112 results about "Transaction time" patented technology

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 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.

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 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.

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 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.

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 embodiment of the application provides a kind of data storage method, device, equipment and storage medium, related to computer technical field, the method comprises: for the transaction processing event of to-be-processed event, corresponding parameter query event is constructed, and transaction information in to-be-processed event is saved in transaction time sequence library with parameter query event.For the parameter processing event of to-be-processed event, corresponding parameter maintenance event is constructed, and parameter maintenance event and exchange rate parameter are associated and saved in parameter time sequence library, so it is not necessary to record corresponding exchange rate information for each transaction, avoid data storage redundancy, improve the utilization of storage resources and the processing performance of system.Parameter time sequence library and transaction time sequence library store and manage data using the same time axis, so that the retrieval time complexity is lower.The storage mechanism of event binding data is used to store the time sequence data of transaction and the time sequence data of exchange rate parameter, so that the recorded time sequence data directly reflects the business processing process.

The invention belongs to the technical field of settlement, and particularly relates to a peer-to-peer power transaction settlement method and system based on an alliance chain, and the method comprises the following steps: S1, obtaining to-be-settled power transaction data at least comprising two transaction parties, the key levels of the two transaction parties, the transaction electric quantity, the transaction price and the transaction time; s2, according to the historical transaction verification reliability and the power grid role identity of each consensus node, calculating to obtain a consensus weight of each consensus node; and calculating the whole-network consensus health degree according to the whole-network distribution of the consensus weight, and taking the whole-network consensus health degree as a regulatory factor. According to the method, false or wrong transactions which do not accord with rules can be filtered out before the data is linked, the authenticity and reliability of the on-chain power transaction data are guaranteed from the source, and the potential operation risk of a power grid is avoided.

The invention relates to an anti-money laundering suspicious transaction monitoring method and device, and relates to the field of data monitoring, and the method comprises the steps: monitoring transaction data of a target account, carrying out the extraction of a transaction amount sequence and a transaction timestamp sequence, and carrying out the calculation of a short-time transaction amount and a short-time transaction frequency; performing anomaly classification on the short-time transaction amount and the short-time transaction frequency to obtain a target amount anomaly coefficient and a target frequency anomaly coefficient; taking associated historical transaction monitoring data of a plurality of associated accounts in the associated account graph, carrying out abnormal classification on short-time transaction frequency, obtaining an associated frequency abnormal coefficient graph, carrying out fitting to obtain an associated frequency abnormal coefficient, combining a target amount abnormal coefficient with a target frequency abnormal coefficient, calculating to obtain a transaction abnormal coefficient, and judging to obtain a monitoring result. The technical problems that in the prior art, money laundering suspicious transaction monitoring is mostly carried out through simple judgment rules, accurate monitoring and recognition are difficult to carry out on multi-level transactions, and the monitoring accuracy is low are solved.

The application provides a blockchain-based network security protection method and system, applied to the technical field of blockchain, which can effectively identify and distinguish the specific transaction confirmation time delay abnormal extension phenomenon caused by hidden computing power manipulation, and distinguish it from the transaction time delay caused by universal network congestion or normal network fluctuation, thereby accurately maintaining the fairness and timeliness of network transactions.

The application relates to a user type determination method and device, computer equipment and a storage medium, and relates to the field of artificial intelligence. The method comprises the following steps: obtaining target transaction data of a to-be-detected user; determining transaction time sequence features corresponding to the target transaction data based on a preset feature extraction algorithm and a data standard format, wherein the transaction time sequence features comprise fixed-length feature data of the to-be-detected user; inputting the transaction time sequence features of the to-be-detected user into a trained user type prediction model to obtain a type prediction result of the user type prediction model, and determining a user type of the to-be-detected user based on the type prediction result. The method can improve the prediction accuracy of a prediction model for predicting abnormal users with abnormal transaction behaviors.

Systems and methods include a database maintained by a financial institution that stores transaction data associated with a previous financial transaction performed via a financial account of a respective account holder, wherein the transaction data comprises a transaction location and a transaction timestamp, a spend memory processor of the financial institution that retrieves the transaction data from the database, interacts with a social linking application programming interface (API) to receive, via a network, social data from a social networking site, wherein the social data comprises a social location and a social timestamp, compares the social data to the transaction data, and creates a spend memory record based on one or more similarities between the social data and the transaction data, and a communication interface of the financial institution that provides the spend memory record to a mobile device associated with the account holder.

The application provides a transaction execution method and device of a blockchain, electronic equipment and a storage medium, and relates to the technical field of blockchains. The method increases the attack cost of an attacker, designs contract content of a smart contract from multiple dimensions such as transaction time and transaction fee and transaction frequency, to alleviate the influence of the attacker on the contract execution result by increasing the transaction fee to make his own transaction preferentially executed; by combining historical transaction information, the transaction request is successfully executed only when the cumulative execution information of the transaction request of the same transaction content meets the set condition of the smart contract, which can avoid the unfair phenomenon of transaction execution caused by the fact that the transaction execution order depends on the transaction fee and the transaction is successfully executed only by one execution, thereby effectively avoiding the influence of the attacker on the transaction execution result caused by maliciously increasing the transaction fee, and improving the transaction experience of the transaction request party.

The invention discloses a transaction closed-loop processing method and system based on double-end collaborative hot update and containerization, and the method comprises the steps: carrying out the encryption storage of a script based on a management end, and pushing the encrypted script to a server; after receiving the script based on the server side, executing decryption and real-time compiling, generating an executable instance and storing the executable instance into a pre-compilation instance pool; the instance is automatically released by the JVM when the reference count returns to zero; the server side is deployed in a Kubernetes cluster in a containerization manner, and the capacity is dynamically expanded and shrunk; the method comprises the following steps of: performing closed-loop processing on a service request by the same server instance: completing multi-step operation through a local database transaction; realizing cross-method state transfer by using JVM memory sharing; the call link depth is constantly a preset value and is directly referenced and executed through a memory. According to the method, the management end pushes the script to the server end, the server end compiles in real time, and Kubernetes containerization capacity expansion and contraction are combined, so that service request closed-loop processing is realized, distributed transaction coordination is eliminated through local transaction and memory sharing, and transaction time consumption is reduced.

The present application belongs to the technical field of data processing, and specifically provides a transaction data anomaly determination method, device, equipment, medium and product, which comprises the following steps: obtaining target transaction data to be determined for an abnormal result; determining a target customer identifier according to the target transaction data; determining customer transaction related data of a target customer within a preset time period before a target transaction time according to the target customer identifier; the target transaction data comprises the target transaction time; inputting the target transaction data and the customer transaction related data into a decision tree algorithm model trained to convergence to output an abnormal result corresponding to the target transaction data. The transaction data anomaly determination method of the present application, based on the decision tree algorithm model trained, combined with the customer transaction related data, can determine whether the target transaction data is abnormal from multiple dimensions of data, thereby improving the accuracy of transaction data anomaly determination.

The invention provides a method and system for processing an API request, a storage medium and electronic equipment, applied to a processing system, the system is in communication connection with a power transaction center and a third-party transaction platform, and the method comprises the following steps: obtaining a transaction time period of the power transaction center and an API request of the third-party transaction platform; performing first processing on the API request, and judging whether the API request is in a transaction time period or not; performing second processing on the API request based on a result of the first processing, and judging whether a unique identifier corresponding to the API request exists in all stored unique identifiers or not; performing third processing on the API request based on a result of the second processing, and judging whether the API request in the current time window exceeds a calling frequency limit specified by a power transaction center; and returning a processing result of the API request to the third-party transaction platform according to results of the first processing, the second processing and the third processing. According to the method, efficient and compliant calling of the API request is guaranteed by monitoring the calling time and frequency of the API request and system storage data.

The invention provides an index aperture conflict identification method and system based on AI training and reasoning cooperative scheduling, and the method comprises the steps: obtaining a transaction initiation timestamp of a multi-warehouse allocation record from a database of an inventory management system, and recognizing in-transit commodities, and a warehouse-out confirmation moment and a target warehouse-in registration moment corresponding to the in-transit commodities, sorting the warehouse-out confirmation time and the target warehouse-in registration time to obtain an ordered transaction time list; according to the caliber conflict type, identifying an in-transit commodity attribution state in each warehouse allocation link, the in-transit commodity attribution state including a warehouse-out and non-warehouse-in state and a state that downstream warehouse allocation depends on upstream warehouse inventory; and performing real-time comparison according to a real stock time sequence and a time sequence accumulated stock value, evaluating whether the deviation exceeds a preset aperture conflict identification range, triggering an aperture conflict identification alarm, and recording a conflict type, a distribution range of potential aperture conflicts and a related transaction initiation timestamp into a management system log.

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 discloses an asset transaction risk monitoring system and method, and relates to the field of asset transaction security. The method comprises the following steps: acquiring transaction object types, transaction time, transaction amount and flow funds through a plurality of transaction objects of a user; calculating a transaction frequency index according to the transaction time of different transaction objects; collecting multiple groups of asset risk training data through historical transaction data of multiple users; each group of asset risk training data comprises the transaction object type, the transaction frequency index and the transaction total amount ratio of a plurality of historical transaction objects of each user, and a risk mark indicating whether each transaction object has a flow fund risk, and training to obtain an asset risk prediction model.