92 results about "Sequence pattern" patented technology

The invention provides a data flow monitoring method and system based on a large model, and the method comprises the steps: obtaining a data flow record set generated by a to-be-monitored system in a continuous operation period, carrying out the correlation path construction of the data flow record set, generating a data flow topological graph containing a node interaction relation and a time sequence dependency relation, and carrying out the correlation path construction of the data flow record set; calling a pre-trained circulation behavior analysis large model to perform node sequence pattern recognition on the data circulation topological graph, and generating behavior abnormal confidence and abnormal pattern labels of each node in the data circulation topological graph; and according to the abnormal behavior confidence and the abnormal mode label, screening an abnormal interaction node cluster in the data flow topological graph. According to the method, relevance between abnormal nodes and time sequence relevance are considered, missing detection or false detection is avoided, and the reliability of the monitoring effect is improved.

The invention discloses a low-altitude security event automatic classification and response method based on a cloud computing platform, and the method comprises the following steps: S1, collecting monitoring data in a low-altitude region, and carrying out the preprocessing of the monitoring data; s2, constructing a heterogeneous airspace behavior map, and extracting an evolution path and a behavior sequence pattern between nodes; s3, inputting the feature embedding matrix into a double-branch classification network, and respectively executing event type identification and event level evaluation; s4, constructing an event index, and designing a multi-objective strategy function; s5, the response instruction set is issued to the multi-source processing terminal through the cloud computing platform, and sealing control, interference, tracking or alarm operation is executed; and S6, performing incremental training on the graph convolutional neural network and the double-branch classification network, continuously updating network parameters and optimizing a multi-objective strategy function. According to the invention, high-precision identification and rapid intelligent response of low-altitude security events are realized, and the automation level and emergency disposal efficiency of airspace management and control are remarkably improved.

The invention discloses a commodity recommendation method and system based on target sequence pattern mining, and belongs to the technical field of data mining. According to the method, a user shopping behavior sequence is coded to obtain a coded shopping sequence, and sequence candidate modes with the length of 2m + 1 are mined from the coded shopping sequence; deleting the modes which do not contain the target query sequence, taking the remaining modes as target sequence modes, and calculating the support degree of the target sequence modes; in the process of mining a sequence candidate mode with the length of 2m + 1 for a coding shopping sequence, when a target query sequence is a negative target query sequence, matching between a current prefix sequence and a precursor positive partner of a negative item and between the current prefix sequence and a subsequent positive partner of the negative item is realized under the guidance of the negative target query sequence. The method is short in time consumption for mining the target sequence pattern.

The invention discloses a chronic obstructive pulmonary disease patient screening system for the respiratory medicine department, particularly relates to the field of computer-aided diagnosis, and is used for solving the technical problems that an existing screening method is low in screening efficiency, low in patient adaptability and difficult to popularize on a large scale in a basic level. According to the system, a standardized patient medical event sequence is constructed by obtaining a target population medical record containing medicine distribution and disease diagnosis codes; identifying a characteristic medication and diagnosis mode before definite diagnosis of the chronic obstructive pulmonary disease based on a sequence pattern mining technology; constructing a medical knowledge graph by using the modes, obtaining a feature embedding vector representing a chronic obstructive pulmonary disease medical trajectory through graph neural network learning, and establishing a high-risk digital portrait; inputting the medical sequence of the person to be screened into a feature extraction model based on same map training, and generating an individualized risk probability prediction value by calculating the matching degree of the medical sequence and the digital portrait; and a screening result report is automatically generated, so that efficient and noninvasive early risk screening is realized.

The invention discloses a time sequence prediction method and system based on large language model semantic prompt enhancement. The adopted model comprises a semantic prompt enhancement module, a pre-training large language model and a self-adaptive mapping network; the semantic prompt enhancement module maps a vocabulary of the large language model into a sparse subset, and encodes the sparse subset of the vocabulary into a key matrix and a value matrix; embedding the time sequence into a vector code to form a query matrix, calculating the sparsity similarity between the query vector and a key matrix, and selecting the query vector to form a sparse query matrix; calculating sparse attention, and splicing the sparse attention with the time sequence embedded vector to obtain an enhanced time sequence embedded vector; the pre-trained large language model is used for embedding a vector according to the enhanced time sequence to obtain a time sequence preliminary prediction representation; and the adaptive mapping network adopts a B-spline function to carry out nonlinear mapping on the preliminary prediction representation of the time sequence. The modeling capability of the model on long-term dependence and complex sequence modes is enhanced, and the prediction precision is improved.

The invention belongs to the technical field of knowledge maps, and discloses a next interest point recommendation method based on a cross-regional city space knowledge map. According to the method, on the basis of the geographic space knowledge graph and the user preference knowledge graph, a cross-regional relationship modeling mechanism is introduced, the spatial proximity and the reachability between the regions are comprehensively considered, and more comprehensive description of the cross-regional behavior of the user is realized. According to the method, a recommendation framework combining a geographic module and a sequence module is designed, spatial dependence between interest points can be captured, a high-order sequence mode in user sign-in behaviors can be mined, and recommendation accuracy and diversity are improved. Geographic representation and sequence representation are fused through a consistency learning framework, the robustness and generalization ability of the model can be enhanced, and the model can still keep stable performance in sparse data and cold start scenes. According to the method, the defects of insufficient region boundary perception, recommendation result centralization, poor cross-region prediction adaptability and the like of an existing method are overcome.

The invention is suitable for the technical field of computers, and provides a game churn user recall method, which comprises the following steps: analyzing a behavior sequence mode before user churn, and determining a churn type of a churn user in combination with game event data before user churn; based on the churn type, making a personalized touch strategy for the churn user for sending periodic touch content to the churn user; whether the lost user triggers the in-game backflow activity or not is judged, a personalized backflow gift bag is configured for the backflow user, the recall efficiency and effect of the lost user can be improved, and powerful technical support is provided for long-line operation of the game.

The invention belongs to the technical field of Internet of Things. The invention provides a dynamic graph neural network link prediction method fusing multi-modal motifs. According to the embodiment of the invention, multi-source heterogeneous information such as structures, semantics, behaviors and the like is adaptively fused through a node-level modal attention mechanism, the problem that single-modal information is insufficient or unreliable is solved, and the discrimination ability of node representation is enhanced. And first-order adjacency, high-order adjacency and multiple motif modes are systematically fused, so that the model can simultaneously describe a local microcosmic mode and a global macroscopic topology, and the understanding of a complex network structure is more comprehensive. Spatio-temporal joint modeling is coordinated with time sequence attention through graph convolution, short-term mutation and long-term trend of network evolution are captured at the same time, and a dynamic link rule is accurately described. Through modal attention redistribution, time sequence consistency constraint and entropy regularization, modal missing and noise interference are effectively resisted, and prediction fluctuation is reduced.

The invention discloses a commodity real-time recommendation method and system capable of efficiently mining by using a repeated negative sequence pattern, and relates to the technical field of data mining. According to the method, a prefix tree structure is designed and used for storing and maintaining an efficient repeated positive sequence mode, and efficient and dynamic management of data is achieved; the invention further provides a new efficient repeated negative sequence candidate mode utility value calculation mode, specifically, for any efficient repeated negative sequence candidate mode, the utility value of the efficient repeated positive sequence mode of the efficient repeated positive sequence mode matched with the efficient repeated negative sequence candidate mode is accumulated, and the effective value of the efficient repeated negative sequence candidate mode is calculated. And obtaining the utility value of the efficient repeated negative sequence candidate mode. According to the method, the accuracy of efficient repeated negative sequence pattern mining can be effectively improved, and commodities recommended by an application layer based on the more accurate efficient repeated negative sequence pattern obtained through mining can better meet the requirements of users.

The invention provides a vehicle fault early warning method, electronic equipment and a storage medium, and relates to the technical field of vehicle fault diagnosis and prediction. The method comprises the following steps: acquiring multi-source time sequence historical data in a vehicle running process within a first preset time period; discretizing the multi-source time sequence historical data based on the first preset time period to obtain a historical event sequence corresponding to each first preset time period; processing the historical event sequence by adopting a preset sequence pattern mining algorithm to obtain an event sequence pattern; determining a corresponding prediction rule based on the event sequence mode; performing monitoring matching on the multi-source time sequence real-time data and the prediction rule in the vehicle running process; and when the prediction rule is successfully matched with the multi-source time sequence real-time data, triggering target fault early warning of the vehicle. Through multi-source time sequence data mining and real-time matching, the limitation of isolated recording DTC is broken, early warning is triggered when matching succeeds, the passive situation of recording after a fault is thoroughly reversed, and beforehand early warning is achieved.

The invention relates to the technical field of intelligent water transportation information, in particular to an intelligent water transportation standard system construction method based on a four-dimensional level model. The method comprises the following steps: learning decision weights of different development value orientations according to idea features and execution features of a case, and determining a target development value of an actual water transportation service at a value dimension level; a spatial semantic tree is constructed by adopting a semantic layering mode, and traversal of the tree is driven by a target development value, so that matching of key element-scene requirements under a spatial dimension is realized. The method comprises the following steps of: partitioning a whole-cycle flow of a service, and performing time-dimension sequence pattern analysis on standard events at each stage to obtain corresponding time sequence construction requirements; and carrying out analysis and decision-making on operation paths among different elements from a logic dimension level to form a target implementation path and a support strategy. According to the method, a full-factor, full-period and full-scene intelligent water transportation standardization system can be constructed, and reliable multi-dimensional standard guidance is effectively provided for water transportation engineering construction.

The invention provides a group target trend prediction method and device based on sequence pattern mining, relates to the technical field of remote sensing target intelligent interpretation, and aims to solve the technical problems of single data source, weak multi-source data relevance, difficulty in accurately predicting cluster target trend and the like in the prior art. The method comprises the following steps: preprocessing multi-source data of a target group, and extracting time sequence data related to a navigation track and a parking area of the target group; according to the time sequence data, extracting a target group trajectory sequence pattern with the support degree higher than a support degree threshold value by using a sequence pattern mining model; based on the time-space correlation characteristics of each target trajectory in the target group trajectory sequence mode, a time sequence event atlas is generated, and the time sequence event atlas represents the behavior evolution path and state conversion relation of the group target at different time points and space points; and predicting the behavior trend of the group target by using a pre-trained long-short-term memory network model according to the time sequence event graph.

The invention provides a security protection method, system and device based on an API identity portrait and a medium, and belongs to the technical field of network security. The method comprises the steps of obtaining static attributes of a target API, including a data operation type, a transaction integrity requirement and a sensitive field, and determining a basic risk score B according to a preset coefficient; historical request logs are collected and analyzed, traffic baselines at different time points are determined, and the current traffic and the baselines are compared in real time to obtain behavior risk scores A; the method comprises the following steps: acquiring a call log, mining an API co-occurrence set by using FP-Growth, constructing a high-frequency sequence pattern through a PrefixSpan algorithm, and comparing a current session request to obtain a relation risk score N; summarizing attack frequencies and types in a preset time period, and obtaining an attack risk score R in combination with a historical statistical value; a total risk score W is determined based on the above scores, and whether to trigger detection is determined with reference to a preset threshold.

The application provides an object association recognition method and device and a storage medium, relates to the field of artificial intelligence, and the method comprises the following steps: obtaining target attribute features and target operation sequence data of a to-be-recognized object group; performing sequence pattern matching on the target operation sequence data based on a preset sequence pattern library to obtain a target operation sequence pattern of the to-be-recognized object group; performing sequence correlation processing on target operation features corresponding to the target operation sequence data by using a sequence correlation mining network of a preset relationship recognition model to obtain target sequence correlation features of the to-be-recognized object group; performing association relationship recognition on the to-be-recognized object group based on the target attribute features, the target operation features, the target operation sequence pattern and the target sequence correlation features by using an object group classification network of the preset relationship recognition model to obtain an association relationship category of the to-be-recognized object group. The application can effectively improve the generalization ability and timeliness of object association recognition.

The embodiment of the invention discloses an AI failure mode dynamic diagnosis method and system based on a sequence mode and function data analysis, which realize the crossing of AI interaction failure from static type identification to dynamic process diagnosis and improve the depth and timeliness of failure analysis. Accurate and explainable data support is provided for optimization of an artificial intelligence system. Through a sequence pattern mining and function data analysis technology, a failure pattern generated in an interaction process of a user and an artificial intelligence model (such as a large language model, a generative AI or an AI intelligent agent) is identified, analyzed and dynamically tracked, and the influence of the failure pattern is analyzed.

The invention discloses a water conservancy construction risk assessment method based on big data analysis, relates to the technical field of water conservancy construction risk assessment, and is used for solving the problem of inaccurate risk identification early warning. According to the method, a construction task unit is taken as an object, time alignment and space mapping are carried out on monitoring, environment, progress, equipment, safety and other multi-source data, risk state vectors organized according to tasks and time windows are constructed, and a risk scene library is formed through clustering and sequence pattern mining; based on the risk scene type, the current risk state and the disposal strategy combination, a disposal strategy effect prediction model is trained, risk falling, construction period influence and resource occupation are jointly predicted, a state vector is circularly generated in construction operation, the risk scene is identified, and different disposal strategy effects are evaluated. And meanwhile, a risk scene library and a prediction model are updated by utilizing disposal result feedback, so that fine identification and dynamic management and control of complex coupling risks are realized, and the reliability of water conservancy construction risk early warning and scheduling decision making is improved.

The application discloses a learning strategy evaluation and guidance method based on experimental operation sequence pattern mining. The method collects original operation logs and reconstructs them into normalized operation event sequences; extracts the silent interval features between adjacent operations, analyzes the cognitive state labels corresponding to the silent interval in combination with the operation context, embeds them into the sequence to generate semantic enhanced operation sequences; then identifies the repeated operation fragments in the sequence, calculates the exploration intention features to distinguish the scientific exploration intention from the random trial intention, extracts the strategy feature vector of the student in combination with the typical strategy mode library; further locates the key decision branch point, extracts the decision micro-behavior features to calculate the decision hesitation index, and evaluates the strategy efficiency; and plans the gradual transition path from the current strategy to the target strategy in combination with the real-time cognitive load level, and generates personalized guidance suggestions. The application can realize deep quantitative analysis and accurate guidance on the cognitive state, operation intention and decision psychology of students.

The application discloses a kind of real-time root cause analysis methods based on operation and maintenance knowledge graph, by constructing operation and maintenance knowledge graph ontology, the extraction of the content such as entity, relationship of concept is completed using natural language processing, machine learning technology, and the basic framework of knowledge graph is built;Device knowledge graph is constructed, and a renewable, maintainable device knowledge graph is established based on knowledge and data using the topology structure of digital device, the calling relationship between system applications;Construct fault knowledge graph, provide backup support for subsequent fault root cause analysis;Real-time fault convergence and root cause analysis, according to the corresponding category and the sequence mode indicated by alarm information, obtain causal relationship, and obtain root cause path;The application provides a kind of thought for real-time fault root cause positioning analysis of digital basic operation and maintenance facilities, and has the characteristics of strong expansibility, high availability of root cause positioning result.

This invention provides a method and apparatus for identifying IoT devices, belonging to the field of IoT device identification technology. The method includes: extracting abnormal features and length features of IoT device data packets, and constructing multivariate temporal features based on the abnormal features and length features; inputting the multivariate temporal features into a fully trained device identification model to obtain the identification result of the IoT device; wherein the device identification model includes a first Inception module, a first residual structure, a second Inception module, a second residual structure, a global average pooling layer, and a fully connected layer. This invention improves the accuracy of IoT device identification by using abnormal features that characterize the impact of network environment and encrypted traffic on the traffic length sequence pattern of IoT device data packets as input to the device identification model.

The application relates to the technical field of computer networks and discloses a TCP service behavior portrait method and system based on network flow metadata, which comprises the following steps: decoding captured TCP network flow and extracting distributed correlation identifiers to form application layer interaction records; converting the interaction records into unified standardized operation logs; organizing the logs into ordered behavior chains based on time windows; further analyzing the logs and the behavior chains, quantifying atomic behaviors by aggregation, and discovering frequent service process modes by sequence pattern mining; attributing the analysis results to specific service entities to form entity portraits containing service function portraits and service process atlases; and finally constructing a global distributed service atlas representing the calling relationship between services by using the distributed correlation identifiers. The dependence strength between services is accurately quantified, and data decision-making basis is provided for system operation and maintenance and architecture optimization.

The application relates to a method and system for mining and analyzing the correlation of clinical comorbidity of discharged patients. The method comprises the following steps: constructing an individual diagnosis and treatment narrative graph for each patient according to the discharge medical record text, generating a time sequence transaction sequence corresponding to each patient, and constructing a sequence database; based on the database, the original time sequence frequent pattern set is constructed by analyzing through an improved generalized sequence pattern algorithm; each pattern in the set is classified to obtain multiple classification clusters, and the original time sequence frequent pattern in each classification cluster is processed through multi-sequence alignment to construct a generalized clinical path graph, and the information in the generalized clinical path graph is extracted to generate a natural language abstract. The method improves the time sequence logic, knowledge abstraction degree and clinical interpretability of the clinical comorbidity correlation mining by constructing a diagnosis and treatment narrative graph, mining a time sequence frequent pattern, and constructing a generalized clinical path graph through clustering and multi-sequence alignment.

The invention discloses a link log sampling method and device, equipment and a medium. The method comprises the steps of obtaining a target log set, wherein a target log comprises at least one burying point and original business data; processing each target log according to each burying point in the target log to obtain a target sequence set, the target sequence set comprising at least one target sequence and a sequence type matched with the target sequence; processing each target sequence in the target sequence set based on a pre-trained sequence pattern mining algorithm to obtain at least one service sequence type; and obtaining a sampling log set, and performing screening operation on each sampling log in the sampling log set according to each service sequence type to obtain a result log set. Through the technical scheme of the invention, sampling of the link logs can be realized, the efficiency of log acquisition work is improved, and the comprehensiveness, accuracy and reliability of an acquisition result are improved at the same time.

This invention discloses a network fault alarm analysis method, apparatus, device, and storage medium. The method includes: when several alarm events are generated within several observation periods, extracting key fields of the alarm events and sorting them according to time series to obtain two-dimensional matrix data; extracting alarm data from the two-dimensional matrix data based on a sliding window determined by a sliding window adjustment strategy, and extracting spatiotemporal features from the extracted alarm transaction data to train a first fault type identification model; determining a first fault type through the updated first fault type identification model; using a sequence pattern mining algorithm to obtain several frequent sequences for each observation period and identifying a second fault type; when the first fault type matches any second fault type, determining the faulty device and its corresponding physical location based on the corresponding frequent sequence. This invention can cope with dynamic and ever-changing network attack behaviors and achieve accurate location of fault physical locations.

The invention discloses a firewall communication log anomaly detection method based on association rule and sequence pattern fusion, which comprises the following steps: 1, acquiring and cleaning formatted firewall historical logs, and extracting a source IP, a destination IP, a port, a protocol and an action field; 2, mining a static association rule by adopting an Apriori algorithm; 3, mining a sequential communication mode by adopting an improved PrefixSpan algorithm, and fusing the sequential communication mode with a static rule to construct a comprehensive behavior model; and 4, matching a real-time communication log with the model, and if the real-time communication log meets the front part but does not meet the back part or deviates from the sequence mode, judging that the real-time communication log is abnormal and triggering alarm and visual display. According to the method, the normal communication behavior model can be constructed by using the existing firewall log on the premise that the existing production network architecture is not changed, so that the abnormal communication can be quickly detected and alarmed, and the safety and reliability of the production network can be improved.

The invention relates to the technical field of robot process automation, and discloses an RPA node automatic recommendation method based on a process development history. The method comprises the following steps: firstly, collecting historical process development data, and extracting node features and process context information from the historical process development data so as to construct a node recommendation basic data set; based on the basic data set, training to obtain a mixed recommendation model comprising a collaborative filtering module and a sequence pattern mining module; applying the trained mixed recommendation model to a current process development scene, performing score prediction on candidate nodes, and generating an initial node recommendation list; and dynamically optimizing the initial node recommendation list according to the recommendation target to obtain a final RPA node recommendation scheme. According to the method, by means of combination of historical development data and the hybrid model, automatic recommendation of the RPA nodes is achieved, and the recommendation result better fits a specific development scene.

This invention proposes a method for visualizing criminal behavior sequences in drug-related cases. First, pre-trained word vectors are generated using the case details of the drug-related case. A tree structure of behavioral words is constructed based on the similarity of word vectors at sequence nodes. Then, nodes in the criminal behavior sequence are merged according to the tree structure of the behavioral words. Sequence patterns are mined, and all sequences are clustered based on these patterns. The sequences are represented as sequence patterns and sequence complements. Finally, the focal points in the sequence patterns are extracted for focused visualization. This invention provides assistance for case analysis in the judicial field, offers reference for judicial personnel's decision-making, and promotes the development of visualization analysis in the judicial field.

The invention belongs to the technical field of financial risk control, and particularly relates to a credit user intelligent identification method and system fusing multi-dimensional dynamic behavior characteristics, and the method comprises the steps: firstly integrating multi-source heterogeneous data, and generating a loan application main body characteristic view through preprocessing and correlation analysis; through a dynamic time sequence model, dynamic risk features are extracted based on a time decay function, time window statistics and sequence pattern mining, and the dynamic risk features and the static features are spliced into a high-dimensional vector; outputting a risk prediction value and a quantitative score by using double modules of an improved GBDT model and an expert rule score card; and finally, determining a final risk level through consistency check and a weight voting mechanism. According to the method, the problem that single-dimension or static data analysis has relatively large limitation in the existing credit risk control technology can be solved.

The invention discloses an infrared alarm system false alarm suppression system based on a big data mining algorithm, and the system achieves the effective suppression of a false alarm signal through the structural processing and correlation analysis of an alarm event generated by infrared detection. Infrared trigger signals are collected, a time-ordered alarm event sequence is formed, thermal disturbance identification, signal fluctuation suppression and credibility labeling are carried out on events, and a weighted event sequence is obtained; on the basis, performing sequence pattern mining on the weighted event sequence, extracting an event combination relationship with statistical significance, further guiding a rule generation process, and constructing a stable event structure rule; and performing validity evaluation on the generated rules, performing screening to form a reliable rule set, and outputting an alarm triggering, rechecking or suppression control signal in combination with a matching result of a current alarm event, a mode and a rule so as to realize intelligent judgment and control of infrared alarm false alarm.

An authentication device comprising: a storage unit for storing shared information including password and shared rule that are preset for each user; a random number sequence generation unit for generating a random number sequence pattern; a calculation unit for generating a key code based on the shared rules, using provisional numerals selected from the random number sequence pattern and secret numerals constituting passwords; a transceiver for receiving identification information of a user to be authenticated from an input terminal, transmitting the random number sequence pattern in association with the identification information to the input terminal, and receiving, from the input terminal, a key code consisting of the numerals selected based on the shared rules from the random number sequence pattern by the user to be authenticated; and an authentication unit for authenticating the user to be authenticated, using the key code received and the key code generated.

The invention discloses a method and a system for constructing a multi-channel interactive behavior sequence pattern knowledge graph. The method comprises the following steps: 1) preprocessing a multi-channel interaction log to obtain a generalization representation of each log record in the multi-channel interaction log; 2) mining a multi-channel interaction behavior sequence pattern with gap constraint from the multi-channel interaction log; 3) aiming at a multi-channel interaction behavior sequence mode, calculating a standardization support degree and a user forward uniqueness; and 4) taking the multi-channel interaction behavior category as an entity, constructing a quintuple set based on the multi-channel interaction behavior sequence pattern, and constructing a multi-channel interaction behavior sequence pattern knowledge graph for all users and a multi-channel interaction behavior sequence pattern knowledge graph for a target user. According to the method, structured knowledge representation covering general rules of all users and personalized preferences of specific users can be formed, and support is provided for a user experience optimization strategy in a multi-channel interaction scene.