506 results about "Event sequence" patented technology

The invention discloses a cloud mobile phone end-to-end performance tracking method and related equipment, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining client touch event data and network event data, and generating a global unique identifier based on a preset Hash algorithm; obtaining server resource event data; performing timestamp calibration on the client touch event data and the server resource event data based on a hardware-level clock synchronization and software compensation algorithm to generate a synchronous timestamp; based on the synchronization timestamp, aligning the event sequences of the client and the server through a dynamic time warping algorithm to generate an aligned event sequence; performing causal probability calculation on the aligned event sequence based on a Bayesian network model, and determining a causal relationship weight between resource events; and performing root cause matching according to the causal relationship weight and a preset abnormal mode library, generating a root cause list with priority ranking, and triggering execution of a self-healing strategy.

Methods, systems, and computer program products are provided for training a transactional event data analysis model and fine-tuning a pre-trained model to predict future transactions. The method involves storing asynchronous event sequence data of a user in a transaction table, selecting a user transaction at a snapshot time, selecting transactional events made by user U before time, and storing them in a transactions table. The method further includes mapping the timing position of each transactional event in the transactions table using, e.g., an integer index or a one-hot encoded vector. Features are selected and encoded into token encodings and time encodings.

The invention discloses a key place-oriented abnormal behavior intelligent identification and pre-processing system, which is characterized in that a preliminary abnormal event sequence is generated by collecting multi-modal environment data, the preliminary abnormal event sequence and a preset scene knowledge graph are subjected to semantic fusion to form composite abnormal event description information, and then a dynamic processing plan is generated based on large language model reasoning; the central scheduling agent is decomposed into a cooperative control instruction set to drive the video analysis agent, the broadcast grooming agent and the security and disinfection linkage agent to execute cooperative processing operation, situation evolution information is generated in a shared event canvas through environment feedback data, and dynamic optimization and adjustment of a processing strategy are achieved. According to the system, the whole process intelligence of the abnormal event from identification to disposal is realized, the semantic understanding ability of the system to a complex scene and the multi-agent collaborative response efficiency are improved, and the pertinence and the adaptive adjustment ability of a disposal plan are enhanced.

The invention provides a document collaboration method and system based on cloud office, and the method comprises the steps: firstly obtaining a target document operation record set of a cloud office platform, including user terminal operation event sequences (operation types and timestamps), and then recognizing document collaboration mode features according to the operation event sequences, operation overlapping time periods, type relevance and frequency distribution are covered, then a pre-training model is used for analysis to obtain a cooperation demand prediction result containing a cooperation object set and a cooperation strength index, dynamic permission configuration is conducted on cooperation objects according to the cooperation strength index, and a permission hierarchical structure is generated. And finally, in response to a cooperation request of the target user terminal, controlling the data synchronization process of the document based on the permission range. According to the method, the efficiency, the safety and the intelligent degree of cloud office document collaboration are effectively improved.

A training data set which includes event sequences representing actions of respective users of an application, properties of the users, and dynamic attributes associated with events such as the elapsed time between successive events, is prepared. A machine learning model which provides probabilistic predictions of next events of input event sequences is trained using the training data set. A trained version of the model is stored.

The invention provides an intelligent factory management method and system based on the industrial internet, and the method comprises the steps: obtaining original data of a multi-source heterogeneous device, and generating a semantic event structure through preprocessing; the industrial event sequence is mapped into nodes, candidate edges are generated in combination with the physical topology connection relation of the equipment, and a target causal map is constructed; according to the target causal atlas, calculating a node risk score of each event node, and performing balance adjustment on the score by using a graph-level centrality value to generate a normalized node risk score of each event node; mapping the node risk scores to devices, and calculating a risk average value of each device; and converting a scoring matrix of the current task to all candidate devices into a scheduling strategy value, executing a scheduling strategy and collecting task execution feedback, and dynamically adjusting a target causal atlas and a node risk score according to the difference between an actual result and a predicted value to realize dual optimization of the target causal atlas and the scheduling strategy.

The invention provides an event sequence prediction method based on a space-time diffusion generation network. Comprising two parts: an event sequence spatio-temporal feature extraction and de-noising diffusion module; the spatio-temporal feature extraction of the event sequence comprises an event space correlation modeling module, an event space correlation embedding module and an event time correlation learning and embedding module; the denoising diffusion module receives sequence time coding information and space coding information generated by the space-time feature extraction module, predicts time and space information of event occurrence by adopting a denoising diffusion model, and jointly learns space-time coding features by using a denoising network through a reverse denoising step to respectively obtain time and space attention output; and a de-noising result is obtained for multiple times through an iterative calculation method, and a space-time prediction result is finally generated. According to the scheme, the structural relationship and the spatial representation information between the sequence events are adaptively generated according to the spatial features of the sequence, and a better learning effect is achieved for different spatial distribution data.

The invention relates to a resource recommendation method and device based on a small enterprise dynamic portrait, equipment and a medium. According to the method, key events are extracted from enterprise multi-dimensional data and standardized to form an initial event sequence, importance scores are calculated based on frequency and weight, and a core event set is generated through self-adaptive filtering; according to the timestamps and the semantic features, calculating dependency strength among events, and constructing a causal event relation graph; learning event embedding and time sequence characteristics by using a graph neural network and a gating loop unit, and generating a dynamic portrait vector; analyzing a historical event influence weight through an attention mechanism to predict future demand distribution; the resource urgency degree and the matching degree are optimized in combination with enterprise capability constraints, and a prospective recommendation list is generated; and a closed loop is formed by predicting deviation to trigger incremental updating of the atlas and retraining the model. Finally, the dynamic capture of the development trajectory and the accurate pre-judgment of the resource demand are realized, and the recommendation timeliness and the service suitability are remarkably improved.

Disclosed herein are systems and method for detecting malicious activity using a tuned machine learning model. In one aspect, a method includes receiving a plurality of logs indicative of software behavior from a plurality of endpoint devices and generating a plurality of event sequences from the plurality of logs. The method includes training a global machine learning model using the plurality of event sequences to predict resultant events for a sequence of lead up events and classify whether the resultant events indicate malicious activity. The method includes, for each respective endpoint device of the plurality of endpoint devices, generating a testing dataset comprising a plurality of benign event sequences that occurred on the respective endpoint device. The method includes generating a tuned machine learning model for the respective endpoint device by retraining the global machine learning model using the testing dataset. The method includes executing the tuned machine learning model.

The invention relates to a micro-video popularity prediction system and method. The method comprises the following steps: constructing a micro-video data set; respectively training a micro-video retrieval module, a multi-mode processing module, a GRU4Rec module and a prediction module by utilizing the micro-video data set; a to-be-queried micro-video is obtained, on one hand, the to-be-queried micro-video is input to a micro-video retrieval module and a multi-modal processing module to be processed, and multi-modal features are obtained; on the other hand, the information is input to a GRU4Rec module to be processed, and recommendation features are obtained; and carrying out fusion processing on the obtained multi-modal features and the recommendation features to obtain a micro-video popularity prediction result corresponding to the micro-video to be queried. Compared with the prior art, the social network information and the recommendation system information except the video content are integrated, so that the factors influencing the popularity of the micro-video are more comprehensively captured, the influence of the user behavior on the popularity of the video is simulated, the time dynamic state of the event sequence can be captured, the understanding of the user behavior and the video propagation is enhanced, and the user experience is improved. And the accuracy of micro-video popularity prediction is greatly improved.

An event analysis and prediction system is disclosed that is configured to analyze at least one event data stream from a monitored system for the purpose of predicting future events in the system, e.g., for predictive maintenance of the system. The event analysis and prediction system advantageously predicts, in real-time, such failures in the monitored system that would otherwise lead to interruptions. The event analysis and prediction system advantageously leverages a novel data generation procedure that, in essence, converts the problem of failure prediction to a classification problem, which is solved using machine learning algorithms. The event analysis and prediction system is designed to work well even when there is limited labeled data available for model training.

The present disclosure provides a fault prediction method, a fault prediction model training method, a computing device, a storage medium, and a computer program product. The fault prediction method comprises: obtaining abnormal log data and unit attribute information of a service processing unit; determining an abnormal event sequence on the basis of the abnormal log data; and inputting the unit attribute information and the abnormal event sequence into a fault prediction model to obtain a fault prediction result of the service processing unit, wherein the fault prediction model is obtained by performing training on the basis of a positive sample, a first sample label corresponding to the positive sample, a negative sample, and a second sample label corresponding to the negative sample, the positive sample comprises a positive sample abnormal event sequence and sample unit attribute information, and the negative sample comprises a negative sample abnormal event sequence and sample unit attribute information. The training data of the fault prediction model is richer, so that the accuracy of prediction results of the fault prediction model during applications is improved.

The invention discloses a digraph-based power grid abnormal event identification method and device, terminal equipment and a storage medium, and the method comprises the steps: obtaining current power data and historical fault data in a to-be-detected region, and extracting entities, relationships and attributes from the current power data and historical fault data to construct an initial digraph; acquiring historical power data, and adjusting the weight of a directed edge in the initial directed graph to obtain an optimized directed graph and conditional probability distribution of each node; performing Monte Carlo random sampling on the optimized directed graph to generate a plurality of abnormal event sequences; and determining the actual node state of each node according to the current operation parameter, matching the node state feature of the actual node state with the event state feature of the abnormal event for each abnormal event sequence, determining a target abnormal event sequence, and further identifying the abnormal event occurring in the power grid. According to the invention, the abnormal event of the power grid can be identified.

The invention discloses a depth time sequence prediction method and system based on an event gating mechanism, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining time sequence data and corresponding event sequence data; mapping the time sequence data and the corresponding event sequence data to a low-dimensional potential vector space to obtain a time sequence feature representation and an event sequence feature representation; according to the time step sequence, inputting the time sequence feature representation and the event sequence feature representation into an improved recurrent neural network for processing to obtain a hidden state sequence; performing attention calculation based on the hidden state sequence to obtain final vector representation; and obtaining a prediction result according to the final vector representation. According to the invention, the prediction capability of the time sequence containing the non-periodic event interference can be improved.

The invention relates to the technical field of man-machine interaction data, in particular to man-machine interaction equipment and a control method thereof. The method comprises the following steps: acquiring equipment screen operation data, performing time window segmentation, generating a discrete touch event sequence and a dynamic touch feature sequence, clustering and processing the features by utilizing interaction features, forming user operation behavior grouped data, and combining equipment historical interaction data to form a user operation behavior group. Frequent item set mining is carried out to identify a high-frequency operation mode, a user operation preference model is constructed, user operation habits are identified, redundant actions are eliminated, main interaction action data are extracted, interaction intention prediction is carried out on main interaction actions based on historical data of equipment, and dynamic response is carried out on the man-machine interaction equipment according to a prediction result. And effective control is realized. According to the invention, a more personalized and smoother man-machine interaction equipment control method is realized.

The invention provides an event-driven sparse attention optimization method and system based on a brain-like computing chip, and belongs to the technical field of computers.The method comprises the steps that semantic analysis and feature extraction are conducted on input data and text information, and an input event sequence and a text event sequence are generated; calculating semantic relevancy between the input event sequence and the text event sequence, and generating an event importance evaluation matrix; calculating a sparseness parameter based on the complexity of the input data and the load of a brain-like calculation chip calculation node, and strengthening the sparseness parameter to obtain a target sparseness parameter; based on the target sparseness parameter and the event importance evaluation matrix, generating a sparse attention mask matrix; a sparse attention calculation task based on the sparse attention mask matrix is allocated to calculation nodes of the brain-like calculation chip for execution; and the output results of the calculation nodes are fused to generate a target output result. According to the invention, the accuracy and efficiency of the query result are improved.

The invention discloses a teaching event extraction method, device and equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the following steps: collecting a video sequence, an audio sequence and a text sequence in a teaching scene in real time, and inputting the video sequence, the audio sequence and the text sequence into a teaching event extraction model, a multi-level teaching event sequence is obtained; teaching events in the multi-level teaching event sequence are defined as nodes, semantic association of two adjacent teaching events is defined as edges, and a teaching event knowledge graph is constructed; and extracting a teaching event through the teaching event knowledge graph. According to the knowledge graph disclosed by the invention, the multi-dimension and time sequence of a plurality of teaching events and the complex relationship among the teaching events are considered, and the node information is updated according to the real-time teaching scene information, so that the teaching event extraction accuracy is improved.

The invention provides a social search hotspot propagation prediction method and system based on a multi-feature sequence, a program, equipment and a storage medium. According to the method, time information, event type information, user information and user attribute information in the event sequence are considered, representation learning is carried out on the social search hot event sequence by utilizing the Transform model, and the learned representation vector is used as the input of the Hawkes process, so that the mutual influence relationship between the events can be better captured. According to the method, the event sequence is predicted according to the intensity function of the historical event, the short-term prediction is to predict the occurrence time and the event type of the next event, and the long-term prediction is to predict the propagation size of the event in a given time interval, so that better sequence data fitting and sequence prediction effects are achieved.

Systems and methods for generating customizable models for predicting user behavior are provided. Such a method includes: obtaining first structured event data representative of first events performed by one or more users; training a generative language model using the first structured event data; obtaining second structured event data representative of one or more second events performed by a user; and predicting, at least in part by applying the second structured event data as an input to the trained generative language model, third structured event data representative of behavior associated with the user according to one or more customizable analytic outputs.

The invention discloses a natural language driven situation awareness control method and system based on task intention and a medium. Relates to the technical field of artificial intelligence and man-machine interaction. Generating a natural language template library and a task intention field table based on the three-table knowledge; constructing an interface-data double-mapping knowledge graph by taking three-table knowledge as a construction basis and taking a natural language template library and a task intention field table as retrieval rearrangement tools, and then performing semantic retrieval and anaphora resolution to generate structured intention data; on the premise of not depending on a fixed layout, a user instruction is analyzed into a task intention, stable anchoring of an interface object is achieved through semantic positioning, and the task intention is automatically compiled into a system API call or interface event sequence; and meanwhile, the availability and compliance of execution are guaranteed through a closed-loop mechanism of credible execution scoring and minimization clarification, so that the operation complexity is effectively reduced, and the response efficiency and intelligent interaction level of the situation awareness system are improved.

The invention discloses a laboratory risk control method based on a graph neural network, and the method comprises the following steps: S1, constructing a dynamically updated interaction event graph based on an interaction event sequence of laboratory equipment, personnel and environment; s2, dynamically updating the state space of each node according to the event preorder relation, and generating a synchronous node state sequence; s3, generating an early warning feature set of a node state change trend by adopting a graph convolution operation; s4, constructing an inter-node risk incidence matrix according to the early warning feature set, and generating an updated risk propagation path; s5, evaluating the probability of transition from the node state to the abnormal state, and generating a state risk prediction value; and S6, inputting a wolf pack optimization algorithm, and optimizing a parameter combination to obtain a laboratory risk prediction result. According to the invention, the real-time performance and accuracy of laboratory risk prediction are improved.

The invention provides a dual-network collaborative event sequence automatic testing method and device and a storage medium. The method comprises the following steps: generating a trigger identifier associated with a first pulse as a time reference; slave station logs are collected on the secondary backboard bus side, and communication messages are collected on the primary communication network side; correcting time offset and time drift of the primary communication network and the secondary backplane bus to generate a primary event sequence and a secondary event sequence; performing consistency comparison on the primary event sequence and the secondary event sequence under a unified time base to obtain a consistency comparison result; calculating an interval between adjacent events, comparing the interval with an expected interval to obtain a time scale precision checking result, and calculating an event resolution result; and calculating the time delay between the request and the response and counting the pairing rate to obtain a redundant pairing statistical result. According to the invention, end-to-end automatic testing, unified time base alignment, dual-network consistency verification, redundant request, response quantification and millisecond-level event resolution can be realized.

The invention provides a computer performance dynamic adjusting system and method based on user operation behaviors, and relates to the technical field of computers. Comprising an event capture module, an operation semantic association module, a system resource optimization module and an adaptive correction module. The event capturing module obtains user operation data and generates an operation event sequence with a timestamp. The operation semantic association module is used for matching an operation event and an operation mode through a nonlinear time axis, and generating a resource demand vector in combination with a window focus and a process stack calling relationship. And the system resource optimization module selects a resource allocation scheme with the lowest power consumption on the basis of an ant colony algorithm under the condition that response time constraints are met, and reallocates resources. And the adaptive correction module adjusts the matching weight of the operation mode by comparing the deviation between the actual response time and the expected response time, and feeds back a corrected matching threshold. The system can dynamically adjust computer performance, improve response speed and reduce power consumption.

The invention provides a lip language recognition method and system, terminal equipment and a medium, and the method comprises the steps: collecting a lip reading video through an event camera, and converting the lip reading video into an event sequence; voxelization is carried out on the event sequence, and data representation is carried out on the event sequence after voxelization in a voxel grid mode; and inputting the event sequence in the voxel grid form into a binary lip language recognition network model for lip language recognition to obtain a lip language recognition result corresponding to the lip reading video. According to the invention, the efficiency and accuracy of lip language recognition can be improved.

The invention relates to the technical field of network security, in particular to a network security operation and maintenance automatic analysis and response system based on artificial intelligence, which comprises an operation sensing module for acquiring a server state, extracting a security log, monitoring a network protocol and a transmission direction, identifying a performance bottleneck, sorting event data and generating a sensing summary sheet, the path offset identification module tracks a cross-domain path, identifies an abnormal path and generates an offset list; the behavior chain construction module sorts an event sequence and generates a behavior chain graph; the response processing module arranges processing steps, records a response process, generates an operation and maintenance execution sequence, synthesizes an output module to complete a process and generates an analysis result. According to the invention, by comparing the server resources with the historical reference, the abnormity is identified, and the event behavior basis is provided. And tracking cross-domain path lag and abnormity, and positioning threats. Arranging event data, and constructing an attack chain. The automatic response improves the speed and accuracy, shortens the response time, and enhances the attack handling capability.

The invention relates to the technical field of informatization, in particular to a workflow engine implementation method based on event traceability and decentralized monitoring, which is based on the design of recording node operation and deducing a process state on the basis of an immutable event sequence, thoroughly relieves the constraint of traditional state table storage on dynamic adjustment, and improves the working efficiency. Operation such as node insertion, skipping or rollback can take effect in real time through runtime metadata reconstruction, meanwhile, a completely stored operation event chain naturally supports full-process audit tracing, and a log system does not need to be additionally developed; a power proportion algorithm decomposes distributed node decisions into three layers of logics of role allocation, predefined result verification and secondary monitoring triggering, cross-service consistency is guaranteed through a post weight adjustment and historical behavior voting dual mechanism, and an online voting mechanism dynamically calculates and processes human weights by using event logs, so that the decision accuracy is improved. Therefore, the technical bottlenecks of rigidity, redundancy and weak consistency of the traditional scheme are overcome.

A method and related system for efficiently capturing relationships between event feature values in embeddings includes flattening an event sequence into a feature sequence including a first event prefix, a second event prefix, and a first set of feature values. The method includes generating an attention mask including first mask indicators to associate the first set of feature values with each other and second mask indicator to associate a first feature value of the first set of feature values with the second event prefix. The method includes providing the feature sequence and the attention mask to a self-attention neural network model to generate an embedding.

The invention relates to the technical field of computers, and provides a log anomaly detection method and device, equipment, a storage medium and a program product. The method comprises the steps of constructing a time sequence event graph based on a log event sequence; determining an adjacent matrix of each event graph snapshot and a node feature matrix of each event graph snapshot; inputting each event graph snapshot, each adjacent matrix and each node feature matrix into a dynamic graph model, and performing time sequence feature extraction and link prediction to obtain a predicted event graph snapshot; and judging whether the log event sequence is abnormal or not based on the predicted event graph snapshot. By means of the mode, efficient detection and positioning of the abnormal log can be achieved, the detection effect of the abnormal log is improved, and the detection requirement of an information system for log abnormity is met.

The invention relates to the technical field of intelligent manufacturing systems, and discloses an optimal scheduling search method for a multi-robot coordinated cooperation system, which comprises the following steps of: constructing a library timing Petri net model of the multi-robot coordinated cooperation system according to a robot resource use condition and a production process procedure of the multi-robot coordinated cooperation system; constructing a state diagram under a steepest excitation strategy based on a library time-assigned Petri net model, wherein the state diagram retains an optimal state sequence corresponding to the shortest processing time; designing a state merging rule in combination with the structure information of the Petri network, removing redundant states which do not belong to the optimal state sequence, and reducing the scale of the state diagram; the optimal scheduling scheme of the system can be obtained by searching the state diagram through global search. According to the method, a timing Petri net model which effectively represents a multi-robot resource conflict relationship and a processing event logic and time relationship is constructed, and a state space containing an optimal processing event sequence is constructed and searched on the basis, so that an optimal scheduling strategy is solved.

The invention relates to the technical field of log analysis, and discloses a log association analysis method and system for a network abnormal operation behavior, and the method comprises the steps: carrying out the normalized element analysis of a multi-source heterogeneous log of a target network environment, and obtaining a standardized event of the multi-source heterogeneous log; performing time sequence dependence mining on the standardized event to obtain a potential threat mode of the standardized event; performing active traversal matching on the standardized events to obtain a candidate associated event sequence of the standardized events; performing reverse deduction on a subsequent evolution stage of the target network environment to obtain a verification probe strategy of the subsequent evolution stage; applying the verification probe strategy to the target network environment, and performing data acquisition on the target network environment to obtain verification feedback data of the target network environment; performing threat degree evaluation on the candidate associated event sequence to obtain a research and judgment report of the target network environment; according to the invention, the efficiency of log association analysis of network abnormal operation behaviors can be improved.