254 results about "Transaction time" patented technology

The invention discloses a transaction flow account checking method, a transaction flow account checking system, transaction flow account checking equipment and a storage medium. The method comprises the following steps: docking with each transaction channel through an API (Application Program Interface) to obtain transaction data in real time; correspondingly storing the transaction data of each transaction channel in different strokes by taking the transaction time as a sequence; performing account checking processing on each piece of transaction data of each transaction channel, and obtaining an account checking result of each piece of transaction data of each transaction channel; the account checking processing comprises the steps of extracting key information for each piece of transaction data of each transaction channel, and comparing the key information of each piece of transaction data of every two transaction channels for each piece of transaction data of each transaction channel and each piece of transaction data of other transaction channels; and for each transaction data of each transaction channel which is judged to be abnormal, performing abnormity verification according to a preset transaction business rule. According to the invention, timely, efficient and accurate transaction flow account checking can be realized.

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.

Disclosed herein are methods and systems for using machine learning to improve the likelihood of success of recurring transactions. In one example, a suite of different machine learning models can be used together, such that a first machine learning model predicts a likelihood of success for a recurring transaction associated with a user account and the second machine learning model predicts whether a pre-authorization would help with the predicted likelihood of success. As a result, a server may pre-authorize the recurring transactions at a time earlier than the scheduled transaction time and place a hold on the user account using an amount predicted by the second machine learning model where the hold amount can be adjusted in accordance with the user account's activities. Data associated with the recurring transaction itself can be ingested by the second machine learning model for re-calibration purposes.

The invention relates to the technical field of product recommendation, in particular to an intelligent product recommendation method based on a collaborative filtering algorithm. According to the invention, the popular style of each product is obtained according to the product sales volume change trend of each product at different transaction moments; obtaining the conformity of each user in combination with the transaction time characteristics of the corresponding historically purchased products; for any user as a target user, according to the similarity between each emerging product and different historically purchased products, obtaining the product similarity between the emerging products and the historically purchased products, and screening emerging recommended products; according to the transaction times and transaction time distribution of different historically purchased products, obtaining transaction performance of the user corresponding to each historically purchased product; the interest tendency of the target user corresponding to the historical purchased product is obtained; and obtaining a recommendation index of the target user corresponding to the emerging recommended product. According to the method, the accurate recommendation index of each emerging product to the user is obtained, and the product recommendation accuracy is improved.

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 embodiments of the present application relate to the technical field of computers. Provided are a data storage method and apparatus, and a device and a storage medium. The method comprises: if the event to be processed is a transaction processing event, constructing a corresponding parameter query event, and storing, in a transaction time-sequence library, transaction information in the parameter query event and transaction information in the event to be processed; and if the event to be processed is a parameter processing event, constructing a corresponding parameter maintenance event, and storing the parameter maintenance event and an exchange rate parameter in a parameter time-sequence library in an associated manner. Therefore, it is not necessary to record corresponding exchange rate information for each transaction, thereby avoiding data storage redundancy, and also improving the utilization rate of storage resources and the processing performance of a system. The parameter time-sequence library and the transaction time-sequence library use the same time axis to store and manage data, such that the retrieval time complexity is relatively low. Time sequence data of a transaction and time sequence data of an exchange rate parameter are stored by means of a storage mechanism in which an event is bound to data, such that recorded time sequence data directly reflects a service processing process.

The invention discloses a fraudulent transaction risk monitoring method and device based on a graph neural network, equipment and a medium, and relates to the field of anti-fraudulent, and the method comprises the steps: determining each transaction node and each neighbor entity in a dynamic transaction graph, linking each transaction node with each neighbor entity, and constructing a target static graph; creating new transaction nodes and new neighbor entities respectively, and constructing a snapshot graph according to the new transaction nodes, the transaction time corresponding to the new transaction nodes, the new neighbor entities and the reference transaction nodes; determining a first target directed graph based on the target static graph and the snapshot graph, and training the target graph neural network by using the first target directed graph to obtain a trained neural network; and when a new transaction occurs, determining a second target directed graph of the new transaction through transaction information of the new transaction, determining a risk score of the new transaction by using the trained neural network and the second target directed graph, and determining a fraud risk of the new transaction based on the risk score. Therefore, accurate fraud monitoring is realized.

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 relates to the technical field of cashier data monitoring, and particularly discloses a cashier data monitoring management system based on data analysis, and the system specifically comprises a cashier data module which obtains a cashier transaction time value, carries out the judgment, and obtains an abnormal transaction. The method comprises the following steps: acquiring a cashier transaction record time value, judging whether the transaction record time value is in a normal business time period or not based on the normal business time period of a merchant, and if not, marking the transaction as an abnormal transaction, so that the abnormal transaction in the cashier record can be acquired in time; calculating an abnormal time characterization value and an abnormal amount characterization value of the abnormal transaction, substituting the abnormal time characterization value and the abnormal amount characterization value into a formula to calculate a risk judgment value, judging the risk degree of the abnormal transaction in each analysis period in the non-business time, generating a low-risk signal or a high-risk signal, and sending the low-risk signal or the high-risk signal to the server. Therefore, timely response can be carried out based on the abnormal transaction risk degree, and potential risks caused by abnormal transactions are reduced.

The invention provides a database multi-transaction processing method and device, equipment and a storage medium, and can be applied to the technical field of databases. The method comprises the following steps: in response to at least one write transaction request and read transaction request for a database, obtaining transaction time sequence identifiers respectively corresponding to the write transaction request and the read transaction request; executing at least one write transaction request for a target key of the database to obtain at least one key value and an operation attribute corresponding to the at least one key value; according to the transaction time sequence identifier corresponding to the ith write transaction request, index query ranges corresponding to the (i-1) th key value and the ith key value are determined, the ith key value is obtained based on the ith write transaction request operation, and i is larger than 1 and is a positive integer; and executing the read transaction request bound with the index key value, and determining a target query result according to the transaction time sequence identifier corresponding to the read transaction request and the respective index query range and operation attribute of the at least one key value.

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.

The invention relates to the technical field of electronic signature evidence storage, in particular to an electronic signature whole process data evidence storage method based on a block chain, and the method comprises the steps: collecting all historical transaction behavior data, including transaction users, transaction time and text data of signed files, of each user before each transaction in the block chain; determining the theme difference degree of the signed files of any two transactions; determining a transaction behavior abnormal coefficient of each user during each transaction; acquiring the malicious user confidence of each user during each transaction; and for each transaction, based on the number of transactions between each user and each to-be-signed user, in combination with the malicious user confidence of each to-be-signed user, obtaining a selectable coefficient of each to-be-signed user during each transaction of each user, screening the to-be-signed users, and performing electronic signature by using an encrypted signature algorithm. According to the application, the anonymity of the identity of the signer is ensured, and the privacy security of both transaction parties is 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 discloses a face recognition attack interception method and device, equipment and a storage medium, and relates to the technical field of information security. The method comprises the following steps: acquiring transaction data and biopsy face images of N users in a first time window, wherein the transaction data comprises at least one of the following items: transaction time and a transaction place; based on the transaction data, biopsy face images of N users are divided, at least one image cluster is obtained, the image cluster comprises biopsy face images of at least two users with similar background areas, and the background areas are non-face areas in the biopsy face images; and determining interception data of the risk user corresponding to the biopsy face image in the image cluster, wherein the interception data is used for indicating interception of a face recognition attack related to the risk user in the second time window. In this way, potential or missing face recognition attacks are avoided, and the accuracy of intercepting the face recognition attacks is improved.

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.

The invention relates to the technical field of account bookkeeping, in particular to a bank system online account concurrent transaction implementation method based on summary bookkeeping. The method comprises the following steps: judging whether a current transaction relates to a hotspot account or not; if it is determined that the current transaction relates to the hotspot account, a definite transaction detail registration book is created, and a flag bit is set; creating a plurality of shadow accounts corresponding to the hotspot account; after the existing shadow account is inquired, bookkeeping is carried out on the shadow account, and the transaction amount, the loan direction and the account balance of the shadow account are synchronously updated; and setting a timed automatic task, and carrying out accounting processing on a non-posted hotspot account transaction every hour, the accounting processing comprising: locking a non-posted record, then calculating a total amount, posting in a hotspot account, reducing the amount of a shadow account, setting a flag bit of the non-posted record to be posted and unlocking. According to the method, the transaction time of the hotspot account can be effectively reduced, so that the resource utilization rate of a financial transaction system is improved.

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 transaction data processing, in particular to a transnational energy transaction risk assessment method and system, and the method comprises the steps: obtaining transaction volume data, transaction price data and transaction order response time data under a plurality of transaction times; comprehensively analyzing the sensitive condition of the data affected by the outside world based on the change condition of the transaction volume data, the transaction price data and the transaction order response time data under each transaction frequency, and obtaining the interference degree under each transaction frequency; the fluctuation stability condition of the overall interference degree of the transaction times is analyzed, and the transaction data safety credibility under each transaction time is obtained; according to the security credibility of the transaction data, grading all transaction times to obtain a plurality of security level categories; and performing security assessment according to the security level category. According to the invention, the interference of external interference on transaction data security judgment is reduced, and the accuracy of data security risk assessment is improved.

The invention belongs to the technical field of power transaction, and discloses a channel ATC locking method and system supporting inter-provincial medium and long term high-frequency transactions, and the method comprises the steps: obtaining the transaction time of each transaction in a power transaction network, and setting ATC locking identifiers of a power transmission channel at the transaction starting time and the transaction ending time according to the transaction time; reading an ATC locking identifier of the power transmission channel, and judging whether the locking state of the power transmission channel involved in the transaction is a non-locking state or not; and calculating the residual ATC of the channel for the transaction in the non-locking state, and unlocking the transaction channel. According to the method, different transactions relate to different power transmission channels, and related power transmission channels are locked as required instead of all channels, so that efficient operation and organization of the transactions are facilitated; a channel ATC locking identifier design and a library table structure are provided, a locking identifier is bound with a power transmission channel ID and transaction time, the range of a locked channel is minimized as much as possible, and high-frequency organization of interprovincial medium and long term transactions is also facilitated.

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.

To suitably provide information as an electronic receipt, even if it is transaction information obtained by using a non-networked store terminal, and even after a transaction time point with respect to a consumer.MEANS FOR SOLVING THE PROBLEM: An electronic receipt information management method makes a computer execute: a transaction information acquisition step which acquires transaction information which is information about commercial transactions together with a print output request; a character information conversion step which converts a content of the acquired transaction information into character information by a predetermined unit; a coding processing step which generates HTTP request data using a predetermined code based on the character information; a code display step which displays and outputs the code according to the request; and an electronic receipt information generation step which generates electronic receipt information, according to a reception state of a read result of the displayed code.SELECTED DRAWING: Figure 2

The invention relates to a data output method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring at least one piece of transaction data from a distributed database node; the transaction data comprises node transactions, transaction time and transaction identifiers; for each piece of transaction data, generating a key value pair corresponding to the transaction data according to a node transaction, transaction time and a transaction identifier in the transaction data; the key value pair comprises a target key and a target value; and for each key-value pair, generating and outputting a transactional structured query language (SQL) statement of the key-value pair according to the key-value pair. By adopting the method, the parallel capability of the distributed database can be effectively utilized, and the high efficiency and consistency of data operation are ensured, so that potential errors are reduced, and the accuracy of transaction data processing is improved.