2955 results about "Transaction data" patented technology

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 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 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 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.

Systems and techniques may generally be used for chatbot-based fraud assistance. An example method may include initiating a chatbot session with a user and receiving a prompt from the user related to suspected suspicious activity in an account. The method may include retrieving, using a Retrieval-Augmented Generation (RAG) component, contextual information from at least one of transaction data and a knowledge base. The method may include evaluating the prompt using a large language fraud model and the retrieved contextual information to determine a response.

The invention relates to the technical field of data processing, and provides a trade big data processing and user behavior prediction method and system.The trade big data processing and user behavior prediction method comprises the steps that transaction data, browsing behavior data and feedback evaluation data of a user on a cross-border e-commerce platform are obtained to generate a user behavior pattern sequence, and then a user interaction graph is constructed; and carrying out edge weight updating and node label classification to obtain a high-dimensional relation graph, carrying out similarity partition processing to obtain a predicted popularity index graph, and predicting the future cross-border commodity interaction behavior of the target user group by using the predicted popularity index graph. By acquiring transaction data, browsing behavior data and feedback evaluation data of users and generating a prediction popularity index graph, multi-dimensional data are effectively and comprehensively processed, accurate prediction of cross-border commodity interaction behaviors of a target user group is realized, and the user experience is improved when a user intention deep relationship is processed and cross-time-period behavior evolution analysis is performed. The problems of low model dimension and insufficient prediction precision exist.

The present invention relates to a fast anti-money laundering detection method based on a transaction graph, comprising: obtaining transaction data flows and constructing a directed graph structure to form a transaction graph; performing preliminary determination on the transaction graph, wherein if hit, the transaction is blocked, otherwise the transaction graph is sent to a graph neural network based on location information; performing feature learning according to a transaction feature of each node and aggregating a node feature and a full graph feature; performing prediction on a transaction between nodes according to the features, wherein if there is a high risk, an expert estimation is performed and the transaction is sent to a historical transaction database, and if there is a low risk, a transaction result is recorded and sent to the historical transaction database; and updating the network by the historical transaction database according to the transaction result.

The invention discloses a foreign trade e-commerce cloud network aggregation supervision method based on cloud service, and the method comprises the following steps: S1, collecting cross-border e-commerce transaction data, logistics distribution data, storage data and customs supervision data in real time, and carrying out the standardization processing through a distributed cleaning method; s2, constructing a multi-source heterogeneous graph according to the standardized data; s3, marking types, relation types and timestamps for nodes and edges in the graph, and extracting high-dimensional features through a relation enhancement graph Transform; s4, performing local and global aggregation through a multi-layer attention mechanism to generate an aggregation view; s5, constructing a multi-target supervision task model, and generating a resource scheduling scheme; s6, transaction, logistics and supervision abnormity is identified, and a risk early warning and intervention strategy is generated; and S7, dynamically optimizing the relation structure and Transform parameters according to the execution feedback. According to the method, fusion supervision and risk early warning of foreign trade e-commerce cross-domain multi-source data are realized based on high-dimensional graph expression and an optimization scheduling mechanism.

Systems and methods for decentralized orchestration of event records within decentralized computing environments. A processing system including decentralized execution nodes dynamically allocates microservice instances based upon real-time node performance metrics. Transaction requests including transaction data digitally associated with decentralized identifiers (DIDs) are processed by allocated microservice instances. The processing includes verifying transactions, generating cryptographically-linked immutable memorialization records associated with respective DIDs, and distributing these records to a tamper-evident distributed ledger configured to enforce immutability through consensus nodes. The processing system dynamically reallocates microservice instances among decentralized execution nodes based upon monitored utilization metrics and predefined scaling thresholds. Each memorialization record is cryptographically secured and linked permanently to the DID for data immutability, enhanced security, and dynamic scalability for event data processing in distributed infrastructures.

The invention relates to an artificial intelligence-based cargo transportation path planning method and system for automatic warehouse logistics. The method comprises the steps of performing weighted scoring calculation based on customer historical transaction data and cargo attributes, and generating service label data for distinguishing a high-value hierarchy and a common hierarchy; a differentiation algorithm strategy is called according to the service label, an exclusive transportation path is distributed to the high-value goods, obstacle avoidance is optimized in real time, and adjacent orders are matched for common goods to be merged and distributed; in combination with real-time road conditions and storage scheduling data, through path weight dynamic calculation and genetic algorithm iterative optimization, generating a global transportation scheme considering both time efficiency and cost; customer preference parameters are updated in a closed loop mode based on execution feedback data, and system self-adaptive learning is achieved. According to the method, the problems that differentiated services, dynamic response to environment changes and low-efficiency resource utilization cannot be achieved in a traditional method are solved, the punctuality rate of high-value orders and common orders can be increased, and meanwhile the transportation cost of the common orders can be reduced.

The invention provides a financial analysis method and system based on big data, and the method comprises the steps: analyzing the dependency relationship between a non-standard code combination and a fuzzy text mode in a material purchasing scene based on a credible rule library, extracting a new attribute combination feature, and forming an optimization rule feature set if the new attribute combination feature can improve the matching accuracy; performing rule-based key field and attribute set verification matching on the real-time non-standard transaction data according to the optimization rule feature set in combination with a dependency relationship between a non-standard code combination and a fuzzy text mode, and outputting a matched transaction record set; and comparing the difference between the matched transaction record set and the historical manual matching record, extracting and judging whether the newly added non-standard attribute combination is applicable to the non-standard transaction data which is not covered or not after a matching rule is adjusted by adopting an incremental learning algorithm, and if so, forming a dynamic matching table.

The invention discloses a bank system risk monitoring and early warning method and system based on artificial intelligence, and relates to the field of data processing suitable for administrative, commercial, financial, management, supervision or prediction purposes, and the method comprises the steps: constructing an account transaction network according to transaction data of a target time window, an account being a node, and a transaction relationship being a directed edge; an initial importance score is calculated by analyzing a flow direction characteristic, a time correlation characteristic and a business mode characteristic of an account, importance transfer between nodes is carried out, a link position characteristic is constructed by combining a fund dispersion degree and a path collaboration degree of a preorder transaction path, an account suspicious degree score is finally calculated, and an early warning is given to the account exceeding a threshold value. By implementing the method, the timeliness of discovering the bank transaction risk behavior can be improved.

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 vehicle transaction risk intelligent early warning and credit assessment method based on big data analysis. The method comprises the steps of S1, collecting an original transaction data set; s2, constructing a heterogeneous graph structure based on the original transaction data set; s3, dividing the heterogeneous graph structure into a graph snapshot sequence according to a fixed time window, and generating a time evolution graph sequence with time evolution characteristics; s4, constructing low-dimensional feature representations of the user nodes and the transaction event nodes under each time window; s5, constructing a causal path graph according to the time sequence and behavior dependency relationship of the vehicle transaction record data, and extracting a high-frequency transaction causal chain from the causal path graph; s6, performing joint modeling on the low-dimensional feature representation of the user node and the transaction event node and the high-frequency transaction causal chain; and S7, outputting the risk level label of the target transaction and the credit scoring result of the corresponding user. The method has the advantages of being high in structure expression ability, clear in causal logic and high in risk assessment accuracy.

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.

The invention discloses a financial data intelligent auditing analysis method based on deep learning, and the method comprises the following steps: S1, collecting enterprise financial data, constructing a dynamic memory adaptive attention network, and generating weighted transaction data; s2, calculating a weight factor of each transaction, and mapping the weighted transaction data to an attention weight distribution space; s3, screening transaction features by using variational information bottlenecks, and extracting key transaction features; s4, optimizing hyper-parameters of the dynamic memory adaptive attention network by using a whale optimization algorithm according to the key transaction characteristics; s5, processing the newly input financial transaction data, and judging an abnormal transaction based on a preset threshold value; and S6, generating an audit analysis result according to the abnormal transaction, and performing comparative analysis in combination with a historical audit record. According to the method, deep learning, machine learning, a statistical method and an intelligent optimization algorithm technology are combined, and financial data intelligent auditing analysis based on deep learning is realized.

The invention relates to the technical field of financial intelligent risk management and control, and discloses a bank transaction risk assessment method based on big data analysis, and the method comprises the steps: collecting historical bank transaction data, extracting abnormal transaction data and related influence factors, and carrying out the analysis of the abnormal transaction data; the abnormal transaction data comprises abnormal indexes of transaction amount, transaction frequency, geographic position and user behavior mode; constructing a risk assessment model by using a big data analysis technology, performing weight distribution and feature extraction on the associated influence factors, and generating a dynamic risk assessment rule; and inputting real-time bank transaction data into the risk assessment model, calculating a risk probability value of the transaction, dividing the risk probability value into a high level, a middle level and a low level according to a dynamic risk assessment rule, and storing the risk probability value into a hierarchical database. According to the invention, the attention mechanism and incremental learning are combined and applied to the dynamic allocation of the risk weights, so that the automatic closed loop of risk grading and disposal strategies is realized, and the early warning accuracy and real-time performance of transaction risks 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 invention discloses an intelligent financial risk early warning method based on multi-modal data fusion, particularly relates to the field of financial risk management and control, and is used for solving the problem of existing enterprise transaction authenticity risk prevention and control. According to the method, a cross-modal transaction behavior chain of supervision enterprises is constructed through merging of transaction data in continuous account periods and modal information aggregation; generating an initial transaction relation graph based on the behavior chain, identifying business associated enterprises, and screening pseudo independent enterprises with control path crossing; performing modal semantic consistency check on the transaction path of the pseudo independent enterprise, identifying a semantic mismatch node, tracing a fund return path by taking the semantic mismatch node as a starting point, identifying a transaction closed-loop structure, and constructing a pseudo compliance risk chain; finally, the control relation and modal data are integrated, a structured evidence chain is generated, risk early warning output of involved enterprises is rapidly completed in a high-risk marking mode, and the automation degree, interpretability and accuracy of abnormal financial risk discrimination are improved.

The invention discloses a standardized processing system and method for multi-modal data acquisition and fusion of a network transaction platform, and particularly relates to the technical field of network transaction data acquisition, and the system comprises a data acquisition module, a preprocessing module, a multi-modal fusion module, a threat detection module and a response strategy generation module. The system is further provided with a privacy protection unit and a confrontation defense unit, the system collects multi-modal data from multiple sources, a standardized data set is generated through preprocessing, the fusion module conducts feature dimensionality reduction and semantic analysis, the detection module recognizes abnormity based on a large model, and the response module automatically generates a defense strategy. The method comprises the steps of data acquisition, processing, fusion, detection and response, and further comprises model deployment, test and visual display. The system can efficiently collect and process data, accurately detect threats, quickly respond and guarantee data security, and improve the security and stability of a network transaction platform.

Systems and methods for dynamically generating optimized application programming interfaces (APIs) using artificial intelligence (AI). The system may include an API database storing API data of a plurality of APIs of an API platform, and a generative AI model trained to generate an optimized API having at least a portion of the operational characteristics of one or more APIs of the API platform. The system may obtain an API call data stream of API calls from a user device to the APIs, and analyze the API call data stream to generate user-specific API transaction data. The generative AI model may receive the user-specific API transaction data and the API data to determine, and generate the optimized API. The system may configure the optimized API for execution, and provide the optimized API for execution by a user of the API platform.

The invention relates to the field of edge computing and Internet of Things, and discloses a miniature data transaction device which comprises an edge computing unit used for executing a centralized or decentralized matchmaking algorithm and a transaction protocol; the multi-dimensional detection module comprises a compliance detection unit, a safety detection unit, a quality evaluation unit and a scene applicability evaluation unit; the hardware accelerator is used for accelerating a detection task and encryption operation; the communication module supports multi-protocol data transmission; the storage unit is used for caching transaction data and intelligent contract codes; the power management unit is used for controlling the total power consumption of the equipment, and the edge computing unit comprises a hierarchical trusted execution environment architecture consisting of a safe enclave and a general computing unit. According to the method, a hardware isolation architecture of the secure enclave and the general computing unit is adopted, and the dynamic key encryption shared memory technology is combined, so that the effects of privacy computing and efficient matching parallel execution are achieved.

The invention discloses a PCIe cross-version link capability mapping and transaction data reconstruction method, which comprises the following steps: link negotiation and physical layer opposite end simulation: a cross-generation PCIe bridging device simulates high-version equipment behavior training and establishes a first link between the cross-generation PCIe bridging device and a host, the cross-generation PCIe bridging device simulates behavior training of low-version equipment and establishes a second link between the cross-generation PCIe bridging device and the low-version equipment; data processing and transmission: the cross-generation PCIe bridging device processes data received from the host to adapt to and transmit the data to the low-version device, the cross-generation PCIe bridging device processes data received from the low-version device to adapt to and transmit the data to the host, the high version is a PCIe Gen6 protocol, and the low version is a PCIe Gen6 protocol. The low version is a PCIe (Peripheral Component Interconnect Express) Gen5 protocol or a PCIe Gen4 protocol.

Various systems and methods for providing a digital estate plan system are described herein. An online system for managing beneficiaries and assets of a will is configured to access transaction data related to a user; identify a person or a property based on the transaction data; present a confirmation request to the user to confirm that the person is a beneficiary of the will or that the property is an asset of the will; and add, in response to receiving a confirmation from the user, the person to a list of beneficiaries of the will, or the property to a list of assets of the will.

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.

In one embodiment, a method includes accessing transaction data associated with a protected first function of a first blockchain transaction protocol by a cybersecurity authentication service of a blockchain cybersecurity platform, accessing a transaction protocol runbook comprising preconfigured conditions associated with the protected first function, determining that the protected first function is eligible to be analyzed by an automated decisional logic module based on the transaction protocol runbook, determining each preconfigured condition is satisfied based on the transaction data by the automated decisional logic module, generating an authentication object for the protected first function, generating signed transaction data based on the accessed transaction data and a private encryption key, wherein the signed transaction data is configured to update a second blockchain transaction protocol to indicate that the protected first function is authenticated, and transmitting a transaction bundle comprising the accessed transaction data and the signed transaction data.

The invention relates to the technical field of financial transaction security, and discloses a POS machine transaction risk identification method based on data analysis and a deep learning technology. According to the method, a transaction data stream is collected in real time, after multi-dimensional feature extraction and dynamic time sequence segmentation, the transaction data stream is input into a first deep learning model for preliminary risk scoring, high-risk time windows are screened, transaction data fine-grained features of the high-risk time windows are enhanced, then the transaction data stream is input into a second deep learning model for secondary risk judgment, and transaction risk tags are generated. Based on this, a real-time risk handling instruction is triggered. Wherein the multi-dimensional feature extraction comprehensively depicts transaction data, the deep learning model effectively mines risk features, and the dynamic time sequence segmentation adapts to different transaction frequencies. According to the method, the POS machine transaction risk can be accurately identified, disposal measures are taken in time, financial transaction safety is guaranteed, and the effect of improving risk identification accuracy, timeliness and comprehensiveness is remarkable.

The invention relates to a method and device for monitoring abnormal transactions, a medium and a program product. The method comprises the steps of obtaining multi-modal transaction data related to a target transaction in response to a transaction monitoring trigger instruction; dynamically selecting a streaming computing engine matched with the target transaction based on the type mode information of the target transaction; performing real-time data processing and analysis on the multi-modal transaction data by using the selected streaming computing engine; and inputting the feature data processed by the streaming computing engine into a trained target model to obtain an anomaly recognition result output by the target model, the target model being automatically iteratively updated based on new transaction data. According to the method and the device, the multi-modal transaction data related to the to-be-detected target transaction is acquired, the transaction behavior is comprehensively analyzed from multiple dimensions, the characteristics of the abnormal transaction can be more accurately captured, and the accuracy of abnormal recognition of the target transaction is effectively improved.

The invention discloses a financial analysis-based transaction anomaly detection method and system, and the method comprises the steps: obtaining transaction network data, calculating a risk index through node transaction data, and marking the risk index as an abnormal node if the risk index exceeds a threshold value; obtaining transaction links associated with the abnormal nodes to form a to-be-detected set, performing time sequence segmentation processing on the to-be-detected set to generate transaction segmentation points, and collecting multi-dimensional data to construct a transaction feature matrix; calculating the mode similarity between a to-be-detected link and a normal link, screening the high-similarity normal link, clustering the feature matrix of the high-similarity normal link, and generating a reference clustering center; and by calculating the deviation degree between the feature matrix of the link to be detected and the reference center, determining that the link is an abnormal transaction link if the threshold value is super dynamic. According to the invention, through multi-dimensional node modeling, time sequence dynamic feature analysis and mode matching, accurate identification of abnormal transaction nodes and links is realized, and detection comprehensiveness and prevention and control accuracy are improved.

The invention provides a block chain dynamic trust evaluation system and method based on artificial intelligence, and the method comprises the steps: carrying out the time sequence modeling of the transaction data of a block chain node and the consensus historical record of the node participation consensus, obtaining the behavior credibility of the block chain node, calculating the resource contribution degree of each transaction node, and carrying out the calculation of the resource contribution degree. Fusing the behavior credibility and each resource contribution degree to obtain a consensus score of each transaction node; constructing a node graph neural network of the block chain based on the transaction topological relation and the interaction frequency between the transaction nodes, detecting a plurality of malicious nodes in the block chain by using the node graph neural network, and further evaluating a consensus influence coefficient of the current malicious node aggregation on the block chain node consensus; and performing punitive correction on the consensus score of each malicious node through the consensus influence coefficient to obtain a final trust score of each transaction node in the block chain. Based on the above scheme, punishment correction of malicious node aggregation in blockchain node trust evaluation can be realized.