16575 results about "Financial transaction" patented technology

A block chain double-chain cold chain traceability management system for meat quality fluctuation belongs to the technical field of traceability management systems and comprises a data acquisition and preprocessing module, a composite index module, a security verification module, a federated learning and AI resource management module, a fragmentation and verification architecture module and an automatic error correction module. According to the method, data chain storage environment data hash and transaction chain record operation logs are innovatively separated through a block chain double-chain architecture, hash fingerprints are synchronized in combination with an anchoring technology, cross-chain consistency is ensured, long-term non-tampering of data is realized through an anti-quantum hash algorithm, resource allocation is optimized through hierarchical storage design, and the method is suitable for large-scale popularization and application. The composite index technology is fused with hypergraph dynamic index to trace production batches, vehicles, warehouses and space-time quadtree dynamic subdivision areas, complex query response time is remarkably compressed, path deviation real-time alarm is achieved, the edge node cache hit rate is increased, and full-link data safety and query efficiency are remarkably improved.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

The invention discloses a risk control credit monitoring method based on cloud computing, and belongs to the technical field of cloud computing, and the risk control credit monitoring method based on cloud computing comprises the following steps: S1, collecting user transaction data and behavior track data in real time; s2, cleaning and standardizing the data; s3, constructing a multi-dimensional risk assessment model based on machine learning; s4, dynamically generating a credit score according to the risk characteristics; s5, triggering an early warning mechanism for abnormal transactions in real time; and S6, generating a visual risk control report and updating a monitoring strategy. According to the method, multi-source heterogeneous data are integrated through federated learning, hierarchical privacy protection is realized in combination with homomorphic encryption and differential privacy, risk assessment real-time performance is improved by using a hybrid cloud resource scheduling and dynamic model updating technology, and a compliance audit closed loop is constructed based on a block chain and interpretability analysis.

An online social wager-based gaming system is disclosed, featuring dynamic cross-provider game filtering and persistent cross-provider voice-interactive group play. The system allows for automated multi-seat group game reservations and incorporates a distributed ledger for bet verification. Features include a Group Connect module for dynamic group formation and live audio across various game providers, enabling real-time play. A Professional Companion Connect module facilitates live webcam or microphone gambling sessions with professional companions, under contractual agreements. The User Games module enhances personalization by integrating user-uploaded images into the gaming environment. A cryptocurrency / blockchain-based bet tracking system ensures transparency and security in managing bet transactions through a controlled digital wallet system. This integrated platform aims to provide a cohesive, interactive, and personalized online casino experience, addressing limitations of traditional solitary online gaming by fostering social connections and enhancing user trust and engagement.

The invention discloses an AI-based automatic financial information processing method, and relates to the field of financial automation, and the method comprises the steps: achieving the automatic collection and storage of structured and unstructured data through the access of enterprise multi-source financial data; systematic preprocessing is carried out on the collected multi-source heterogeneous financial data, and a unified and high-quality financial data set is constructed; based on natural language processing and a knowledge graph technology, performing text semantic understanding, transaction automatic classification, field standardization and label generation on the cleaned and integrated financial data; comprehensively quantifying enterprise operation and financial performance based on the structured transaction data and the semantic annotation result; based on historical financial indexes, establishing a multi-model architecture to predict key financial variables; and based on the structured data, the prediction result and the historical rule, identifying potential financial abnormity and risk behaviors, and realizing intelligent early warning. According to the method, the intelligence, the real-time performance and the accuracy of financial information processing can be remarkably improved.

The invention relates to the technical field of block chains, and discloses a supply chain tracing method and system based on a block chain, and the method comprises the steps: carrying out the collection and feature learning of the product identification information and transaction records of a supply chain node, and obtaining a supply chain transaction feature vector; based on the supply chain transaction feature vector, executing intelligent contract verification strategy confirmation and weighted voting verification on a transaction group to obtain a consensus confirmation block; extracting transaction data from the consensus confirmation block, constructing a double-Hash Merkle tree structure and generating supply chain traceability features; and executing a commodity tracing contract, a quality verification contract and a responsibility judgment contract according to the supply chain tracing characteristics, and generating an execution log and a visual tracing report, thereby reducing the number of verification nodes and network communication overhead while ensuring the block verification reliability, improving the consensus efficiency, and improving the objectivity and efficiency of the tracing process.

The invention relates to the technical field of computer communication, provides a distributed accounting asynchronous data processing method and system based on an MEMO state machine, and is used for improving the intelligent adaptability of a distributed accounting network to a dynamic service scene. The method comprises the following steps: acquiring a transaction request set in a distributed accounting network, extracting an asynchronous processing trigger event of the transaction request set, analyzing the asynchronous processing trigger event, generating a state transition instruction sequence containing M state transition paths associated with K transaction data blocks, a parallel prediction module based on an MEMO state machine performs asynchronous processing on the M state transition paths to generate N prediction state vectors and corresponding conflict detection marks; performing feature fusion on the N prediction state vectors according to the conflict detection mark to generate a global state updating matrix; and writing the global state updating matrix into a consensus node set of the distributed accounting network, and generating an asynchronous processing state log matched with the transaction request set.

The invention relates to the technical field of financial data security, and discloses an intelligent ERP financial system data security management and authentication method. The method comprises the following steps: acquiring an original transaction data stream in an ERP system, extracting key financial fields, and dividing the key financial fields into a sensitive data set and a common data set according to a preset rule; a dynamic encryption strategy framework is constructed based on sensitive data set attributes, the framework comprises multiple levels of encryption strength parameters, and the corresponding encryption strength can be automatically matched according to the authentication level of an access request. And monitoring a system data access behavior in real time, collecting feature data, inputting the feature data into the anomaly detection model, and triggering access blocking when an output anomaly access probability exceeds a threshold value. And generating a periodic integrity verification instruction according to the sensitive data updating frequency, performing integrity verification by using a hash chain technology, recording a result and marking a tampering risk level. And based on the association relationship between the tampering risk level and the abnormal access probability, generating an updated security policy and synchronizing the updated security policy to each data access node.

The invention discloses a digital asset secure transaction system and method based on a block chain, and belongs to the cross field of block chain technology and financial science and technology. The system adopts a layered architecture design and comprises an intelligent contract execution layer, a distributed account book storage layer and a cross-chain interaction layer. The transaction method comprises the following steps: generating a digital identity certificate based on asymmetric encryption, and realizing privacy protection through zero-knowledge proof; a secure transaction channel is constructed by adopting a multi-signature mechanism, and sensitive data processing is performed in combination with a trusted execution environment; and designing a dynamic fragmentation strategy to optimize the transaction throughput, and establishing an on-chain and off-chain collaborative verification mechanism. The innovation point is that a double-layer verification model combining a verifiable delay function and a threshold signature is provided, and millisecond-level confirmation is realized while the transaction irreversibility is ensured. The system supports multi-chain asset atomic exchange, and interoperability of different block chain networks is realized through a heterogeneous cross-chain gateway. The scheme has the characteristics of high transaction confirmation speed, high privacy protection level and high system expansibility.

A method and a system for providing at least one of a process automation and artificial intelligence (PAAI), a market aggregation, or an embedded marketplace relating to transactions are disclosed. In particular, a method and a system for automation of transactions in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for managing transactions in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for automating processing of transactions in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for automated orchestration of a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for augmenting of services in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed.

The invention relates to the technical field of encryption communication, in particular to a data encryption communication method and system applied to an intelligent cash register. The method comprises the following steps: collecting user touch data and equipment hardware fingerprints, and carrying out high-dimensional feature fusion coding to obtain a joint identity certificate; obtaining an original key negotiation protocol and transmission metadata of an opposite-end server, and executing multi-logic path mapping and channel encryption configuration to obtain distributed ciphertext channel parameters; encrypting the distributed ciphertext channel parameters and the corresponding transmission metadata, and then performing block content packaging and node hash calculation to obtain an audit chain hash block; performing homomorphic encryption processing on the obtained real-time transaction data to obtain a to-be-transmitted homomorphic ciphertext; and performing communication transmission anomaly detection on information transmission data acquired in real time, and triggering dynamic key re-negotiation so as to improve the communication feedback capability. According to the invention, the overall security and stability of data encryption communication between the cash register and the back-end server can be improved.

The invention relates to the technical field of artificial intelligence, can be applied to the field of medical health and the field of financial transaction, discloses an action generation method and device based on multi-modal pre-training, a robot and a medium, and is applied to a high-frequency action generation scene of an intelligent surgical robot or an intelligent customer service and wealth management scene. The method comprises the following steps: acquiring a language instruction, a visual image and robot body sensing data; performing multi-modal feature alignment and cross-modal feature fusion on the language instruction, the visual image and the robot body sensing data through a pre-trained visual language model to generate a fused joint feature vector; generating a target continuous control instruction based on the fused joint features through a pre-trained target action model by adopting a flow matching technology; and generating continuous actions of the robot based on the target continuous control instruction. According to the method, the robot action generation efficiency and the cross-platform adaptability are improved.

The invention provides a big data mining method and system applied to supply chain financial businesses, and the method comprises the steps: firstly obtaining real-time heterogeneous data streams of a plurality of participants of a target supply chain network, covering structured orders, unstructured logistics texts and semi-structured settlement document data, carrying out the multi-modal feature fusion, and carrying out the real-time heterogeneous data streams, including the structured orders, the unstructured logistics texts and the semi-structured settlement document data, of a plurality of participants of the target supply chain network; the method comprises the following steps: generating a supply chain feature matrix containing static and dynamic features of entity nodes, constructing a supply chain time sequence diagram network based on the supply chain feature matrix, representing participants by nodes, representing transaction events with timestamps and transaction strength weights by edges, hierarchically aggregating features by using a pre-trained time sequence diagram convolutional network, extracting global transaction modes and local abnormal fluctuation features, and constructing a supply chain network; and finally, generating a supply chain risk conduction topological graph which comprises a risk propagation path and an intensity parameter, and triggering a real-time risk early warning signal so as to realize accurate insight and timely early warning of the supply chain financial risk.

The invention provides a financial data risk analysis method and system based on a large model, and relates to the technical field of intelligent risk control, and the method comprises the steps: obtaining financial data from a participant, carrying out the standardization and aggregation through a federated learning framework, generating a federated feature vector, constructing a bidirectional knowledge distillation model, receiving a vector, and carrying out the risk analysis of the financial data. A teacher model and a student model are deployed, a risk conduction map is constructed, a generative adversarial network is utilized to monitor changes of an enterprise association network, a risk conduction edge weight is adjusted, a financial report time sequence and a transaction event sequence are aligned through a space-time coupling encoder based on an LSTM and Transform architecture, and a multi-modal fusion feature vector is generated and input to a bidirectional knowledge distillation model; and a final risk score is obtained by combining the atlas, a grading early warning mechanism is triggered, and a risk disposal suggestion and a conduction path are generated, so that the data standardization and aggregation efficiency can be improved, and the dynamic coupling capability of a multi-modal fusion mechanism and the adaptability of a risk conduction model can be enhanced.

Techniques for verifying authenticity of a cosmetic product using a distributed ledger include generating a transaction including identification information for a cosmetic product indicating a manufacturer of the cosmetic product. The transaction is augmented with a cryptographic signature from the manufacturer to prove the identity of the manufacturer. Additionally, the transaction is transmitted to at least one other participant in a distributed ledger network of participants maintaining the distributed ledger. A unique identifier is embedded on the cosmetic product or on packaging for the cosmetic product that references the identification information for a cosmetic product in the distributed ledger.

In accordance with embodiments of the present disclosure, a method for conducting secure financial transactions uses a model-based reflex agent to ensure real-time authorization, anomaly detection, and risk assessment. The agent receives transaction-related data, maintains an internal state reflecting observed and inferred conditions, and applies condition-action rules to determine appropriate actions, such as authorizing payments, triggering multi-factor authentication, or halting transactions. The agent dynamically updates its internal state based on feedback, adapts to changes using machine learning, and ensures compliance through audit logs. This approach enhances security, minimizes fraud, and provides seamless, adaptive processing for financial transactions in dynamic environments.

The invention relates to the technical field of data processing, and discloses an intelligent supply chain optimization management method and system based on digital AI analysis. The method comprises the steps of collecting supply chain data to construct a resource library; analyzing a sales mode and K-means + + clustering customer features by using LSTM, and predicting demands; inventory parameters and supplier scores are calculated, and a collaborative data set is formed; commodity association is analyzed through a knowledge graph, and a commodity selection decision is generated; resource allocation is carried out by applying multi-objective optimization, and cross-channel inventory collaboration is realized; and an integral rule and a merchant enabling scheme are constructed based on the transaction data, and a digital asset closed-loop mechanism is formed. According to the intelligent supply chain optimization management method based on AI analysis, global visualization, dynamic scheduling and accurate allocation of inventory resources are realized, the technical problem of cross-channel inventory collaborative allocation is solved, and the operation efficiency and the resource utilization rate of the whole supply chain are remarkably improved.

In embodiments, systems and methods for configuring and deploying artificial intelligence driven transacting agents that are permitted to autonomously execute transactions on behalf of the individual or organization and configuring the transacting agent based on the agent configuration instructions and a set of predefined system prompts. The method further includes granting the transacting agent access to a digital wallet associated with the individual or organization and deploying the transacting agent to a public network, such that the transacting agent executes transactions on behalf of the individual or organization via one or more digital marketplaces using the digital wallet to which the transacting agent was granted access. In some embodiments, the transacting agent is granted access to the digital wallet using a consent token. In some embodiments, the method also includes hyper-personalizing and / or fine-tuning the agent.

An AI-driven financial planning system for real-time market adjustment, consisting of: a neural inference coprocessor configured to execute deep financial forecasting models, including recurrent neural networks and attention-based encoders, on the device, and wherein the processor dynamically updates portfolio parameters in response to market signals exhibiting volatility differences above a statistical threshold calculated using an exponentially weighted moving standard deviation; a financial data acquisition module configured to continuously receive and analyze heterogeneous data streams, including market indices, interest rates, stock and bond price fluctuations, economic indicators, regulatory updates, and financial news sentiment feeds; a behavioral analytics engine configured to create a dynamically evolving user-specific financial behavior profile based on real-time analysis of transaction history, income-expenditure cycles, psychometric test results, and temporal lifestyle patterns using supervised and unsupervised machine learning algorithms; A goal optimization module configured to transform high-level, user-defined financial goals into quantitatively tracked multi-level goals. It uses a reinforcement learning framework that predicts optimal asset allocations across multiple time horizons. a real-time strategy simulation engine configured to perform Monte Carlo simulations and deep Q-learning-based assessments to simulate the resilience of proposed financial strategies under different macroeconomic regimes and trigger redistribution events based on predefined confidence thresholds; a compliance-aware execution interface configured to interact with financial institutions through encrypted API channels, ensuring policy enforcement using a smart contract validator and a hardware-enabled secure transaction signing unit; and a recommendation display unit configured to render dynamic dashboards for visualizing investments, reallocation warnings, confidence intervals, and sensitivity sliders, and where user interaction with the unit flows back into the behavioral model for real-time learning.

The invention relates to the field of financial science and technology, and discloses a financial risk dynamic assessment system and method based on artificial intelligence. The system comprises a multi-source heterogeneous data acquisition module which acquires transaction data, public opinion texts and association maps in real time; the adaptive feature engineering module dynamically screens key risk factors; the dynamic risk map construction module calculates a risk conduction coefficient through a map neural network; the multi-modal AI analysis engine cooperatively runs a time sequence prediction model, a text mining model and a graph calculation model; a risk conduction simulator quantifies a systematic risk path. The problems of data splitting processing, model static solidification and correlation risk quantification deficiency in the prior art are solved, the false alarm rate is reduced to 12%, the response speed reaches 90 seconds, the prediction deviation is reduced to 22%, and an interpretable supervision report is generated.

The invention discloses a supply chain financial risk management system based on credit assessment, and relates to the field of financial science and technology. A five-dimensional data model is constructed through a dynamic data modeling unit, and transaction subject levels and legal potency dimensions are integrated; the causal analysis engine adopts a dynamic updating algorithm of time sequence feature coding and compliance score constraint, and introduces a legal potency score constraint mechanism to strengthen compliance verification. The cross-domain risk conduction model combines physical space voxel grid monitoring with a digital space fluid dynamic equation, and the risk conduction velocity is quantified through a risk diffusion coefficient and a partial differential equation; and the self-evolution defense module integrates a confrontation sample generator and an incorruption degree feedback mechanism, simulates logistics track abnormity through Levy distribution, and improves the defense capability of the system in combination with a dynamic weight adjustment strategy of approval skipping times. According to the system, the deep supply chain risk conduction path identification precision is improved, and the problems of a multi-level network risk monitoring blind area and cross-domain risk tracking lag are effectively solved.

The invention relates to the technical field of accounting informatization, and discloses an accounting document cross-platform synchronization system based on a block chain and a conflict processing method, and the system automatically judges a multi-mechanism concurrent operation conflict based on a preset medical authority level and a millisecond timestamp. And when a plurality of branch hospitals modify the same patient voucher at the same time, preferentially adopting a high-authority mechanism to operate, if the authority is at the same level, selecting the operation with the latest timestamp, and if logic conflicts exist, pushing to a general hospital for manual auditing. And an operation log is recorded in real time by adopting Apache Kafka, and a non-tampering historical version is generated through a block chain technology. All modification records are stored in a Merkle tree structure, a complete operation track at any time point can be inquired through a patient ID, the complete operation track comprises modification content, operator identity and block chain transaction hash, a national medical insurance platform is connected in real time, and cost compliance is automatically verified; the system can solve the problems of data conflicts, version chaos and auditing tracing among heterogeneous ERP (Enterprise Resource Planning) systems.

The invention provides an investment decision-making system and method based on federated learning and multi-modal risk control, and the system comprises a data collection layer which is used for integrating satellite remote sensing data, supply chain Internet of Things data and financial market data; the federal processing layer is used for constructing a distributed training framework based on a FedAvg algorithm and realizing cross-mechanism data fusion and global model parameter updating through homomorphic encryption and differential privacy; the intelligent decision-making layer adopts a TD3 reinforcement learning engine and adopts a dynamic gating mechanism to adjust fusion weights of texts, time sequences and image modalities in real time; and the execution monitoring layer is used for detecting transaction execution deviation in real time through a digital twinning technology, realizing abnormal response by adopting a CUSUM algorithm, and dynamically adjusting a federal learning rate through Bayesian optimization. The key technical defects of data islands, tail risk response delay, insufficient cross-market linkage analysis and the like of a traditional investment system are overcome through an innovatively designed federated learning architecture and a time-varying risk control model.

The invention discloses a power transaction market risk dynamic assessment system, which comprises a multi-modal sensing module used for collecting physical equipment operation data, market transaction data and environmental parameters in real time, and performing space-time alignment processing through edge computing nodes to generate a fusion data stream; the self-adaptive evaluation engine is in data connection with the multi-modal sensing module and is used for analyzing multi-dimensional risk association in the fused data stream; the invention belongs to the technical field of electricity markets, and aims to solve the problems that in the prior art, historical data static analysis is relied on, spatial-temporal scales of multi-source data are not uniform, and data cannot be given in time. The technical effects are that a closed loop is formed through sensor data, a multi-mode sensing module (alignment processing), an adaptive evaluation engine (risk analysis), a digital twin system (simulation strategy) and a dynamic feedback module (optimization system), automatic calibration and learning are realized, and the risk prediction accuracy and the reliability of the scheme are improved.

The invention, which relates to the technical field of payment security, discloses a payment-scene-oriented interaction intention identification and error correction system comprising an input analysis module, an intention simulation module, a dynamic decision module, a biological verification module, an audit evidence storage module, and a cross-scene knowledge migration module. According to the method, multi-modal data such as voice, texts, images and touch tracks are integrated, structured feature vectors are generated through a cross-modal attention network, the problem of incomplete single-modal coverage is solved, cross-modal data consistency verification is achieved based on a unified semantic tag system, and the reliability of input sources is graded by combining equipment fingerprints and geographic positions, so that the reliability of the input sources is improved. A high-risk transaction protection capability is enhanced, a generative adversarial network is utilized to construct a virtual attack sample library, attacks such as tampering with characters similar in shape and AI faking voiceprints are simulated, unknown threats are actively defended through cosine similarity matching, a user historical behavior statistical model is integrated, and known risks such as high-frequency small-amount transfer are passively intercepted. And a closed-loop incremental learning continuous optimization model is supported.

The invention provides a network service acceleration method and system applied to a security transaction service platform, and the method comprises the steps: firstly obtaining a security transaction request set sent by a user terminal in a transaction period, enabling each transaction request unit to comprise a request type identifier, a timestamp and content data, and then carrying out the classification according to the request type identifier to generate a priority queue, the method comprises the following steps: determining a queue ordering rule according to a timestamp, dynamically screening a proper service node group from a pre-established distributed service node cluster, mapping and binding the proper service node group with a priority queue, distributing request units to corresponding service node groups in batches according to the ordering rule to execute transaction operation, generating a response data packet, and returning the response data packet to a user terminal. And when the service node group executes the operation, the load state parameters are updated in real time and synchronized to the global load monitoring module, so that the network service acceleration performance of the security transaction service platform is effectively improved, and efficient transaction is ensured.

The invention discloses an intelligent network transaction credit risk assessment system and method based on multi-modal data fusion and deep learning, and particularly relates to the technical field of network transaction risk assessment, and the system comprises a multi-modal data collection, fusion and deep reinforcement learning agent, and a large model interaction and risk assessment and supervision execution module. The multi-modal data acquisition module acquires various data in real time; the fusion module realizes semantic alignment and interactive learning through multiple strategies; the deep reinforcement learning agent module optimizes a monitoring and supervision strategy based on an Actor-Critic framework; the large model interaction module provides multi-aspect support by means of various technologies; and the risk assessment and supervision execution module identifies illegal behaviors and executes supervision actions. The evaluation method comprises the steps of data acquisition and preprocessing, feature vector generation, agent training, large model deployment optimization, real-time monitoring decision and the like. The system can improve the monitoring and supervision efficiency, protects the rights and interests of consumers, constructs a healthy transaction environment, and promotes the development of related technologies.

The invention relates to the technical field of asset transaction risk monitoring, and proposes an asset transaction risk monitoring system based on data analysis, and the system comprises a data obtaining module which is used for obtaining and integrating multi-source heterogeneous data, including but not limited to transaction records, market quotation data, user behavior data, legal contract texts and financial indexes, the data is preprocessed; and the risk assessment module is connected with the data acquisition module through a data transmission technology, and assesses the risk in real time based on a preset rule and a machine learning model. Through timely acquisition and analysis of latest transaction information, market dynamics and user behavior changes, a reinforcement learning technology is utilized, and according to a real-time risk assessment result and a disposal effect, risk monitoring is ensured to be always matched with a market actual condition, so that a financial institution can timely capture a risk signal and take precautionary measures in advance, and the risk monitoring efficiency is improved. And the risk loss is effectively reduced.

The present invention discloses a system and method for secure artificial intelligence-based financial technology governance and risk management, designed to provide real-time, autonomous, and verifiable compliance assurance within digital financial ecosystems. The invention integrates a secure artificial intelligence processing unit, a governance control processor, a cryptographically anchored storage unit, a federated learning coordination processor, and a quantum-resistant communication interface enclosed within a tamper-proof hardware structure. The system performs encrypted machine learning computations on financial transaction data using homomorphic encryption and trusted execution environments to preserve confidentiality during analysis. It computes a governance risk index based on probabilistic inference and anomaly detection to identify regulatory deviations, applies adaptive compliance reasoning across multi-jurisdictional frameworks, and automatically enforces governance actions through secure decision logic.

The invention relates to the technical field of digital economic risk control, and discloses a digital economic risk identification system and method based on artificial intelligence. A risk data acquisition engine of the system obtains transaction behavior data streams from a plurality of digital economic transaction platforms in real time, and converts the transaction behavior data streams into a structured transaction feature matrix; an abnormal mode detection engine extracts time sequence abnormal features through a deep residual network to generate an abnormal feature vector set; the risk association analysis engine constructs a risk propagation path map through a graph neural network, and outputs a risk association degree scoring matrix; the dynamic threshold adjustment engine performs adaptive threshold calibration according to the historical risk event database to generate a dynamic risk threshold vector; and the risk decision engine compares the scoring matrix with a dynamic threshold value, marks risk transaction nodes and generates a risk early warning instruction set. The system can adapt to digital economic transaction characteristics, and the comprehensiveness and accuracy of risk identification are improved.