17 results about "Timing diagram" patented technology

The application discloses a kind of soft measurement modeling methods of fermentation process based on time sequence diagram network, belongs to the technical field of soft measurement of fermentation process.It includes the following steps: (1) data acquisition and integration: the penicillin fermentation process under different conditions is obtained, and the data is divided, collected and integrated;(2) data selection: the data is selected, the redundant useless data is removed and the causal diagram between variables is established;(3) modeling training: algorithm model is constructed, and learning training is carried out;(4) model prediction: the trained algorithm model is used for prediction, and the prediction result is given.The application proposes a kind of soft measurement modeling method of fermentation process based on time sequence diagram network, improves the prediction accuracy of key product quality of fermentation process;The method uses graph convolution network and long short-term memory, extracts data in time and space dimensions, increases the generalization of model;The method can accurately measure the key product quality of different fermentation processes.

This application discloses an interface dependency parsing and automated testing method, device, and storage medium, comprising: extracting the minimum path set of the target medical business scenario from the interface call sequence diagram based on the target medical business scenario; using the minimum path set as the test path, parsing the data dependency relationship of each interface in the test path, extracting data flow rules to generate test data; calling the interfaces of the test path according to the test data to generate integrity verification results; comparing the integrity verification results with the expected behavior of the interface call sequence diagram to locate abnormal test nodes; and identifying changes in the interface dependency relationship of abnormal test nodes. This application automatically constructs and dynamically updates the interface call sequence diagram from real call logs through a time sequence pattern mining algorithm, replacing manual dependency analysis. When business logic changes, the test path and data flow rules are re-extracted to ensure that the test plan is updated synchronously with the business, thereby reducing the maintenance cost of test scripts.

The application relates to the technical field of agent dynamics, and particularly discloses a multi-agent dynamics modeling method based on PEACE, which comprises the following steps: constructing time sequence diagram data; extracting invariant observation embedding data; constructing agent model data; based on system parameters, generating prompt context through coding and self-attention mechanism, initializing individual prototype prompts, decoupling and fusing the individual prototype prompts and the invariant observation embedding, combining graph ordinary differential equations to construct an individual agent model, and realizing decoupling of the two types of embedding by minimizing mutual information loss; generating enhanced data; cooperatively optimizing and training; and performing dynamics prediction. In this way, the scheme can improve robustness in a distribution shift scenario.

This application discloses an AI-based cross-scene furniture e-commerce interaction system and method. The system includes: an event receiving and recording module, a sequence verification module, a representation vector generation module, a credibility calculation module, an attribution classification module, and a sample update module. The system receives interaction events, establishes room scene records and product version records, and generates version influence vectors; it performs sequence verification on the same user events, constructs a time series diagram, and generates representation vectors; it calculates event credibility by combining scene validity period, product version validity period, time deviation, and associated anomalies; it performs replay verification and attribution classification on low-credibility negative feedback events, and updates training samples, user preference states, and product recommendation states accordingly. This system can improve the accuracy of interaction data processing and recommendation updates.

The application relates to a ball game situation judgment method, system, device and medium, the ball game situation judgment method comprising: synchronously processing real-time video and historical data to generate a unified space-time reference battle data set; constructing a dynamic time sequence diagram based on opponent data and encoding to generate a team state embedding vector; decoding the vector to identify a structured real-time opponent tactical intention; combining the intention with the historical strategy of the own side to perform game analysis, generating a countermeasures strategy instruction by comprehensively evaluating the strategy risk and deceptive benefits; and finally analyzing the instruction into specific executable tactical actions. The method breaks through the limitation of one-way behavior prediction in the prior art, realizes a paradigm shift from "prediction-reaction" to "intention recognition-active game", can dynamically cope with high-level tactical confrontation, generates an intelligent countermeasures strategy that is not easy to be predicted by the opponent, and thus improves the effectiveness of on-site decision-making.

The application provides a collection implicit fraud identification method and system based on a timing graph, including: collecting full-cycle data and preprocessing to form an original data set; constructing a basic graph with a collection target user as the core; mining fine-grained attributes and extracting exclusive fraud features to form a feature-rich conversation timing semantic graph; performing multi-dimensional reasoning, extracting core risk features and quantifying to form a comprehensive risk feature matrix; inputting a risk judgment model to perform implicit fraud judgment and outputting a judgment result. The application constructs a conversation timing semantic basic graph with a collection target user as the core, not only deeply fuses the implicit fraud features and the collection interaction basic features, but also multi-dimensionally and deeply mines and quantitatively represents the implicit fraud features, realizes the graphed and timed unified representation of the collection full-cycle interaction data, and improves the accuracy and comprehensiveness of the collection fraud identification.

The application provides a method and device for event prediction based on a timing diagram rule, the method comprising: obtaining timing diagram data, and adding event prediction results of a machine learning model to the timing diagram data to obtain timing diagram extension data; extracting a subgraph for verifying prediction rule reliability from the timing diagram extension data according to a verification ratio; training a rule creator according to the timing diagram extension data and the subgraph to obtain a timing event prediction rule set; and obtaining a prediction event set according to the timing event prediction rule set and the timing diagram data. By embedding the machine learning model for event prediction into a rule-based association relationship system as a predicate, the association rule can not only use the existing machine learning model event prediction result, but also use a logical condition to improve the prediction result of the machine learning model, has stronger expression ability, and does not have to constrain constant interval time or have a common focus constraint on a graph pattern.

The application relates to the technical field of data processing, in particular to an engine part production tracing method and system based on bar code data, which comprises the following steps: acquiring a bar code scanning event sequence of an engine part; reconstructing a link of the bar code scanning event sequence by using an improved time sequence diagram attention network model and outputting a tracing result of the engine part; wherein the improved time sequence diagram attention network model comprises a frequency parameter. The application solves the problems of low accuracy and continuity of the tracing result.

The present application relates to a kind of optimization method and system of timing diagram subgraph matching.The method includes: time block is carried out to original timing diagram, and the edge with same end point is aggregated and compressed in each time block to generate intra-block multi-edge object, to build block index in turn;Query graph with time constraint is received, with the edge in query graph as matching unit to execute search, and maintain current legal time window;In candidate generation stage, according to current to be matched data vertex pair and current legal time window, construct cache key, and carry out exploration in hotspot cache;If hotspot cache is not hit, then use current legal time window to locate target time block, and execute intersection operation in target time block to generate candidate multi-edge list;When current to be matched data vertex pair meets preset hotspot condition, the candidate multi-edge list is written into hotspot cache;Continue to execute search based on the candidate multi-edge list of hit or generated candidate multi-edge list, until outputting model matching result.

The application discloses a knowledge-based temporal convolution neural network (KB-TGCN) blast furnace fault diagnosis method. The method can solve the space-time dependence and sample imbalance problem of the blast furnace ironmaking process. First, the calculated variables that can further reflect the process state are calculated by using the variables directly detected by the sensor. Then, the knowledge-based graph structure is constructed according to the spatial position and calculation relationship of the variables. Subsequently, a one-dimensional time information extraction module is embedded in the graph convolutional neural network. Therefore, the TGCN can capture time information while maintaining the original spatial relationship. By using the knowledge-based graph structure, the KB-TGCN can fully mine the spatial information of different blast furnace positions. In addition, the method uses a focal loss function with an adaptive balance factor instead of a traditional cross-entropy loss function to overcome the sample imbalance problem.

This invention provides an automatic UML sequence diagram generation method compatible with multilingual code analysis. By capturing the characteristics of C++ and Java code, it identifies the code types of each part of the mixed source code and analyzes different code types. For code marked as C++, regular expressions are used to match keywords, class information, and inheritance information of the C++ source code to be analyzed, and these are converted into parameter names. For code marked as Java, syntax and lexical analysis are used to extract class information and method call information of the Java source code to be analyzed. Corresponding Plant UML code is generated and drawn as a comprehensive UML sequence diagram. This invention's automatic UML sequence diagram generation method compatible with multilingual code analysis can intuitively reflect the functional and logical composition of mixed C++ and Java code, and intuitively reflect the interaction relationship between them. It avoids the need to view tedious source code to obtain program information and functional relationships, improving code readability and maintainability.

The present application relates to a kind of rolling time sequence calculation method, belong to the technical field of steel rolling production control.The method constructs time sequence number pair sequence with rolling piece coordinate and motion time, according to speed system, the entire rolling piece movement process is decomposed into several one-way movement process, and each one-way movement process is divided into different speed stage with acceleration size, the starting speed, acceleration, moving distance and moving time of each speed stage are calculated in turn, with a few variable sequence, the complex rolling piece movement process is digitized, on this basis, the motion time sequence of each one-way movement process is calculated section by section, and according to the needs, the rolling piece motion time sequence diagram, key equipment running state time sequence diagram etc.are drawn, greatly improve the precision and calculation efficiency of rolling time sequence calculation, it is favorable to off-line simulation to guide production line design, also favorable to online control to improve temperature prediction accuracy, optimize production rhythm, improve control precision, to improve product quality and economic benefits.

This invention relates to the field of PCB circuits and discloses a method for online testing and fault node localization of PCB circuits based on a timing graph convolutional network. The method includes mapping components to graph nodes and electrical connections between components to graph edges according to the PCB circuit design documents, constructing a graph structure representation of the PCB circuit. Timing signal data of each node under normal and fault conditions are collected using online testing equipment. The timing data is normalized to fill in missing information in the graph structure. If there are outliers, the timing data of the corresponding node is re-collected. The preprocessed graph structure and timing data are then input into the timing graph convolutional network. This invention achieves automated fault node localization, significantly improving localization efficiency and reducing the time and labor costs of manual troubleshooting. It solves the problems of low efficiency, low accuracy, and inability to meet the increasingly complex fault diagnosis needs of existing fault localization methods.

This invention discloses an efficient static timing analysis method for large-scale digital circuits, belonging to the field of electronic design automation technology. This method addresses the problems of high computational cost and low efficiency in critical failure path selection during common path pessimism elimination in existing static timing analysis methods. First, it reads the circuit netlist, standard cell library, and timing constraints to construct a timing diagram and clock tree. Then, it performs arrival time and required arrival time propagation to obtain the estimated timing margin for each timing check. Next, it establishes a nearest common ancestor query structure for the clock tree and a cumulative value of common path pessimism. Then, it selects candidate paths in order of decreasing estimated timing margin, performs common path pessimism elimination on these candidate paths, and obtains the corrected timing margin. Finally, it outputs the target number of critical failure paths based on the corrected timing margin. This method limits the computationally intensive precise correction to a small number of candidate paths, improving the efficiency and scalability of static timing analysis for large-scale digital circuits while ensuring the reliability of the analysis results.