management method for an Internet-based financial service platform, adopting the following technical solutions:

[0032] An Internet-based financial service platform management system, including:

[0033] A multi-dimensional dynamic identity chain generation module that real-time collects the user's cross-platform static identity data and dynamic behavior data. The static identity data includes the ID number hash value and device fingerprint, and the dynamic behavior data includes the bank transfer timestamp sequence, e-commerce shopping frequency, and social media sentiment keywords. Synchronously obtain the user's real-time biometric signals and environmental parameters. The biometric signals include the gamma band power density of brain waves and the time domain characteristics of fingertip photoplethysmogram. The environmental parameters include the GPS positioning error radius and the Wi-Fi signal strength fluctuation value. Desensitize and aggregate multi-source heterogeneous data through the zero-knowledge proof protocol, and align the behavior time series based on the spatio-temporal grid technology according to 50-millisecond time slices, for generating a multi-dimensional dynamic identity chain integrating identity, behavior, and biometrics. The multi-source heterogeneous data includes static identity data, dynamic behavior data, real-time biometric signals, and environmental parameters;

[0034] An engine construction module for constructing an attack-defense engine. The attack engine generates a virtual user profile based on the diffusion model and simulates a decentralized small-amount transfer attack strategy. The defense engine uses a spatio-temporal graph convolutional network to analyze the causal temporal contradictions and topological clustering coefficients of the dynamic identity chain. When it is detected that the time when the first user recommends the second user to register is later than the time of the second user's first transaction, or the number of registered users under the same IP exceeds 5 and the transaction correlation degree is greater than 70%, it is marked as a high-risk node, and the parameters of the attack and defense models are dynamically adjusted through reinforcement learning, with the daily iteration times exceeding 100 times, generating a real-time risk score and an incremental parameter package of the defense model;

[0035] Quantum signature transaction instruction generation module, which performs quantum-resistant signature on transaction data based on quantum security protocols, generates a one-time session key, invokes the consortium blockchain nodes, and verifies the high-risk list and SWIFT codes. When the transaction involves a country with a high risk control level or the probability of abnormal fund transfer predicted by quantum Monte Carlo simulation exceeds 95%, the funds are automatically frozen and used to generate quantum signature transaction instructions, and at the same time, it is dynamically routed to an alternative compliance channel;

[0036] Bio-signal brain monitoring module, which is used to monitor the user's bio-signals in real time. When a significant decrease in the power of the gamma band of the brain waves or a sharp increase in the rising slope of the pulse wave is detected, it switches to a single-modal brain wave biometric token and triggers a silent alarm. If the same token is used 3 times, the user is forced to pass a multi-modal challenge test, which includes voice verification of random math problems and detection of the synchronization of solving problems with brain electrical signals, generates a dynamic biometric token, and records the security event log;

[0037] Model evolutionary tree construction module, which aggregates the desensitized behavior data of edge nodes, uses differential privacy to protect the local gradient, and updates the global risk control model in the federated learning framework through a secure multi-party computing protocol. The global risk control model is jointly constructed by a spatio-temporal graph convolutional network and a lightweight graph attention network; when the false alarm rate of the new version of the global risk control model rises by more than 5%, it automatically rolls back to the historical optimal version, is used to construct the model evolutionary tree and support transaction traceability analysis, and is used to generate an optimized global risk control model and a version difference comparison report.

[0038] Thirdly, the present application provides a management system for an Internet-based financial service platform, adopting the following technical solutions:

[0039] A management method for an Internet-based financial service platform, including a processor, and a program of the management method for the Internet-based financial service platform described in any one of the above is running in the processor.

[0040] Fourthly, the present application provides a storage medium, adopting the following technical solutions:

[0041] A storage medium stores a program of the management method for the Internet-based financial service platform described in any one of the above.

[0042] In summary, the present application includes at least one of the following beneficial technical effects:

[0043] Through real-time fusion and intelligent analysis of multi-source data, the risk identification efficiency and decision-making accuracy of the financial service platform are significantly improved; the system integrates cross-platform identity, behavior, biometric, and environmental data to construct a dynamic identity portrait, realizing millisecond-level response to complex risks such as decentralized attacks and abnormal transactions, and solving the problems of lag and misjudgment caused by traditional risk control relying on single-dimensional data.

[0044] By constructing an attack - defense confrontation engine and an adaptive model, the system has dynamic defense capabilities. Using reinforcement learning to simulate attack strategies and optimize defense parameters, combined with quantum - secure protocols and federated learning frameworks, it not only ensures the quantum - resistant security of data privacy and transaction signatures but also realizes the continuous evolution of the risk control model, effectively coping with the challenges brought by new fraud means and algorithm iterations.

[0045] At the system operation level, an edge - cloud collaborative architecture and an elastic resource scheduling mechanism are adopted. Low - risk transactions are quickly processed by lightweight models, while high - risk transactions are deeply analyzed by a quantum computing cluster. At the same time, high service availability is ensured through load migration and core function degradation, ensuring the efficient and stable operation of financial services even in extreme attack or high - concurrency scenarios. Brief Description of the Drawings

[0046] Figure 1 is a flowchart of a management method for an Internet - based financial service platform shown according to an exemplary embodiment.

[0047] Figure 2 is a block diagram of a management system for an Internet - based financial service platform shown according to an exemplary embodiment. Detailed Embodiments

[0048] The following details the embodiments of the present application, and the examples of the embodiments are shown in the drawings.

[0049] In the description of this specification, the description with reference to terms such as "certain embodiments", "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0050] The embodiments of the present application disclose a management method for an Internet - based financial service platform, referring to Figure 1 , including:

[0051] S100, real - time collect the static identity data and dynamic behavior data of users across platforms, synchronously obtain the real - time biometric signals and environmental parameters of users, desensitize and aggregate multi - source heterogeneous data through a zero - knowledge proof protocol, and align the behavior time series based on the spatio - temporal grid technology at 50 - millisecond time slices to generate a multi - dimensional dynamic identity chain integrating identity, behavior, and biometrics.

[0052] Among them, the static identity data includes the ID number hash value and the device fingerprint. The ID number hash value refers to encrypting the user's ID number through a hash algorithm such as SHA-256 to generate a unique and irreversible identifier to ensure privacy and security. The device fingerprint is to collect the characteristics of the device's hardware (such as IMEI, MAC address) and software (operating system version, browser type) to generate a unique device identifier for detecting device sharing or counterfeiting behavior.

[0053] The dynamic behavior data includes the bank transfer timestamp sequence, the e-commerce shopping frequency, and the social media sentiment keywords. Specifically: The bank transfer timestamp sequence: Records the specific time of each user transfer (accurate to milliseconds) to analyze the transaction frequency and time pattern (such as high-frequency transfers in the early morning may be abnormal behavior). The e-commerce shopping frequency: Counts the daily / weekly shopping times and amount distribution of users on the e-commerce platform to identify sudden changes in consumption patterns (such as sudden high-frequency small transactions). The social media sentiment keywords: Extracts the emotional tendency in the text published by users on the social platform through natural language processing (NLP) (such as an increase in the frequency of negative emotion words may be associated with fraud behavior).

[0054] The biometric signals include the power density of the gamma band of brain waves and the time-domain characteristics of the fingertip photoplethysmogram. Specifically, the power density of the gamma band of brain waves: Collects electroencephalogram (EEG) signals through a wearable device and extracts the power density of the gamma band (30 - 100 Hz) to reflect the user's cognitive load and attention concentration (abnormal fluctuations may indicate human manipulation). The time-domain characteristics of the fingertip photoplethysmogram: Uses photoplethysmography (PPG) technology to monitor the change in blood flow at the fingertip and analyzes parameters such as the rising slope and peak interval of the pulse wave to identify abnormal physiological reactions (such as a state of tension or coercion).

[0055] The environmental parameters include the GPS positioning error radius and the Wi-Fi signal strength fluctuation value. Specifically, the GPS positioning error radius: Calculates the error range of the user's location through the GPS module (such as an error > 100 meters may indicate signal interference or forged positioning). The Wi-Fi signal strength fluctuation value: Monitors the change in the RSSI (Received Signal Strength Indicator) of the Wi-Fi signal, and abnormal fluctuations may indicate an abnormal network environment or a man-in-the-middle attack.

[0056] Regarding data desensitization and aggregation, specifically including:

[0057] Zero-Knowledge Proof (ZKP) Protocol: Using zk-SNARKs or zk-STARKs technology, encrypt and prove the original data, verifying the authenticity of the data without revealing specific information (e.g., proving that an ID number is valid without exposing the number itself); Multi-source Heterogeneous Data Alignment: Align the time-series data from different data sources (such as bank APIs, social platform APIs, sensors) according to timestamps, eliminating time-series deviations caused by network latency or differences in collection frequencies.

[0058] For spatio-temporal gridification and dynamic identity chain construction, it specifically includes: Spatio-temporal Grid Division: Divide space into geographical grids (such as 100m×100m cells) and time into 50ms time slices to form spatio-temporal units; Data Fusion: Aggregate multi-source data within the same spatio-temporal unit (such as a user's transfer behavior within a specific time slice, GPS error in the grid where they are located, current brain wave state) into a "dynamic identity segment"; Chain Structure: Link the dynamic identity segments in chronological order to form a continuous dynamic identity chain, recording the temporal evolution of user behavior (such as the path from registration to the first transaction).

[0059] By integrating static, behavioral, biological, and environmental data, construct a comprehensive user profile. For example, through the correlation between brain wave state and transfer behavior, identify abnormal scenarios such as "coerced transfers"; The 50ms time slice ensures the precise capture of high-frequency behaviors (such as flash sales transactions), avoiding misjudgments caused by data latency.

[0060] S200, construct an attack-defense engine. The attack engine generates virtual user profiles based on diffusion models and simulates decentralized small-amount transfer attack strategies. The defense engine uses spatio-temporal graph convolutional networks to analyze the causal temporal contradictions and topological clustering coefficients of the dynamic identity chain. When it detects that the time when the first user recommends the second user to register is later than the time of the second user's first transaction, or the number of registered users under the same IP exceeds 5 and the transaction correlation degree is greater than 70%, mark it as a high-risk node. Dynamically adjust the parameters of the attack and defense models through reinforcement learning, with the number of daily iterations exceeding 100 times, generating real-time risk scores and incremental parameter packages for the defense model.

[0061] Among them, for the attack engine simulation, it specifically includes three steps: generating virtual user profiles using diffusion models, generating a mixed user group using GAN, and maintaining the attack strategy library. Specifically:

[0062] The first step, use a diffusion model (such as DDPM) to reversely generate virtual user behavior sequences that conform to fraud patterns (for example, simulate "wool-shearing" users registering on multiple platforms and quickly withdrawing cash); Train attack strategies through a generative adversarial network (GAN) to generate transaction sequences for decentralized small-amount transfer attacks (such as multi-account collaborative small-amount transfers to avoid single-transaction threshold monitoring).

[0063] In the second step, virtual users conforming to the real user behavior characteristics are generated based on the power-law distribution (such as Zipf distribution) to simulate normal trading patterns (such as high-frequency small-value shopping during the day); and fraudulent users with high-frequency operation characteristics in the early morning are generated (such as high-frequency transfers and abnormal cross-platform operations at 2-4 am), and are injected into the real-time transaction flow at a ratio of 10% to form a mixed test environment.

[0064] In the third step, the attack strategy library is continuously updated, including known fraud patterns (such as social engineering-induced registration, virtual currency arbitrage) and new attack hypotheses (such as AI-generated false transactions); the newly added "high-frequency operation in the early morning" fraud role is classified as a high-priority attack scenario and is regularly updated to the strategy library.

[0065] Among them, for the defense engine, it specifically includes three steps: spatio-temporal graph convolutional network (ST-GCN) analysis, topological clustering coefficient calculation, and real-time risk scoring. Specifically:

[0066] In the first step, spatio-temporal graph convolutional network (ST-GCN) analysis: The dynamic identity chain is transformed into a spatio-temporal graph structure (nodes are user behavior events, and edges are time / space associations), and node features are extracted through graph convolution; causal contradictions are detected (such as the registration time of user B recommended by user A is later than B's first transaction time, indicating that B may be a forged account).

[0067] In the second step, topological clustering coefficient calculation: Analyze the user group under the same IP or device fingerprint. If the number of registered users > 5 and the transaction correlation degree (such as the overlap of fund flows) > 70%, it is marked as an "aggregation node", which may be a fraud gang registered in batches.

[0068] In the third step, real-time risk scoring: Combine behavior contradiction degree, clustering coefficient, historical risk records, etc., and output a risk score of 0-100 points.

[0069] For reinforcement learning optimization and incremental training, specifically:

[0070] First is the attack and defense confrontation training. The attack engine attempts to break through the defense model, and the defense engine counteracts the attack by adjusting graph convolution parameters, risk thresholds, etc. Then, a targeted stress test is conducted, and 10% of GAN-generated fraud roles (such as high-frequency transfers in the early morning) are injected into the real-time transaction flow to simulate real attack scenarios.

[0071] If the false negative rate of the defense model for new fraud roles (such as high-frequency operations in the early morning) exceeds 2% (that is, the proportion of undetected fraud transactions > 2%), the following actions are automatically triggered: a. Incremental training: Only update the parameters related to this fraud pattern in the defense model (such as the weight of topological clustering coefficient calculation, the threshold of time series anomaly detection); b. Adversarial feature analysis report: Generate a report and mark the features for which the defense model fails (such as the failure to recognize the spatio-temporal correlation of high-frequency transfers in the early morning).

[0072] For the dynamic optimization of the verification mechanism: If the false negative rate of the defense model is relatively high, increase the graphic complexity of the gesture verification (e.g., upgrade from a simple straight-line gesture to a polygon trajectory). Also, increase the difficulty of the arithmetic problems in the voice verification (e.g., upgrade from "3 + 5" to "17 × (8 - 3)"), and require the user to complete the synchronization detection of the electroencephalogram signal problem-solving during the voice answer (e.g., the power of the theta band of the electroencephalogram needs to match the problem-solving steps).

[0073] Finally, send the incremental (instead of full) parameters of the optimized defense model every day to the edge nodes to reduce bandwidth consumption.

[0074] Correspondingly, by using GAN to generate a mixed user group (normal users and high-frequency fraudsters in the early morning) that conforms to the power-law distribution, the system can simulate real attack scenarios, expose the blind spots of the defense model, and optimize it specifically. The incremental training mechanism triggered by the false negative rate ensures that local vulnerabilities can be quickly repaired when the defense fails, combines adversarial feature analysis to accurately locate model defects, and avoids the high cost of full model updates. The dynamic upgrade of multimodal verification (such as gesture complexity and voice challenge difficulty) balances security and user experience according to the attack intensity, not only improving the ability to resist automated attacks but also reducing interference to normal users.

[0075] In addition, the integration of spatio-temporal graph analysis and new features (such as "high-frequency operations in the early morning") enables the system to not only accurately identify traditional fraud (such as batch registration) but also actively defend against complex attacks generated by AI (such as coordinated transfers). The combination of the dynamic verification mechanism and the adaptive defense strategy not only ensures the real-time response ability to new attacks but also maintains the high availability of the system through local parameter optimization and feature enhancement, ultimately achieving the dynamic balance between security intensity and user experience.

[0076] S300, perform quantum-resistant signatures on transaction data based on the quantum security protocol, generate a one-time session key, and call the consortium chain nodes to verify the high-risk list and SWIFT code in parallel. When the transaction involves a country with a high risk control level or the probability of abnormal fund transfer predicted by quantum Monte Carlo simulation exceeds 95%, automatically freeze the funds, generate a quantum signature transaction instruction, and simultaneously dynamically route it to the backup compliance channel.

[0077] For the quantum security protocol, first, protocol selection. Adopt quantum-resistant cryptographic algorithms (such as CRYSTALS-Kyber or CRYSTALS-Dilithium based on lattice cryptography) to replace the traditional RSA / ECC algorithms to ensure security under quantum computing attacks. The user device and the server generate a key pair through a quantum security key generation algorithm (such as the NIST standard algorithm). Both parties generate a one-time session key through quantum key distribution (QKD) or a hash function to ensure that the key is only used for a single transaction.

[0078] Among them, lattice cryptography algorithms can still ensure the security of keys under quantum computer attacks, preventing traditional encryption algorithms from being cracked by Shor's algorithm; and the one-time key mechanism avoids the risk of key reuse. Even if the key for a single transaction is leaked, it does not affect the security of other transactions.

[0079] For quantum-resistant signature generation, first is the transaction data signing. The user uses the private key to perform a quantum-resistant signature (such as CRYSTALS-Dilithium signature) on the transaction data (such as amount, payee and payer, timestamp). The signature contains the digest of a quantum-secure hash (such as SHA-3) to ensure data integrity. Then is the signature verification preparation. The signature, together with the public key and transaction data, is submitted to the consortium blockchain nodes for subsequent parallel verification.

[0080] The signature binds the transaction data to the user's identity. Even if the data is tampered with, the signature verification will fail; and the quantum-resistant signature ensures that even if quantum computers become popular in the future, historical transaction records are still not forgeable.

[0081] Furthermore, for the parallel verification of consortium blockchain nodes, first is the node distribution and verification. The transaction data, signature, and user identity chain fragments (such as behavioral characteristics in the dynamic identity chain) are distributed to the consortium blockchain nodes; and each node independently verifies:

[0082] Signature validity: Use the user's public key to verify whether the quantum-resistant signature matches the transaction data.

[0083] Identity consistency: Compare whether there are contradictions between the real-time behavioral characteristics (such as device fingerprint, geographical location) in the user identity chain and the transaction information.

[0084] Then is the consensus mechanism. The nodes reach a consensus through PBFT (Practical Byzantine Fault Tolerance) or similar algorithms to confirm the legitimacy of the transaction. Parallel verification by multiple nodes reduces the risk of single-point failures and improves the system reliability. And the consortium blockchain nodes can quickly verify whether the transaction complies with anti-money laundering (AML) and counter-terrorism financing (CTF) rules.

[0085] Secondly, for high-risk country detection and quantum Monte Carlo simulation, first is the determination of high-risk countries. Conduct a real-time query on the country / region involved in the transaction. If it belongs to a high-risk control level country (such as a sanctioned country or a region with a high fraud rate), trigger additional verification. Then is the quantum Monte Carlo simulation. Input the transaction data (such as amount, time, associated accounts) into the quantum computing cluster and run the Monte Carlo simulation:

[0086] Fund flow prediction: Simulate the transfer path of funds in the next 10 time steps and calculate the abnormal probability (such as the funds being quickly dispersed to multiple high-risk accounts).

[0087] Abnormal threshold determination: If the predicted abnormal probability > 95%, mark it as a high-risk transaction.

[0088] Through cross-border risk interception, suspicious transactions involving high-risk countries can be directly blocked, meeting international compliance requirements (such as the OFAC sanctions list); quantum Monte Carlo simulation utilizes quantum parallelism to accelerate calculations and can quickly identify abnormal patterns of fund transfers.

[0089] Furthermore, for automatic fund freezing and generating quantum signature instructions, specifically, when the freezing instruction is triggered, when a transaction is marked as high-risk (such as involving a high-risk country or an abnormal probability > 95%), the system automatically sends a freezing instruction to the bank / payment gateway, and the freezing instruction contains a quantum signature to ensure the credibility of the instruction source. Then, for generating the quantum signature instruction, the collective signature (multi-signature mechanism) of the consortium chain nodes is used to generate the final freezing instruction, ensuring that the operation is irreversible and traceable.

[0090] Finally, corresponding dynamic routing to the alternative compliance channel is carried out, specifically including:

[0091] Compliance channel selection: According to the transaction risk level and national regulatory requirements, an alternative payment channel (such as a SWIFT alternative channel, local compliance gateway) is dynamically selected; the channel selection needs to be verified by a quantum signature to ensure the credibility of the path.

[0092] Routing execution: The transaction data is resubmitted through the alternative channel, bypassing restricted paths (such as the SWIFT codes of sanctioned countries).

[0093] Through quantum security protocols (such as quantum-resistant signatures and one-time session keys), it is ensured that transaction data cannot be forged or tampered with in the era of quantum computing, protecting user privacy and asset security; combining quantum Monte Carlo simulation with high-risk country detection, automatic interception of abnormal fund transfers in milliseconds is achieved, and dynamic routing to the compliance channel is carried out to balance risk control and business continuity. Its consortium chain node parallel verification mechanism and quantum signature instructions build a distributed trust system, ensuring transparent and non-repudiable operations and significantly enhancing the system credibility.

[0094] While ensuring transaction security, S300 takes into account compliance, real-time performance, and reliability: By freezing high-risk transactions in real time (such as involving sanctioned countries or abnormal fund flows), asset losses are avoided; dynamic routing and alternative channels are used to ensure uninterrupted services; distributed verification and quantum-resistant technologies meet the strict control requirements for high-risk transactions in global financial scenarios, providing an efficient and auditable solution for cross-border payments, anti-money laundering, and other scenarios.

[0095] S400 monitors the user's biological signals in real time. When it detects an abnormal decrease in the power of the gamma band of the brain wave or a sharp increase in the rising slope of the pulse wave, it switches to a single-modal brain wave bio-token and triggers a silent alarm. If the same token is used three times, the user is forced to pass a multimodal challenge test, which includes voice verification of random math problems and synchronization detection of brain wave signal problem-solving, generates a dynamic bio-token and records a security event log.

[0096] The first is biosignal monitoring: real-time collection of the user's brainwave gamma band power density (reflecting cognitive state) and fingertip pulse wave rising slope (reflecting physiological stress). Trigger conditions: If an abnormal decrease in brainwave gamma power (such as a deepfake attack) or a sharp increase in pulse wave (such as a state of duress) is detected, the system immediately switches to single-modal brainwave biometric token authentication and triggers a silent alarm (silent background logging and alerting).

[0097] Then there are token usage restrictions and single-modal token restrictions for multimodal verification: after the same biometric token is used three times consecutively, the user is forced to pass a multimodal challenge test.

[0098] Dynamic token generation: After passing multimodal verification, a new temporary biometric token (with a short validity period and cannot be reused) is generated, and a security event log (including time, exception type, and user operation trajectory) is recorded.

[0099] By monitoring biosignal anomalies and implementing a dynamic authentication circuit-breaker mechanism, the system rapidly switches authentication modes and enforces multi-dimensional verification when a user faces potential duress or forgery attacks. This prevents identity theft while also minimizing the impact of frequent circuit-breaker triggering on the user experience. Its silent alarm and logging capabilities support post-incident traceability, while multimodal testing, combining physiological and behavioral characteristics, significantly improves authentication reliability, ultimately achieving a dynamic balance between real-time risk mitigation and security strength.

[0100] S500 aggregates desensitized behavioral data from edge nodes, uses differential privacy to protect local gradients, and updates the global risk control model in the federated learning framework through a secure multi-party computing protocol. The global risk control model is jointly constructed by a spatiotemporal graph convolutional network and a lightweight graph attention network. When the false alarm rate of the new version of the global risk control model rises by more than 5%, it automatically rolls back to the historical optimal version to build a model evolution tree and support transaction traceability analysis, and to generate an optimized global risk control model and version difference comparison report.

[0101] Among them, in the first step, edge data aggregation and privacy protection: Edge nodes protect gradient information by adding noise through differential privacy technology (such as adding noise) to local desensitized behavior data (such as user transaction frequency, device characteristics), and then encrypt and transmit it to the federated learning framework through the **Secure Multi-Party Computation (SMC)** protocol to ensure that data privacy is not leaked. Prevent sensitive information from being stolen during the data sharing process and meet privacy regulation requirements (such as GDPR).

[0102] In the second step, global risk control model training: Use the Spatio-Temporal Graph Convolutional Network (ST-GCN) to capture the temporal and spatial correlations of user behaviors (such as cross-platform operation patterns), combined with the lightweight Graph Attention Network (GAT) **to focus on key risk nodes (such as high-aggregation IP addresses), and the model is iteratively updated daily to optimize the risk identification ability using distributed data.

[0103] In the third step, model stability guarantee: If the false positive rate of the new version model rises by more than 5% (such as the proportion of misintercepting normal transactions is too high), the system automatically rolls back to the historical optimal version and records the model evolution path (model evolution tree). Thus, it can avoid performance degradation caused by data noise or model overfitting, and at the same time support tracing the root cause of problems through version difference reports (such as vulnerabilities introduced in a certain update).

[0104] Through federated learning and privacy protection technologies, continuously optimize the risk control model on the premise of ensuring data security, and improve the ability to identify new types of fraud; its automatic rollback and evolution tree mechanism ensure model stability and prevent the business from being affected by the increase in the misjudgment rate. Finally, the dynamic model update and traceability analysis capabilities balance the accuracy of risk prevention and control and the reliability of the system, providing interpretable and iterative risk control support for financial transactions.

[0105] In the embodiment of this application, the method further includes:

[0106] In the first step, when a user registers, encode the electroencephalogram feature vector into a quantum state, perform a Bell state measurement with the entangled pair generated by the quantum random number generator, and generate a quantum biological tag Q bio = Hash(|ψ>), t is the timestamp, |ψ> is the quantum state generated by quantum encoding of the user's electroencephalogram feature vector, |ψ> = α|0> + β|1>, where α, β are complex numbers and satisfy |α| 2 + |β| 2 = 1, is the tensor product operator, indicating binding the user's quantum state with the quantum random number. QRNG(t) is a random number generated based on a quantum physical process (such as the quantum random walk of photons), and its quantum state is uniquely determined at the timestamp t. Hash is a quantum-resistant hash function that maps the quantum state combination to a fixed-length biological tag.

[0107] Among them, when the user registers, the system converts the user's electroencephalogram feature vector into a quantum state (through quantum encoding), and performs a Bell state measurement on the entangled pairs generated by the quantum random number generator (QRNG). This process binds the user's quantum state with the random number to form a combined state. Finally, the combined quantum state is mapped to a quantum biometric tag of a fixed length through an anti-quantum hash function. Thus, a unique and secure user identity identifier can be generated. The combination of the quantum state, random number, and hash technology ensures that the tag cannot be forged or replicated, enhancing the anti-quantum attack ability of identity authentication.

[0108] In the second step, the quantum state decoherence time is monitored in real time. If the decoherence time is less than 50 microseconds, it is determined that a quantum attack has occurred and the iris backup authentication is initiated.

[0109] Among them, the system monitors the quantum state decoherence time of the user in real time (the duration for which the quantum state remains stable). If it is detected that the decoherence time is below 50 microseconds (which may be caused by external interference or attacks), the iris backup authentication mechanism is immediately initiated. Thus, potential quantum attacks (such as environmental interference or malicious tampering) can be identified in a timely manner, and the system security can be ensured by switching to iris authentication, avoiding the risk of identity theft due to the invalidation of the quantum state.

[0110] In the third step, when the bionic pulse attack feature appears in the detected pulse wave signal, the self-destruction protocol is triggered to erase the edge device key and broadcast the fuse event to the blockchain.

[0111] Among them, the system continuously analyzes the user's pulse wave signal. If the bionic pulse attack feature (such as an abnormal signal pattern) is detected, the self-destruction protocol is immediately triggered: erase the key information on the edge device and broadcast the fuse event to the blockchain. It can prevent advanced bionic attacks (such as forging biological signals). By completely clearing the key through the self-destruction mechanism and combining the blockchain to record the attack event, the theft or abuse of sensitive data can be prevented.

[0112] In the embodiment of this application, for the permission management of the identity graph, the method further includes:

[0113] In the first step, the entropy value change of the user behavior chain is monitored in real time. When it is detected that the entropy value drops by more than 40% due to high-frequency sensitive operations, the three-level permission downgrading policy is automatically triggered. The three-level permission downgrading policy includes closing the large-amount transfer interface and restricting social finance functions.

[0114] Among them, 1. Behavior entropy value calculation and baseline modeling:

[0115] Behavior feature extraction: The system continuously collects the user behavior data (such as transfer frequency, logged-in device, transaction amount, geographical location change, etc.) and converts it into an analyzable numerical sequence;

[0116] Information Entropy Analysis: Calculate the "baseline entropy value" of user behavior through information entropy (which measures data randomness) to reflect the randomness characteristics of normal operations. For example, if a user usually makes small transfers during the day and has a low login frequency at night, the system will establish a corresponding baseline model;

[0117] Dynamic Baseline Update: The baseline will be automatically adjusted according to the user's historical behavior (for example, when a new user has frequent operations in the initial stage, the baseline allows a higher entropy value fluctuation).

[0118] 2. Real-time Monitoring and Anomaly Detection:

[0119] High-frequency Sensitive Operation Detection: The system counts user behavior through a time window (such as 5 minutes). If an abnormal pattern is found (such as high-frequency transfers, logins from different locations, operations at abnormal times within a short period), trigger a recalculation of the entropy value;

[0120] Threshold Trigger Condition: When the entropy value calculated in real-time drops by more than 40% compared to the baseline (for example, the baseline is 80% randomness and the current is only 48%), it indicates that the behavior pattern is highly regularized (possibly controlled by an automated attack or the account has been stolen), and the system determines it as a high risk.

[0121] 3. Three-level Privilege Downgrade Mechanism:

[0122] Large Transfer Limit: Dynamically adjust the transaction limit through the API gateway, switch the user's transfer privilege from "unlimited" to "single transaction ≤ preset small threshold", and block all large requests.

[0123] Freeze of Social Finance Functions: Mark as "high-risk status" in the user portrait system, synchronize it to the risk control engine, and suspend their participation in high-risk financial activities such as P2P lending and stock margin trading.

[0124] Log Record: Detailed record of the triggering event (such as operation type, time, entropy value change curve), and mark it as an "abnormal behavior downgrade event" for subsequent auditing.

[0125] Quantify the anomaly degree of the behavior pattern through the change of the entropy value, effectively distinguish normal users from hijacked accounts (such as automated attacks usually lead to highly regularized behaviors); it can also avoid a "one-size-fits-all" ban, retain the small transaction function to reduce the impact on user perception, and at the same time contain potential losses by restricting high-risk functions; in addition, the dynamic baseline reduces misjudgments, for example, it will not trigger mis-downgrades when new users have frequent operations in the initial stage.

[0126] In the second step, synchronously analyze the return rate data of the user on the associated e-commerce platform. If the return rate exceeds 30%, compress the credit loan limit to 50% of the original limit.

[0127] 1. Cross-platform Data Integration:

[0128] Data Synchronization: The system obtains the user's order data for the past 30 days from the e-commerce platform through the API or the data middle platform, and extracts the return rate (number of returned orders / total number of orders).

[0129] Filtering of Abnormal Return Patterns: Exclude normal after-sales behaviors (such as returns due to product quality issues), and focus on identifying suspicious patterns (such as concentrated returns of specific products, returns within 1 hour after receipt).

[0130] 2. Dynamic Adjustment of Credit Limit:

[0131] Threshold Trigger Conditions: If the return rate exceeds 30% for three consecutive periods (such as 30 days), the system determines that the user has high risks (such as fraudulent returns or cash flow problems).

[0132] Quota Compression Mechanism: Compress the user's credit loan limit to 50% of the original limit, which takes effect immediately by updating the user profile and risk control rules.

[0133] User Notification: Send a notification to inform the user of the reason for the quota adjustment and the restoration path (for example, it can be restored only if the return rate ≤ 10% for 30 consecutive days).

[0134] By incorporating data from non-financial scenarios (e-commerce returns) into the risk control model, potential user risks (such as fraudulent arbitrage or irrational consumption) are revealed; by compressing the credit limit, the bad debt risk of the platform is reduced, while 50% of the limit is retained to avoid over-punishing users.

[0135] In the third step, when the user passes the multi-modal liveness verification and the behavior entropy value returns to the baseline level, the gradient restores the original permission configuration and generates a permission change log.

[0136] 1. Multi-modal Biometric Verification:

[0137] Biometric Feature Fusion: Require the user to pass multi-modal verifications such as fingerprint, face, and voice to ensure the authenticity of the identity. For example: Fingerprint verification: Compare fingerprint feature points through a trusted execution environment (TEE); 3D face recognition: Detect the facial depth information to prevent photo or video forgery; Voice challenge: Randomly generate a math problem (such as "calculate 17×4"), require the user to answer verbally and synchronously monitor the brain waves (such as whether the cognitive activities during problem-solving match).

[0138] Cognitive Consistency Detection: Combine biometric signals with behavioral logic (such as the synchrony between verbal answers and brain waves) to prevent bionic attacks (such as AI-synthesized voices).

[0139] 2. Determination of the Restoration of Behavioral Entropy Value:

[0140] Baseline regression analysis: Continuously monitor the entropy value of user behavior. If it recovers to more than 90% of the baseline (e.g., baseline 80% → current 72%) for 5 consecutive monitoring cycles (e.g., 1 hour), the behavior is considered to have returned to normal.

[0141] 3. Gradient permission recovery:

[0142] Phase 1 (within 24 hours): Open small-value transfers (such as ≤5,000 yuan) and restrict high-risk functions; Phase 2 (within 48 hours): Gradually resume social financial activities (such as allowing viewing of loan products but prohibiting submission of applications); Phase 3 (after 72 hours): Fully resume large-value transfers and high-risk functions.

[0143] Logging: Detailed records of the recovery process, including verification results, entropy changes, and permission change timelines. By combining biometrics with cognitive challenges, the system ensures user authenticity and prevents attackers from circumventing permission restrictions. By restoring permissions in stages, the system gradually restores the user experience while ensuring security. The complete log supports post-audit efforts. For example, if a user subsequently triggers a downgrade, the root cause (e.g., a verification vulnerability) can be quickly identified.

[0144] In the embodiment of the present application, during the risk decision execution process, the method further includes:

[0145] The first step is to deploy a lightweight model on edge nodes to process low-risk transactions, with response latency controlled within 80 milliseconds.

[0146] A lightweight risk assessment model (such as a simplified machine learning model) is deployed on user devices or near-user edge computing nodes to analyze transaction risk levels in real time. If a transaction is determined to be low-risk (such as a small transfer or regular payment), the model directly verifies and approves it, ensuring end-to-end response latency of less than 80 milliseconds.

[0147] In the second step, high-risk transactions are routed to the cloud-based quantum decision cluster in real time, calling the 128Qubit processor to perform Monte Carlo simulation and generate quantum-resistant signature instructions.

[0148] High-risk transactions (such as large transfers and remote logins) are routed in real time to a cloud-based quantum decision cluster. The cluster's 128-qubit processor uses Monte Carlo simulations (random sampling calculations) to predict transaction risk probabilities, such as abnormal fund transfer paths or the possibility of account hijacking. If verified, a quantum-resistant signature instruction (based on quantum cryptography algorithms such as CRYSTALS-Dilithium) is generated, ensuring that the transaction cannot be tampered with and is resistant to quantum computing attacks.

[0149] When the regional network attack traffic surges to more than 100,000 QPS, the cross-regional load migration and core business degradation guarantee mechanism will be automatically enabled to ensure that the transfer function availability is not less than 99.99%.

[0150] When it is detected that the single-region network attack traffic exceeds 100,000 QPS (queries per second), the system will automatically trigger:

[0151] 1. Cross-region load migration: Migrate the traffic of non-core services (such as report generation, user notification) to other un-attacked regional nodes to disperse the pressure.

[0152] 2. Core service degradation: Temporarily close non-essential functions (such as social finance recommendations, advertisement push), and retain the minimum resource requirements for the core transfer function.

[0153] 3. Availability guarantee: Through dynamic resource allocation (such as increasing the computing power quota of the cloud quantum cluster), ensure that the availability of the transfer function is maintained above 99.99%.

[0154] Through the edge-cloud collaborative architecture, quantum computing enhanced decision-making, and elastic load management technologies, a full-link risk prevention and control and high-availability system in the financial scenario is constructed: Deploy lightweight models at the edge nodes to quickly process low-risk transactions with a latency of less than 80 milliseconds, taking into account both efficiency and user experience; High-risk transactions are routed to the cloud quantum decision-making cluster in real time, and complex risk analysis is carried out through Monte Carlo simulation of a 128-qubit processor, and an anti-quantum signature is generated to ensure the immutability of transactions; When encountering large-scale network attacks (such as a traffic surge exceeding 100,000 QPS), the system automatically triggers cross-region load migration and core service degradation mechanisms, and through dynamic resource allocation and suspension of non-essential functions, ensure that the availability of core functions such as transfers is not less than 99.99%. This design realizes low latency, high throughput, and business continuity in extreme scenarios while ensuring transaction security and anti-quantum attack capabilities, meeting the stringent requirements of financial-level risk control and service reliability.

[0155] In the embodiment of this application, further constructing the dynamic identity chain includes:

[0156] The first step is to forcibly implant a behavior verification anchor point when the user initiates a critical operation, requiring the user to continuously draw a specific topological gesture pattern and answer a random voice arithmetic question. When the deviation of the gesture trajectory exceeds 15% or the voice answer delay exceeds 3 seconds, the transaction link is interrupted.

[0157] Among them, when the user initiates a critical operation (such as large-amount transfer, modification of account sensitive settings), the system forcibly requires the user to complete dual verification:

[0158] 1. Topological gesture verification: The user needs to continuously draw a preset complex pattern (such as a spiral, polygon combination) on the screen, and the system real-time tracks the coordinates, speed, and acceleration of the gesture trajectory.

[0159] 2. Voice Arithmetic Challenge: Randomly generate math problems (such as "Calculate 9×(15 - 7)"), and the user needs to answer by voice within 3 seconds. The system synchronously detects the voice delay and the accuracy of the answer. If the deviation of the gesture trajectory from the preset graph exceeds 15% (such as excessive path deviation or abnormal speed), or the voice answer delay exceeds 3 seconds, the system immediately interrupts the transaction link and rolls back the operation.

[0160] In the second step, synchronously compare the temporal distribution characteristics of the user's historical behavior chain. If the KL divergence of the current behavior chain exceeds 1.5, start the manual review process and generate a risk behavior comparison report.

[0161] The system synchronously compares the temporal distribution characteristics of the current behavior chain with the user's historical behavior:

[0162] 1. Feature Extraction: Extract the temporal patterns of the user's behavior from historical data (such as operation frequency, time interval, device switching pattern).

[0163] 2. KL Divergence Calculation: Quantify the deviation degree of the current behavior from the historical pattern through the KL divergence in information theory (measuring the difference between two probability distributions).

[0164] 3. Threshold Trigger: If the KL divergence value of the current behavior exceeds 1.5 (indicating that the behavior pattern deviates abnormally from the historical normality), the system automatically triggers the manual review process and generates a risk report including historical behavior comparison and current operation details.

[0165] Through the dynamic comparative analysis of multi-factor real-time verification (such as gesture trajectory and voice arithmetic challenge) and historical behavior patterns (detecting anomalies based on KL divergence), construct a dynamic identity chain to strengthen the security of key operations. It can block automated attacks or unauthorized operations through the implantation of behavior anchors, and trigger manual review for abnormal behaviors to reduce the risk of misjudgment. At the same time, combined with the mechanism of quickly interrupting high-risk transactions and post-event manual auditing, while ensuring the security of transactions and the ability to resist quantum attacks, balance the user experience and system reliability, and form a full-link risk control system covering "real-time blocking - risk assessment - manual intervention", ultimately achieving the dual goals of financial-level security protection and business continuity.

[0166] The embodiment of the present application discloses a management system of an Internet-based financial service platform, referring to Figure 2 , including;

[0167] Multi-dimensional Dynamic Identity Chain Generation Module 001, which collects the static identity data and dynamic behavior data of users across platforms in real time. The static identity data includes the hash value of the ID number and the device fingerprint, and the dynamic behavior data includes the bank transfer timestamp sequence, the e-commerce shopping frequency, and the social media sentiment keywords. At the same time, it synchronously obtains the real-time biometric signals and environmental parameters of the users. The biometric signals include the power density of the gamma band of the electroencephalogram and the time-domain characteristics of the fingertip photoplethysmogram, and the environmental parameters include the GPS positioning error radius and the Wi-Fi signal strength fluctuation value. It desensitizes and aggregates the multi-source heterogeneous data through the zero-knowledge proof protocol, and aligns the behavior time series according to 50-millisecond time slices based on the spatio-temporal grid technology, which is used to generate a multi-dimensional dynamic identity chain integrating identity, behavior, and biometrics. The multi-source heterogeneous data includes static identity data, dynamic behavior data, real-time biometric signals, and environmental parameters;

[0168] Engine Construction Module 002, which is used to construct an attack-defense engine. The attack engine generates a virtual user profile based on the diffusion model and simulates a decentralized small-amount transfer attack strategy. The defense engine uses a spatio-temporal graph convolutional network to analyze the causal time series contradiction and topological clustering coefficient of the dynamic identity chain. When it detects that the time when the first user recommends the second user to register is later than the time of the second user's first transaction, or the number of registered users under the same IP exceeds 5 and the transaction correlation degree is greater than 70%, it is marked as a high-risk node, and dynamically adjusts the parameters of the attack and defense models through reinforcement learning. The daily iteration times exceed 100 times, and generates a real-time risk score and an incremental parameter package of the defense model;

[0169] Quantum Signature Transaction Instruction Generation Module 003, which performs quantum-resistant signature on the transaction data based on the quantum security protocol, generates a one-time session key and calls the consortium chain node to verify the high-risk list and SWIFT code in parallel. When the transaction involves a country with a high risk control level or the probability of abnormal fund transfer predicted by the quantum Monte Carlo simulation exceeds 95%, it automatically freezes the funds and is used to generate quantum signature transaction instructions, and at the same time dynamically routes to the standby compliance channel;

[0170] Bio-signal Brain Monitoring Module 004, which is used to monitor the user's bio-signals in real time. When it detects that the power of the gamma band of the electroencephalogram drops abnormally or the rising slope of the pulse wave increases steeply, it switches to the electroencephalogram single-modal biometric token and triggers a silent alarm. If the same token is used 3 times, it forces the user to pass a multi-modal challenge test. The test includes random math problem voice verification and electroencephalogram signal problem-solving synchronization detection, generates a dynamic biometric token and records the security event log;

[0171] The model evolution tree construction module 005 aggregates the desensitized behavior data of edge nodes, protects the local gradient using differential privacy, and updates the global risk control model in the federated learning framework through a secure multi-party computing protocol. The global risk control model is jointly constructed by a spatio-temporal graph convolutional network and a lightweight graph attention network. When the false alarm rate of the new version of the global risk control model rises by more than 5%, it automatically rolls back to the historical optimal version, which is used to construct the model evolution tree and support transaction traceability analysis, and to generate an optimized global risk control model and a version difference comparison report.

[0172] The embodiment of the present application also discloses a management system for an Internet-based financial service platform, including a processor, and a program of the management method for the Internet-based financial service platform described in any one of the above is run in the processor.

[0173] The embodiment of the present application also discloses a storage medium storing a program of the management method for the Internet-based financial service platform described in any one of the above.

[0174] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A management method for an Internet-based financial service platform, characterized in that, Including: Real-time collect the static identity data and dynamic behavior data of users across platforms. The static identity data includes the hash value of the ID number and the device fingerprint. The dynamic behavior data includes the bank transfer timestamp sequence, the e-commerce shopping frequency, and the social media sentiment keywords. Synchronously obtain the real-time biometric signals and environmental parameters of users. The biometric signals include the power density of the gamma band of electroencephalogram and the time-domain characteristics of the fingertip photoplethysmogram. The environmental parameters include the GPS positioning error radius and the Wi-Fi signal strength fluctuation value. Desensitize and aggregate the multi-source heterogeneous data through the zero-knowledge proof protocol, align the behavior time series according to 50-millisecond time slices based on the spatio-temporal grid technology, and generate a multi-dimensional dynamic identity chain integrating identity, behavior, and biometrics. The multi-source heterogeneous data includes static identity data, dynamic behavior data, real-time biometric signals, and environmental parameters; Construct an attack-defense engine. The attack engine generates a virtual user portrait based on the diffusion model and simulates the decentralized small-amount transfer attack strategy. The defense engine uses the spatio-temporal graph convolutional network to analyze the causal time-series contradiction and topological clustering coefficient of the dynamic identity chain. When it is detected that the time when the first user recommends the second user to register is later than the time of the second user's first transaction, or the number of registered users under the same IP exceeds 5 and the transaction correlation degree is greater than 70%, it is marked as a high-risk node. Dynamically adjust the parameters of the attack and defense models through reinforcement learning, with the number of daily iterations exceeding 100 times, and generate real-time risk scores and incremental parameter packages for the defense model; Perform quantum-resistant signature on the transaction data based on the quantum security protocol, generate a one-time session key, call the consortium chain node, and verify the high-risk list and SWIFT code. When the transaction involves a country with a high risk control level or the probability of abnormal fund transfer predicted by the quantum Monte Carlo simulation exceeds 95%, automatically freeze the funds and generate a quantum signature transaction instruction, and at the same time dynamically route it to the standby compliance channel; Real-time monitor the user's biological signals. When it is detected that the power of the gamma band of the electroencephalogram drops abnormally or the rising slope of the pulse wave increases steeply, switch to the electroencephalogram single-modal biometric token and trigger a silent alarm. If the same token is used 3 times, force the user to pass the multi-modal challenge test. The test includes random math problem voice verification and electroencephalogram signal problem-solving synchronization detection, generate a dynamic biometric token, and record the security event log; Aggregate the desensitized behavior data of the edge nodes, use differential privacy to protect the local gradient, and update the global risk control model in the federated learning framework through the secure multi-party computation protocol. The global risk control model is jointly constructed by the spatio-temporal graph convolutional network and the lightweight graph attention network; when the false alarm rate of the new version of the global risk control model rises by more than 5%, automatically roll back to the historical optimal version, which is used to construct a model evolution tree and support transaction traceability analysis, and is used to generate an optimized global risk control model and a version difference comparison report.

2. The management method of the Internet-based financial service platform according to claim 1, characterized in that The method further includes: When a user registers, the electroencephalogram feature vector is encoded into a quantum state, and a Bell state measurement is performed with the entangled pairs generated by the quantum random number generator to generate a quantum biological tag t is the timestamp, |ψ> is the quantum state generated by quantum encoding of the user's electroencephalogram feature vector, |ψ> = α|0> + β|1>, where α and β are complex numbers and satisfy |α| 2 +|β| 2 = 1, is the tensor product operator, indicating binding the user's quantum state with the quantum random number. QRNG(t) is a random number generated based on a quantum physical process (such as the quantum random walk of photons), and its quantum state is uniquely determined at the timestamp t. Hash is a quantum-resistant hash function that maps the combination of quantum states to a biological tag of a fixed length; Real-time monitor the quantum state decoherence time. If the decoherence time is less than 50 microseconds, it is determined that a quantum attack has been suffered and iris standby authentication is started; When the bionic pulse attack feature appears in the pulse wave signal, trigger the self-destruction protocol to erase the edge device key and broadcast the fusing event to the blockchain.

3. The management method of the Internet-based financial service platform according to claim 2, characterized in that, Perform permission management for the identity graph. The method further includes: Real-time monitor the change in the entropy value of the user behavior chain. When it is detected that the entropy value drops by more than 40% due to high-frequency sensitive operations, automatically trigger a three-level permission downgrading policy, which includes closing the large-amount transfer interface and restricting social finance functions; Synchronously analyze the return rate data of the user on the associated e-commerce platform. If the return rate exceeds 30%, compress the credit loan limit to 50% of the original limit; When the user passes the multi-modal liveness verification and the behavior entropy value returns to the baseline level, gradually restore the original permission configuration and generate a permission change log.

4. The management method of the Internet-based financial service platform according to claim 3, characterized in that, During the execution of the risk decision-making process, the method further includes: Deploy a lightweight model at the edge node to process low-risk transactions, and control the response delay within 80 milliseconds; Route high-risk transactions to the cloud quantum decision-making cluster in real time, call a 128Qubit processor to perform Monte Carlo simulation and generate anti-quantum signature instructions; When the attack traffic of the regional network surges by more than 100,000 QPS, automatically enable the cross-regional load migration and core business degradation protection mechanism to ensure that the availability of the transfer function is not less than 99.99%; 5. The management method of the Internet-based financial service platform according to claim 4, characterized in that Attack-Defense Engine. The method further includes: Based on GAN, generate a virtual normal user group that conforms to the power-law distribution and a fraud role with early morning high-frequency operation characteristics, and inject them into the real-time transaction flow at a ratio of 10% for targeted stress testing; When the false negative rate of the defense model for new fraud roles exceeds 2%, automatically trigger incremental training and generate an adversarial feature analysis report, and synchronously optimize the graphic complexity of the topological gesture verification and the arithmetic difficulty level of the voice challenge.

6. The management method of the Internet-based financial service platform according to claim 5, characterized in that The construction of the dynamic identity chain further includes: Forcibly implant a behavior verification anchor point when the user initiates a critical operation, requiring continuous drawing of a specific topological gesture graph and answering random voice arithmetic questions. When the deviation of the gesture trajectory exceeds 15% or the voice response delay exceeds 3 seconds, interrupt the transaction link; Synchronously compare the temporal distribution characteristics of the user's historical behavior chain. If the KL divergence of the current behavior chain exceeds 1.5, start the manual review process and generate a risk behavior comparison report.

7. A management system for an Internet-based financial service platform, characterized in that, Include: A multi-dimensional dynamic identity chain generation module that real-time collects the static identity data and dynamic behavior data of the user across platforms. The static identity data includes the hash value of the ID number and the device fingerprint, and the dynamic behavior data includes the bank transfer timestamp sequence, the e-commerce shopping frequency, and the social media sentiment keywords. Synchronously obtain the user's real-time biometric signals and environmental parameters. The biometric signals include the gamma-band power density of the brain wave and the time-domain characteristics of the fingertip photoplethysmogram. The environmental parameters include the GPS positioning error radius and the Wi-Fi signal strength fluctuation value. Desensitize and aggregate multi-source heterogeneous data through the zero-knowledge proof protocol, and align the behavior time series according to 50-millisecond time slices based on the spatio-temporal grid technology, for generating a multi-dimensional dynamic identity chain that integrates identity, behavior, and biometrics. The multi-source heterogeneous data includes static identity data, dynamic behavior data, real-time biometric signals, and environmental parameters; An engine construction module for constructing an attack-defense engine. The attack engine generates virtual user portraits based on a diffusion model and simulates a decentralized small-amount transfer attack strategy. The defense engine uses a spatio-temporal graph convolutional network to analyze the causal temporal contradictions and topological clustering coefficients of dynamic identity chains. When it is detected that the time when the first user recommends the second user to register is later than the time of the second user's first transaction, or the number of registered users under the same IP exceeds 5 and the transaction correlation degree is greater than 70%, it is marked as a high-risk node. The parameters of the attack and defense models are dynamically adjusted through reinforcement learning, with the number of daily iterations exceeding 100 times, generating real-time risk scores and incremental parameter packages for the defense model; A quantum signature transaction instruction generation module that performs quantum-resistant signatures on transaction data based on a quantum security protocol, generates a one-time session key, calls the consortium chain nodes, and verifies the high-risk list and SWIFT codes. When a transaction involves a country with a high risk control level or the probability of abnormal fund transfer predicted by quantum Monte Carlo simulation exceeds 95%, the funds are automatically frozen and used to generate quantum signature transaction instructions, and at the same time, it is dynamically routed to an alternative compliance channel; A biological signal brain monitoring module for real-time monitoring of user biological signals. When it is detected that the power of the gamma band of the brain wave decreases abnormally or the rising slope of the pulse wave increases steeply, it switches to a brain wave single-modal biological token and triggers a silent alarm. If the same token is used 3 times, the user is forced to pass a multi-modal challenge test, which includes random mathematical problem voice verification and electroencephalogram signal problem-solving synchronization detection, generating a dynamic biological token and recording security event logs; A model evolutionary tree construction module that aggregates the desensitized behavior data of edge nodes, uses differential privacy to protect the local gradient, and updates the global risk control model in the federated learning framework through a secure multi-party computing protocol. The global risk control model is jointly constructed by a spatio-temporal graph convolutional network and a lightweight graph attention network; when the false alarm rate of the new version of the global risk control model rises by more than 5%, it automatically rolls back to the historical optimal version, which is used to construct a model evolutionary tree and support transaction traceability analysis, and is used to generate an optimized global risk control model and a version difference comparison report.

8. A management system for an Internet-based financial service platform, characterized in that, It includes a processor, and a program of the management method of the Internet-based financial service platform as described in any one of claims 1-6 runs in the processor.

9. A storage medium, characterized in that, Stores a program of the management method of the Internet-based financial service platform as described in any one of claims 1-6.

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