10188 results about "Financial transaction" patented technology

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 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.

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.

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.

The invention belongs to the technical field of financial data management, and particularly discloses a financial multi-source protocol self-adaptive fusion system based on AI semantic understanding and a knowledge graph, which comprises the steps of avoiding fusion errors caused by semantic misunderstanding through deep semantic analysis and a conflict resolution decision based on the knowledge graph; when protocol version updating is detected, incremental learning and model adjustment are carried out based on a newly added sample and historical experience, and a fusion protocol standard is dynamically updated, so that the high adaptive capacity to dynamic change of a financial protocol is realized; an exception monitoring and repairing mechanism is introduced, data missing, format errors and other exceptions are found in time and repaired online based on historical data modes and business logic, and negative influences of abnormal data on downstream risk control, transaction decision making and other key business links are avoided; through deep collaboration and information feedback among the intelligent modules, an intelligent system capable of self-learning and evolution is constructed.

Systems and methods for securing transactions in accordance with various embodiments of the invention are illustrated. One embodiment includes a method for confirming token ownership rights. The method generates a pre-image value using a random number generator. The method generates an action request that corresponds to an action performed cryptographic token(s). The method derives a commit output based on the pre-image value. The method generates a commit request that obfuscates the action request based on the commit output and submits the commit request to an immutable ledger. The method receives a confirmation when the commit request is approved by the ledger. When a reveal request referencing the commit output is published, the method receives a confirmation of at least one of: the reveal request being published, that the reveal request comprises a reference to the commit output, or that the action request has been approved.

The invention is suitable for the technical field of data asset management, and provides a data asset management system based on a block chain and big data analysis, and the system comprises a first terminal which carries out the storage of the ownership information of data assets through a block chain network, and generates a storage data package; generating an evaluation result based on a preset technical index analysis model; writing the hash value of the evaluation result into transaction data of the main chain of the block chain, and generating an evidence storage voucher matched with the block chain transaction; an interaction data sequence is packaged for identity signature, encryption and compression to generate a target code stream, and the target code stream is sent to the second terminal; the second terminal decrypts the encrypted data in the target code stream by using a private key, decompresses the data by using a compression algorithm matched with the first terminal, and restores the data into an interactive data sequence; verifying the access authority and the transaction condition of the evidence storage data packet; checking the anchoring state of the evidence storage voucher on the main chain of the block chain; and the data transaction is completed, the data asset state report is generated, and the data element marketization configuration efficiency is improved.

The invention discloses a listed company operation risk early warning method based on multi-source auditing and text semantic fusion, and relates to the technical field of auditing, and the method comprises the steps: S1, crawling and converging multi-source heterogeneous data of listed company financial newspapers, auditing suggestions, supervision announcements, inquiry letters, news public opinions and market transactions; according to the method, unstructured texts are subjected to cleaning, blocking and semantic vectorization processing, each text segment is embedded into a high-dimensional semantic space, a vector index is established, a bottom-layer knowledge base of an RAG framework is formed, in the stage, it is ensured that the data structure is uniform, the source is traceable, standardized input is provided for subsequent semantic retrieval and modeling, and the reliability of the system is improved. S2, a query expression is constructed based on a target company, a time window and a risk topic, dense semantic retrieval and sparse BM25 retrieval methods are comprehensively used, a time decay and source credibility weighting mechanism is introduced, and the problems that a traditional method is single in data dimension and information is split are solved.

The invention provides a fund flow abnormity intelligent early warning analysis method, and relates to the technical field of data processing, and the method comprises the steps: obtaining transaction data of a target account in a continuous time interval; semantic feature extraction is carried out, and a fund semantic unit set is generated according to a transaction initiator, a transaction receiver and a business type; performing causal correlation calculation on adjacent fund semantic units to determine behavior causal strength; when the behavior causal intensity is lower than a preset causal threshold value, marking the corresponding time period as a behavior fault region; performing context reconstruction on the account set in the behavior fault area; executing mode deviation analysis, and identifying fund flow modes with abnormal aggregation or reverse backflow characteristics; performing self-learning updating on the abnormal threshold weight of the initial judgment model to obtain a corrected judgment model; detecting subsequent transaction data according to the corrected judgment model, and generating fund flow abnormity intelligent early warning information; according to the invention, autonomy and accuracy of fund flow abnormity intelligent early warning analysis are improved.

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

The present embodiment discloses a novel process and system for storing versioned data on the Bitcoin blockchain network. This process involves generating derived public keys linked to specific versions and data parts using a key tweaking method, and storing each data part on the blockchain using its associated derived public key. Information about versions and parts are stored in root transactions. An API is provided for managing the stored data, with options for encryption, checksums, data anchoring, timestamping, segmentation, and digital signatures. AIDIOS, a data protocol designed for direct storage and retrieval on the Bitcoin blockchain, is also included. This system allows for significant amounts of data to be stored directly on the blockchain.

The invention discloses an electric power spot market electric charge settlement system and method fused with a block chain technology, and the system comprises a core settlement layer which is used for storing the electricity price and user identity data, and executing an electric charge settlement rule through a built-in intelligent contract module; the high-frequency transaction processing layer is used for processing a transaction process through an under-chain caching technology and uploading data in the transaction process to the core settlement module; an external data interface is integrated on one side of the high-frequency transaction processing module and used for acquiring electric quantity and load data in real time, and a cross-chain gateway is arranged on the other side of the high-frequency transaction processing module and used for achieving synchronous settlement of electric charge and capital flow. According to the method, the efficiency and transparency defects of a traditional mode are overcome, the performance and energy consumption bottleneck of the block chain technology in an electric power scene is broken through, high efficiency, safety, expandability and environmental protection benefits are achieved, and a credible, real-time and compliant digital settlement infrastructure is provided for an electric power spot market.

A system for real-time creditworthiness assessment of small and medium-sized enterprises (SMEs) based on agent-based artificial intelligence (AI), the system includes: a large number of autonomous agents connected to each other via secure communication protocols; a data ingestion processing unit configured to capture and preprocess heterogeneous data streams from structured and unstructured sources, including APIs for financial transactions, tax databases, e-invoicing registers, inventory management systems, social sentiment feeds, and IoT-enabled devices, with preprocessing including schema harmonization, anomaly detection, and encryption-based integrity preservation; an ensemble of scoring control units configured to generate dynamic credit scores, wherein the ensemble includes machine learning models, including gradient-enhanced decision trees, recurrent neural networks, and graph neural networks, the graph neural networks being configured to assess relational dependencies between SMEs, suppliers, and customer nodes in a business ecosystem; an audit controller configured to generate regulatory-compliant justification paths for each credit score; a feedback processing unit configured to update the evaluation control unit ensemble using reinforcement learning signals derived from observed repayment behavior, payment default trends, and fraud detection markers; and a user interface configured to display timestamped credit histories, confidence intervals and explanatory justifications, with the entire system running on a containerized cloud-native infrastructure with blockchain-based ledgers for version control of each point update.

A method of provisioning a credential for a transaction between a payment device of a user and a transaction processing device is described. The method is implemented a third-party server remote from the payment device and the transaction processing device. The method comprises assigning the credential to the transaction and the user and provisioning the credential into the payment device for subsequent retrieval by the payment device. Assigning the credential to the transaction comprises: determining unique identifying information of the user; allocating a first user-identifying portion of the credential to correspond to the unique identifying information; determining a plurality of properties intrinsic to the transaction and encoding the plurality of properties into transaction-identifying information; and allocating a second transaction-identifying portion of the credential to correspond to the transaction-identifying information. The credential is associated with encrypted authentication data that is generated by the payment device during the transaction, and used for subsequent authentication of the transaction by the remote third-party server.

Systems and processes are disclosed for enhancing cybersecurity and optimizing software repositories through integration of web crawling, web scraping, feature engineering, and advanced machine learning algorithms to detect phishing attempts, prevent account takeover fraud, and identify unused code in repositories. The system collects and refines data from various sources, including transaction logs, customer databases, device details, external data sources, and historical fraud data, to build comprehensive datasets. Feature engineering creates new, meaningful features from the refined data, which are used to train and evaluate machine learning models. The best-performing models are deployed in production to monitor incoming communications and transactions in real-time, flagging suspicious activities and optimizing codebases. This processing ensures timely detection and prevention of security threats while maintaining efficient software development processes. Robust protection is provided against evolving cyber threats and enhances software performance and security through continuous learning and adaptation.

The invention belongs to the technical field of novel electric power system operation control and trust management, and particularly discloses a virtual power plant full-process trusted aggregation method and system based on a hierarchical trust chain. Differentiated hierarchical trust chain models of a data acquisition link, a scheduling control link and a market transaction link are constructed respectively; through real-time credibility evaluation and adaptive weight adjustment, credibility calculation of multi-agent collaborative decision is realized. According to the invention, a trust network formed by a plurality of efficient, safe and transparent virtual power plant trust chains is constructed, solid technical support is provided for fair competition, intelligent scheduling and reliable operation of a power market, and efficient interaction and stable operation of a power system are ensured.

The invention discloses an intelligent recommendation precision marketing method and system based on a large model, and belongs to the technical field of artificial intelligence and retail, and the method comprises a data perception layer which is used for collecting user related information and comprises a face recognition terminal, an Internet of Things sensor, a CRM system and transaction POS data; the intelligent decision-making layer is used for performing fusion modeling and recommendation strategy generation on the sensing data and comprises a dynamic image engine, a demand prediction large model and a recommendation strategy generator; and the execution feedback layer is used for sending the recommendation result to the user in a multi-channel manner and recycling feedback data, and comprises a shop assistant PAD push module, an electronic price tag updating module and a self-service terminal guide module. The repurchase rate and customer unit price of a retail scene can be remarkably improved, and the technical problem that a traditional retail robot cannot adapt to complex requirements of customers is solved.

The invention relates to the technical field of intelligent customer acquisition, in particular to a cross-border supply chain financial service AI intelligent customer acquisition system based on big data analysis. Functional components such as a big data access engine, an artificial intelligence analysis model, a customer portrait modeling engine, a preference setting interface, a public opinion and risk identification module, an intelligent screening engine and a docking output module are integrated; multi-source data such as customs data, trade data, enterprise industry and commerce data, judicial public data, international manufacture and cutting lists and internet public information from all places around the world are widely fused. The system performs modeling calculation on industry categories, regional distribution, export history, transaction records, public opinion trends and compliance risks of import and export enterprises, establishes customizable customer optimization rules, accurately outputs a target customer list which can be covered by credit insurance and financing service in combination with a machine learning algorithm, and attaches contact information. Various import and export related data resources can be accessed, automatic screening and output of credit insurance and account receivable financing adaptive exporters are realized through preference setting and AI analysis, the customer obtaining efficiency is remarkably improved, the marketing and risk control cost is reduced, and the economic benefit is improved. The problems that traditional cross-border supply chain financial service customer obtaining depends on manual recommendation, offline visit and a static customer library, the efficiency is low, and small and medium-sized export enterprises are difficult to cover are solved.

Aspects of the present disclosure describe a system comprising a processor and memory storing instructions, which when executed by the processor, enable the system to detect a payment-related event using vehicle sensors, such as a camera or GPS unit. The detection can involve a machine learning model trained to recognize objects associated with the payment-related event while minimizing false positives. Upon detecting the event, the system can handle the electronic payment by utilizing an in-vehicle digital wallet to execute the payment and recording the transaction on a blockchain-based ledger.

The invention discloses an energy big data right confirmation method and system based on a hierarchical hash tree and dynamic authorization, and belongs to the field of energy big data management and information security, and the method comprises the steps: obtaining and standardizing original data in an energy scene, and dividing the original data into a data block set according to equipment and time; constructing a hierarchical hash tree and generating root hash; writing the root hash and the associated metadata into the block chain to realize right confirmation and evidence storage; executing proxy re-encryption according to the access token, and converting the ciphertext into a decryptable format; verifying the data consistency through the Hash path and the root Hash, and completing the access; and recording the access behavior and distributing transaction earnings by the smart contract based on the contribution degree. Hash calculation and local path updating of a data block level are supported through a layered Hash tree structure, so that when large-scale energy data is frequently updated, a new root Hash value can be quickly generated and right confirmation updating can be completed only by carrying out local Hash recalculation on a changed path.

The invention relates to the technical field of cross-border logistics, and discloses a cross-border e-commerce commodity traceability method and system based on a block chain, and the method comprises the following steps: generating a unique traceability code for each cross-border e-commerce commodity, extracting an original information field of the cross-border e-commerce commodity, and calculating an anti-counterfeiting hash value; performing structured packaging on the original information field and the anti-counterfeiting hash value according to a preset template, confirming a transaction through a consensus mechanism, writing the transaction into a block chain, and generating a block hash value; operation information of all logistics nodes is collected, abnormity is recognized through an abnormity detection algorithm, and data fingerprints obtained after abnormity recognition are encrypted and stored; the encrypted and signed operation information is submitted to a block chain network, and a block chain node verifies the validity of a digital signature through a public key of a producer and verifies the legality through a consensus mechanism; and proving an aggregation verification result by using zero knowledge. According to the invention, a complete traceability path is displayed, and full-chain transparent management from production to consumption is realized.

The invention provides a financial machine room AI operation and maintenance method and device and a readable storage medium. The method comprises the steps of obtaining multi-modal data; preprocessing the multi-modal data to obtain preprocessed data; transform depth feature fusion is carried out on the preprocessed data, and a fusion feature vector is obtained; according to the fusion feature vector, LSTM time sequence prediction and evaluation are carried out, a prediction result is obtained, and the prediction result comprises at least one of the equipment health degree score, the fault occurrence probability, the business influence degree and the residual service life of the equipment. According to the application, the physical state monitoring of the equipment and the real-time state of the financial service are deeply fused, so that the fundamental transformation from the traditional passive response to the active prevention is realized. According to the invention, a business-aware intelligent prediction model and a transaction-aware-free resource scheduling mechanism are constructed especially for the high availability requirement and the transaction continuity guarantee requirement of a financial machine room.

The embodiment of the invention provides a credit scoring method and device for a credit white user. Wherein the data provider comprises a government affair platform party, a transaction opposite party and a financial institution party, and the user data of the financial institution party comprises a user identifier of a credit white user. And the plurality of data providers align the local user data of each party through a privacy intersection algorithm, so that each party determines the first user data of the aligned credit white user from the local user data. And the plurality of data providers respectively encrypt the local first user data of each party and send the encrypted first user data to the service platform. And the service platform performs secret state model prediction based on a local credit scoring model and the first encrypted user data of each party, obtains a secret state credit score of the credit white user, and provides the secret state credit score to a score user. And the score user decrypts the credit score from the secret state credit score, and determines a risk control decision for the credit white user based on the credit score.

The invention provides an enterprise intelligent finance and taxation system fusing supervised learning and a block chain, and solves the core technical problems of insufficient data credibility, transparent contradiction between privacy protection and supervision, AI model training data island and the like of a traditional finance and taxation system. According to the system, an innovative technical fusion scheme is adopted, bank API, OCR recognition, voice input and other multi-source financial data are integrated through a multi-modal data fusion unit, and high-quality data fusion is achieved through confidence evaluation and an intelligent conflict resolution algorithm; an intelligent classification unit based on BERT deep learning integrates a pre-training model with a business rule engine and a gradient boosting decision tree, and accurate and automatic classification of financial transactions is achieved. A complete technical solution is provided for enterprise finance and taxation digital transformation, intelligent, automatic and credible processing of finance and taxation businesses is achieved on the premise that data safety and privacy protection are guaranteed, and the method has wide market application prospects.

The invention discloses a multi-microgrid system distributed optimization scheduling method based on an electric power-carbon market, and the method comprises the steps: constructing an energy consumption equipment model and a carbon quota transaction model based on the energy flow and carbon quota transaction process in a microgrid; a dynamic collaborative pricing model is constructed based on the power and carbon quota market supply-demand relationship; constructing an operation cost optimization model of a single micro-grid system based on the above models, and constructing a multi-micro-grid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all micro-grids based on a Nash bargaining game framework; and solving by adopting an accelerated prediction-correction alternating direction multiplier method algorithm to obtain an optimal scheduling scheme based on power-carbon market coupling, thereby realizing energy operation scheduling of the multi-microgrid. Through power-carbon market coupling, dynamic pricing, game theory optimization and an efficient distributed algorithm, the operation cost of the micro-grid system is reduced, and the reduction of the operation cost assists in improving the operation income of the micro-grid system.

The invention relates to the field of financial science and technology, and particularly discloses a cross-platform financial data integration and real-time analysis system and method. The system is characterized in that a distributed heterogeneous data acquisition module accesses multi-source data in parallel through a pluggable protocol adapter; the streaming data cleaning engine realizes field-level normalization based on a dynamic template and semantic mapping; the hybrid storage architecture forms a three-level system by a memory database, a time sequence database and distributed file storage; the stream processing core engine adopts an elastic sliding window to execute CEP complex event processing; and the dynamic analysis decision module integrates online machine learning to generate a real-time transaction signal. The method comprises the steps of multi-source concurrent acquisition and microsecond-level timestamp injection, context sensing data standardization, dual-channel data storage, event time window aggregation, dynamic risk calculation and block chain audit evidence storage. The defects of low data fusion efficiency, high real-time analysis delay and the like in the prior art are overcome, and cross-platform data processing timeliness and decision accuracy are remarkably improved.

The invention provides a quantitative transaction strategy dynamic generation method and system based on a large model, and a storage medium. The method comprises the following steps: S1, collecting market data, related financial news and information of financial products; s2, inputting market data, financial news and information into a large language model, analyzing current market environment characteristics, and intelligently selecting a matched transaction strategy type through a strategy generator; s3, performing simulated transaction verification on the generated strategies by using historical market data, and selecting the strategy with the optimal performance from a plurality of candidate strategies through risk assessment, parameter optimization and strategy screening; s4, deploying the strategy passing the verification of the large model to a transaction execution system, monitoring the execution effect, the market environment and the risk index of the strategy, and dynamically adjusting the strategy parameters or regenerating the strategy according to the monitoring result; and S5, a feedback learning step. The method has the advantages that multiple strategy types are integrated, and diversity and innovativeness of strategies are improved.

The invention provides a financial account distribution and anomaly detection method and system based on machine learning, and relates to the technical field of machine learning, and the method comprises the steps: building a causal graph model, generating a time sequence attention weight matrix through a conditional random field, carrying out the weighted fusion, and carrying out the attribution of a difference reason; and processing the reconciliation difference feature vector by adopting a reinforcement learning method, generating and optimizing a processing strategy, and finally outputting a difference processing scheme. The account checking accuracy can be improved, abnormal transactions can be automatically found, the manual processing cost is reduced, and financial risks are effectively prevented.

The invention discloses a bidding and incentive combined strategy optimization method, system and device for a source-load double-side peak regulation auxiliary service market, and a storage medium, and belongs to the technical field of electricity market transaction. The method comprises the following steps: constructing a load aggregator and thermal power generating unit-oriented double-market joint optimization model according to parameter data of a peak regulation auxiliary service market and an incentive demand response market; the double-market joint optimization model is formalized into a Markov decision process; constructing a multi-task multi-agent reinforcement learning training framework; and solving the multi-task multi-agent reinforcement learning training framework through an asynchronous training multi-task multi-agent flexible action-evaluation algorithm to obtain a load aggregator bidding and incentive optimal joint strategy and an optimal bidding amount and quotation strategy of the thermal power generating unit. According to the method, game behaviors among multiple market participants and joint decision behaviors of demand-side market subjects in double markets are considered at the same time, and the method is widely applicable to multi-task agent collaborative optimization in the electricity market.