158 results about "Transaction time" patented technology

The invention discloses an abnormal transaction detection method and system based on multi-modal data analysis, and the method comprises the steps: carrying out the synchronous processing of a transaction amount sequence, account feature data and equipment information through a time window alignment mechanism, and obtaining a multi-modal transaction data set; respectively constructing a transaction network graph and an account relation graph according to the fund flow direction relation and the account relevance; using a weighted graph convolution network to extract transaction space features and account space features; performing time sequence modeling through a transaction time sequence sensitive gating circulation unit to obtain a transaction time sequence vector and an account time sequence vector; and performing feature enhancement based on transaction amount distribution contrast learning, and inputting into a classifier to obtain an abnormal transaction recognition result. According to the method, the technical problems of misalignment of cross-modal data time sequences, insufficient transaction amount sensitivity modeling, inaccurate time sequence dependency relationship capture and poor abnormal feature distinguishing capability in an existing multi-modal abnormal transaction detection method are solved.

The invention relates to an abnormal transaction behavior recognition method and system based on artificial intelligence, and belongs to the technical field of financial transaction risk control, and the recognition method comprises the steps: collecting real-time transaction flow data, user behavior time sequence data and association graph data of a user, carrying out the space-time alignment processing, and generating a space-time aligned structured data set; dynamically calculating a transaction time attenuation factor; extracting a composite feature vector, inputting the composite feature vector into a pre-trained dual-channel decision model, and performing confidence weighted fusion on a dual-channel decision result based on a time decay factor to obtain a final risk score; dynamically adjusting a risk judgment threshold, and generating a dynamic decision boundary; judging whether the final risk score exceeds a dynamic decision boundary, if so, outputting an abnormal transaction behavior recognition result, and judging a transaction risk level; and triggering a risk disposal action corresponding to the transaction risk level. The method can improve the understanding depth of complex transaction behaviors, and reduces the false alarm rate of abnormal transaction recognition.

The invention discloses a communication network multi-source big data analysis method based on distributed database collaboration, and relates to the technical field of communication data analysis, and the method comprises the steps: 1, generating a session contract number, a table snapshot number, a flow site set and a transaction time point at the start of a session, registering a collaborative metadata table, judging semantic compatibility, and setting a state; step 2, pushing down three anchors to a lake-warehouse, a message bus and a transaction library according to the serial number, performing freezing reading according to the anchors to obtain a lake-warehouse segment, a streaming segment and a transaction segment, performing alignment extraction on a joint feature set in the step 3, generating output through a deterministic operator chain, and storing the extracted joint feature set in a database; the method comprises the following steps: step 1, setting a session contract number, a field source and an operator relationship, reading an anchor code as a blood relationship code and writing the blood relationship code into a result table, and step 2, resetting three anchors and multiplexing a query plan during reproduction verification and cross-domain migration, and integrating blood relationship equivalence, result equivalence, freezing consistency, registration difference and migration fingerprints to realize session-level consistent reading, recalculation and auditing.

The invention relates to the technical field of data processing, in particular to a financial data processing system based on a block chain, which comprises an access chain authorization module, a write-in arbitration scheduling module, a voucher path identification module, a structure back adjustment point module and a chain type result output module. According to the method, the stability score is calculated according to the field calling sequence difference, the calling interval time difference and the calling return content consistency of the node access behavior log, the nodes with stable access behaviors are screened, and block access permission mapping is formed; a sorting label is constructed based on a transaction timestamp and a fund transaction amount to realize sorting of concurrent transaction priorities, dynamic matching judgment is performed through a public key address list and a multi-signature access threshold, and link access path redirection is formed, so that the possibility that a node tampers data is reduced, and the transaction efficiency is improved. And according to the chain segment block height, the relay node confirmation state and the tail end data field variation value, sequence verification and index tracing are carried out, so that the tracing capability of the financial transaction data is obviously improved.

The invention provides a real-time fraud detection method and system based on multi-modal fusion, and belongs to the technical field of financial transaction fraud detection.The method comprises the steps that firstly, payment behaviors, transaction texts and account behavior data are connected in series through a transaction event unique identifier, and a real-time multi-modal transaction data link is formed; generating an inter-modal fraud association link based on the inter-modal fraud association link; performing interactive operation to obtain cross-modal fraud intermediate features; constructing a transaction fraud tendency path according to a transaction link time sequence; and finally, analyzing the transaction fraud tendency path to determine a fraud association level, generating a fraud response instruction containing a transaction blocking instruction and risk notification content, and transmitting the fraud response instruction to a transaction processing system to terminate the transaction, so that multi-modal data can be comprehensively integrated, a transaction time sequence is considered, and the fraud detection accuracy and real-time performance are improved.

The invention provides a student transaction allocation and service evaluation method and system based on multi-modal hosting, and relates to the technical field of intelligent transaction scheduling and service evaluation, and the method comprises the steps: extracting semantic features in a student transaction request, fusing the semantic features with transaction type information to generate a transaction feature vector, constructing a transaction time sequence association graph, and obtaining a transaction time sequence association graph; identifying a processing dependency relationship, and forming a transaction family and a processing priority sequence thereof; combining skill vectors of the processing personnel, and realizing an optimal matching path by utilizing self-adaptive dynamic programming; and finally, dynamic adjustment and feedback updating are performed according to the matching result and the service score, so that the transaction processing efficiency and the service matching accuracy can be effectively improved.

The invention discloses a foreign exchange transaction open statistical method. The method comprises the following steps: receiving at least one group of transaction data in real time; according to the transaction time of the transaction data received in real time, determining the time of the open cross section into which the transaction data received in real time needs to be inserted; inserting all transaction data received in real time into the open cross section; performing Hash operation on the transaction data received in real time to obtain a corresponding Hash value, and sending the obtained Hash value to a verification center; according to a statistical dimension, all transaction data behind the open cross section in the statistical database are utilized to construct Merkel tree branch chains, root node information of each Merkel tree branch chain is obtained, and the root node information of each Merkel tree branch chain comprises a root node hash value and corresponding statistical dimension information; and sending the root node information of each Merkel tree branch chain to the verification center. The invention further provides electronic equipment and a computer readable medium.

A device, system and method for accelerated compliance testing for real-time detection of suspicious transactions in a transaction stream. A machine learning model may determine a global trend of cumulative transaction behavior based on all, a majority or a representative subset, of the stream of transactions. For each transaction in the stream, a skip rule engine may sort each security compliance test to be skipped or not skipped based on the test's relevance to the global trend of the cumulative transaction behavior. The not skipped security compliance tests may be executed in real-time for each transaction in the stream to generate real-time partial security assessments therefore comprising real-time suspicious transaction alerts. The remaining tests may be skipped in real-time and executed, at a time delay after transaction times, to generate time delayed supplemental security assessments therefore comprising time delayed suspicious transaction alerts to complete all compliance testing.

The invention provides a non-intrusive asynchronous processing method based on transaction event monitoring. An asynchronous event is triggered through a transaction event monitor after a database transaction is submitted, it is ensured that asynchronous operation strictly follows a transaction life cycle, and the risk of data inconsistency caused by dislocation of a thread pool task and a transaction time sequence in a traditional scheme is fundamentally avoided; by intercepting a business method carrying asynchronous execution annotations and automatically extracting parameters to generate an asynchronous request event, physical isolation of business logic and an asynchronous mechanism is realized, and architecture pollution is eliminated; and further dynamically routing to an instant execution or persistent execution module according to the policy type annotated and declared, so as to provide elastic execution capability for different service scenes. Therefore, on the premise of guaranteeing transaction atomicity, a zero-intrusive and extensible asynchronous processing framework is constructed, and the reliability and maintainability of the system are remarkably improved.

The embodiment of the invention discloses a training method and device of a risk detection system. The risk detection system comprises a dynamic graph representation model, a prompt generation module and a risk assessment model. The method comprises the steps that firstly, a dynamic graph representation model obtained through pre-training based on a transaction dynamic graph is obtained, the transaction dynamic graph comprises a plurality of transaction entity nodes and connection edges established due to the fact that the nodes have transaction behaviors, and the connection edges have transaction timestamp attributes; then, respectively taking a plurality of transaction entity nodes carrying the risk tags as target nodes, and generating node representations of the target nodes under a target timestamp based on the transaction dynamic graph by utilizing a dynamic graph representation model; then, determining a prompt vector of the target node by utilizing a prompt generation module; afterwards, the fusion representation between the node representation and the prompt vector is input into a risk assessment model to obtain a risk assessment result, and the risk assessment result is used for training the prompt generation module and the risk assessment model in combination with a corresponding risk label.

The invention provides a financial data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring transaction data generated by a financial service system from a block chain node in real time; performing tagging processing on the transaction data according to a preset rule, wherein the tagging dimension comprises a transaction type, a risk level and a data flow direction; based on a graph data model, converting the tagged transaction data into a structured relationship including nodes and edges, the nodes representing a transaction subject, and the edges including a transaction amount, transaction time and a data flow direction; and displaying the structured relationship in a dynamic graph form. The method is used for achieving the effect of improving financial data processing efficiency and accuracy.

The application discloses a transaction timestamp injection data time sequence reconstruction method compatible with a cloud native architecture, a data production end acquires transaction data and uploads the transaction data to a cloud message queue, a data consumption end acquires the transaction data from the cloud message queue and injects transaction time acquired from a transaction house, the cloud acquires the transaction data injected with the transaction time, creates a partition according to the transaction time, and stores new transaction data in a hot data area and old transaction data in a cold data area. The application reconstructs the transaction data time sequence through authoritative time source injection, ensures transaction sequence authenticity, constructs a time sequence reconstruction scheme based on native database functions, designs a lightweight field injection mechanism, avoids invasive modification of cloud service APIs, establishes a lightweight data fault tolerance mechanism, improves data fault tolerance, and finally solves the fundamental contradiction between unordered storage of the cloud native architecture and financial time sequence requirements and the fault tolerance requirement.

According to one embodiment, there is presented herein a method of licensing, authenticating, and time stamping transactions associated with stored a digital media item. According to a first embodiment, there is provided a method of storing licensing terms in a blockchain token, which terms can be retrieved from the token and examined by someone interested in licensing the item. In still another variation there is provided a method of memorializing changes to a digital item as it undergoes reviews by creating a time stamp associated with each change and storing that in a blockchain token.

The invention relates to the technical field of big data, and provides a transaction data processing method and device based on idle time, equipment and a medium, which can start a business thread to scan a market queue and a return queue in real time in a current transaction period so as to respond in time. In the continuous scanning process of preset times, when no new market information slice is scanned in the market information queue and no new return information is scanned in the return queue, calculating the current idle time according to an idle time determination strategy after the market information and the return are ensured to be processed; the operator type with high real-time performance is divided into the pre-calculation operator and the decision operator, the pre-calculation operator is processed in the current idle time, the decision operator is processed in the current transaction time, the idle time can be fully utilized, glitch time delay in the transaction process is reduced, and the performance and time delay penetration stability of a transaction system are improved.

The invention provides a spot transaction dynamic data aggregation method and system based on multi-objective optimization, and relates to the technical field of power markets, and the method comprises the steps: 1, extracting multi-dimensional transaction feature data from a power transaction platform, constructing a structured feature vector set through a hyper-incremental sequence algorithm, and carrying out the extraction of the multi-dimensional transaction feature data; executing initial clustering based on the vector set to generate an initial clustering result containing a typical mode center point; 2, two risk judgment boundary rays are constructed with the center point of the typical mode as a datum point, and an included angle is calculated; selecting five time sequence characteristic coordinate points in the included angle region according to the transaction time sequence, connecting the coordinate points to form an arc risk conduction path, and generating a risk correction factor according to the path closure degree. According to the method, while data privacy security is guaranteed, transaction clusters are accurately divided, abnormal data are effectively separated, comprehensive decision-making indexes are generated, high-quality data support is provided for dynamic decision-making of an electric power spot market, and transaction processing efficiency is improved.

Systems and methods for implementing record locking for transactions using a probabilistic data structure are described. This probabilistic structure enables adding of data records without growth of the data structure. The data structure includes a hash table for each of multiple hash functions, where entries in the respective hash tables store a transaction time and locking state. To lock a record, each hash function is applied to a record key to provide an index into a respective hash table and a minimum of the values stored in the hash tables is retrieved. If the retrieved value is less than a transaction time for a transaction attempting to lock the record, locking is permitted and the transaction time is recorded to each of the hash tables. To commit the transaction, the probabilistic data structure is atomically updated as part of the commit operation.

The invention relates to the technical field of multi-party secure transactions, and particularly discloses a multi-party secure transaction method based on cross-border e-commerce business rules, which comprises the following steps of: S1, creating a transaction processing center, acquiring historical transaction data of the transaction processing center, and screening high-frequency e-commerce users and authentication information of the high-frequency e-commerce users; s2, constructing a high-frequency authentication information time sequence, selecting time nodes to obtain text vectors to form a set, and obtaining a text change curve by means of principal component analysis; s3, judging whether the high-frequency authentication information is inert change information or not according to the curve, if so, determining a stable interval, and establishing a pre-caching center to store information in the latest stable interval; s4, during cross-border e-commerce user transaction, information extraction and transaction time periods are obtained, and when inert change information and the information extraction and transaction time periods are in the same stable interval, the transaction processing center obtains information from the pre-caching center; according to the method, repeated calculation and data interaction are reduced, the efficiency of cross-border e-commerce multi-party secure transaction is improved, and the calculation burden is reduced.

The invention discloses an Ethereum phishing node detection method based on a graph neural network, and relates to the technical field of network node analysis, and the method comprises the steps: obtaining an account of an Ethereum network and a transaction record corresponding to the account, and constructing a directed weighted multi-graph, the transaction record comprising a sender, a receiver, a transaction amount and a transaction timestamp; extracting transaction data based on the directed weighted multi-graph, and taking the transaction data as basic statistical characteristics of nodes; sequencing the transaction records between the nodes in the directed weighted multi-graph and the adjacent nodes based on the timestamps of the transaction records in the directed weighted multi-graph to generate a transaction record sequence; feature differences between phishing nodes and non-phishing nodes are enhanced through a graph difference convolution method, and structural features of the nodes are extracted by adopting a graph convolutional neural network; and performing category balance processing on the data, and inputting the node features after category balance into the classification model to classify the nodes.

This invention relates to the field of AI-based risk control technology in financial payments, specifically to an AI-based real-time payment risk early warning method and system. The method includes: acquiring transaction behavior feature records containing standardized transaction amount ranges, transaction time slice distributions, and a relational graph of transaction participants; deconstructing the relational graph using a graph structure analyzer to extract potential indirect correlation paths between the transaction initiator and historical risk accounts, generating a risk correlation feature vector, and combining this with multi-dimensional behavioral analysis to obtain a behavioral deviation index; inputting the two types of features into a risk decision fusion model to obtain a comprehensive risk score; generating a dynamic risk threshold based on the risk level; triggering an early warning signal when the score exceeds the threshold, and generating an early warning instruction containing a risk cause code and interception suggestions. This method can uncover multi-level hidden risk correlations, dynamically adapt to the threshold settings for transaction risk levels, improve the completeness of risk feature extraction, and optimize the adaptation effect of risk early warning judgment.

The invention discloses a transaction time sequence characteristic data processing method and device. The method comprises the following steps: constructing a current time sequence financial transaction vector; using an associated party transaction update switch to control whether the current time sequence financial transaction vector needs to be updated so as to determine that the current time sequence transaction state needs to retain the transaction information amount of the previous time sequence to obtain a transaction update result; controlling the ignored degree of the implicit transaction state of the previous time sequence by using an associated party transaction reset switch to obtain a transaction reset result; obtaining a candidate implicit transaction state of the previous time sequence according to a reset result; and fusing the updating and resetting results and the candidate implicit transaction state to obtain associated transaction feature data. According to the invention, the transaction control switch is arranged to update or reset the transaction time sequence information and determine whether to retain or discard useless transaction information, so that effective associated transaction feature data in the transaction time sequence data can be better captured to adapt to financial transaction information time sequence changes, and the quality and availability of the associated transaction feature data are improved.

This solution belongs to the field of data processing, and specifically relates to a method for tracing the associated transactions of digital assets based on blockchain. This solution includes the following steps: generating a unique hash value for the digital asset as the asset identifier, allocating a unique participant identifier for the participants, and receiving transaction data including the asset identifier, participant identifier, and transaction time; the participant creates a first sub-chain with the local participant identifier, stores the relevant transaction data, and establishes a transaction list to associate the transaction data; the transaction data obtained through consensus is used as the first data, and its participant is the first object. A supervisor is selected from other participants, and the first data is sent to the supervisor and the first object; the first object stores the data in the first sub-chain, and the supervisor stores it in the corresponding sub-chain or creates a new supervision sub-chain according to the sub-chain identifier; during tracing, the first sub-chain and the supervision sub-chain are obtained according to the participant identifier, and if they are consistent, the first sub-chain is output. This solution solves the problem of low efficiency in tracing the associated transactions of digital assets in blockchain.

The invention provides a block chain consensus optimization auditing method based on Beidou time service and a terminal. Comprising the following steps: providing unified time reference data for nodes in a block chain network based on a Beidou satellite time service system; when a transaction request is received, generating a transaction timestamp based on the unified time reference data, and generating a transaction global time sequence sorting result of all transaction requests according to the transaction timestamp; performing parallel verification and processing on all the transaction requests through a time window grouping technology by utilizing the transaction global time sequence sorting result to obtain a transaction parallel verification result; and based on the unified time reference data, the transaction global time sequence sorting result and the transaction parallel verification result, generating tamper-proof time audit log data, and storing the data in the block chain network. According to the method, the unified time reference of the block chain network is realized, the block chain consensus mechanism efficiency is optimized, and the performance, security and expandability of the system are improved.

The application relates to a kind of blockchain-based distributed energy transaction verification method and system, relate to the field of blockchain energy transaction, which includes the transaction parameters and identity parameters of the transaction collected;The collected transaction parameters and identity parameters are checked, and transaction code and identity code are determined;After verification, the matching hash value of the blockchain is determined based on the transaction code and the identity code;The matching hash value is converted into a paired string;When the paired string is added to 0, pairing is completed, transactions are carried out according to the transaction code, and data is uploaded to the blockchain platform, and the transaction is completed after the settlement is completed.The application has the effect of reducing transaction time and improving efficiency.

The application discloses an instant information publishing method, system and device supporting interactive transaction and a storage medium. According to the needs of actual power grid balanced operation in a control area or after the interactive transaction application of market subjects in the control area to a power transaction center is audited and passed, the power transaction center broadcasts interactive transaction information to all market subjects in the control area through a power transaction platform. In the transaction process, the market subjects can dynamically adjust the declared transaction information at any time. At this time, the power transaction center publishes transaction adjustment information to other market subjects in the whole range which have participated in the interactive transaction through the power transaction platform. After the transaction result is published or after the market subjects propose transaction termination application within the specified transaction time and obtain the approval of the power transaction center, the interactive transaction is ended. The application can realize instant publishing of interactive transaction information, meet the stability of transaction time and transaction cycle of the interactive transaction and promote the smooth development of the interactive transaction among the market subjects.

The application provides a transaction log collection method, device and system. The transaction log collection method comprises the following steps: putting log files of each application server into a cache queue; determining a time difference according to a tail transaction time and a head message transaction time; determining a log output time according to the time difference and the head message transaction time; sorting the log files in the cache queue according to transaction times in the log files, and putting the sorted log files into an output message queue according to the log output time. The application can effectively deal with a large number of logs generated in a short time, ensure that the collected logs are complete and orderly, avoid transaction failure, and further improve the playback efficiency and effect of a subsequent follow-up playback device.

The present invention belongs to the technical field of data management, and more specifically relates to a construction and query method of a verifiable hierarchical index based on blockchain temporal data. The present invention first defines the research object, namely the structural model of temporal data in the blockchain. This structural model is an extension of the traditional blockchain transaction structure. Every x transactions form a complete level of index, where x is a non-fixed parameter that can change continuously during the operation of the system. Specifically, this structure adds a transaction time attribute to the original blockchain transaction structure to identify the temporality of the data; adds forward traceability pointers for both parties of the transaction to provide a basis for traceability in queries; adds an index time interval attribute to provide a basis for judgment in range queries; and adds an index serial number attribute and an index-level first transaction pointer attribute to improve query efficiency.

An apparatus comprises at least one processing device configured to detect, utilizing a machine learning model, anomalous transactions in a database system comprising first and second databases, the second database having a configurable disaster recovery delay time period relative to the first database. The at least one processing device is also configured to determine a recovery time point for the database system based on a transaction time of a given anomalous transaction, and to replay, from the first database to the second database, a first set of transactions occurring during the configurable disaster recovery delay time period up to the determined recovery time point, to validate recovery of the first set of transactions, and to replay, from the first database to the second database, a second set of transactions of the database system occurring after the determined recovery time point responsive to validating recovery of the first set of transactions.

The invention discloses a business data processing method, device and equipment, a computer medium and a product, which are applied to the field of data processing, and the method comprises the following steps: obtaining transaction information of a transaction subject; constructing a first product detail table based on the transaction information of the transaction subject; the first product detail table comprises a transaction identifier, a transaction subject identifier, a product number, transaction data, transaction time and a transaction data source; receiving a business registration form input by the user, wherein the business registration form comprises a user identifier, an identifier of a transaction subject, registration time and a registration product; based on the transaction subject identifier, matching the business registration form with the first product detail table to obtain a second product detail table of the user; determining initial service data of the user based on the second product detail list and preset service data configuration parameters; summarizing the initial business data to obtain target business data of the user; and displaying the target business data. According to the method, the business data processing efficiency and accuracy can be improved.

The invention discloses a transaction volume prediction model training method, a transaction volume prediction method, a transaction volume prediction device and transaction volume prediction equipment, relates to the technical field of transaction volume prediction, and can be applied to the field of financial science and technology. According to the business type of each service node, the sample transaction data of each service node at at least one first moment and the sample transaction data of each service node at the second moment, respectively constructing at least one sample transaction business feature of the first moment and a sample transaction business feature of the second moment; constructing a sample transaction time feature sequence through a time sequence feature construction module in the initial model according to the sample transaction service features of the first moments and the second moments; determining a predicted transaction volume through a prediction module of the initial model according to the sample transaction business features and the sample transaction time feature sequence at the second moment; according to the real transaction volume and the predicted transaction volume, the initial model is trained, and accurate prediction of the future transaction volume is realized.

The invention relates to the technical field of transaction platform management, and discloses a technology transaction platform management system based on big data, and the system comprises a data collection module which collects technology transaction big data related to technology transaction and carries out the data preprocessing; the foreground evaluation module is used for analyzing, calculating and evaluating the technology transaction foreground of the technology based on the technology transaction big data, and establishing a dynamic technology foreground evaluation system through real-time data acquisition and processing; the transaction recommendation module is used for carrying out foreground trend classification on technologies by combining historical change data based on a dynamic technical foreground evaluation system, carrying out reasonable prediction on technical potential values, and recommending and optimizing transaction time through the technical potential values; the risk assessment module is used for calculating the transaction risk of the expected transaction technology analysis technology to obtain a transaction risk value, and analyzing and judging the risk type of the transaction technology based on the transaction risk value; and the reasonable evaluation module comprehensively analyzes and judges the rationality of the technology transaction in combination with the technology transaction foreground and the transaction risk value.