8 results about "Process analytics" patented technology

A method, apparatus, device, storage medium, and program product for information presentation are provided. The proposed method includes: receiving a generation request, the generation request instructing an intelligent system to generate at least one visualization component corresponding to tabular data; and presenting analytical information, the analytical information instructing the intelligent system to create at least one visualization component in a component page, the analytical information including first information of the at least one visualization component, the first information indicating the layout attributes of the at least one visualization component in the component page.

Automatic detection and handling of security-related anomalies by utilizing machine learning and a large language model (LLM). A computerized method for detecting and handling security threats in an organizational network of an organization includes: (a) collecting event data that pertain to organizational users, organizational devices, and organizational resources of the organizational network; (b) constructing user profiles, device profiles, and resource profiles; (c) constructing Organizational Context information that pertains to organizational users, organizational devices, and organizational resources; (d) constructing a time-series of events, enriched with the Organizational Context information; (e) analyzing the time-series of events using a Machine Learning process that detects an anomalous event, and automatically generating an alert message pertaining to and describing the anomalous event.

Embodiments of the present disclosure include techniques for processing analytic data using delta processing. In one embodiment, result data is generated from source data using a data pipeline and calculation plans. The result data may be modified and the plan may be re-executed to produce the modified result data. A reduced calculation plan is generated including only a portion of the original calculation plan to reduce the computational resources needed to execute the data processing. In some embodiments, data cached by operations in the pipeline are reused so the entire pipeline does not require re-execution to produce the modified result data.

A method for predicting the remaining life (RUL) of rolling bearings across multiple stages based on deep transfer learning is proposed. First, raw vibration signal data of the rolling bearing is collected and segmented using a sliding window method to obtain multi-stage vibration signal data. Next, the change in the health index curve is obtained by calculating the maximum mean difference between the self-health data and the multi-stage vibration signal data. Then, the bearing degradation stage is determined by applying statistical process analysis to the health index curve. The raw vibration signal data is preprocessed, partitioned, and labeled before being fed into a residual network and an Adaptive-Transformer network for training. Finally, the test set is fed into the Adaptive-Transformer network for prediction, outputting the final predicted RUL of the rolling bearing. This method considers the RUL of the bearing at each stage, reducing the prediction loss RMSE by approximately 0.1 and improving the prediction accuracy by 4%–6%.

The application discloses a self-adaptive improvement method and device for reading ability evaluation, which comprises the following steps: terminating judgment is performed based on the ability preset condition of the IRT self-adaptive algorithm; a reading path, information marking behavior, draft reasoning process and final score of a tested person are recorded in real time to generate an ability and strategy report and determine a reading process analysis and evaluation result; based on a reading evaluation index, necessary parameters, optional parameters and unlocking logic are determined; meanwhile, a NLP model and voice emotion analysis are used to collect voice interaction data in the reading process of a user; a reading task library covering different school stages and different reading targets is built, and a matching operation guide is formulated; a standardization intervention script library corresponding to a fixed intervention prompt is constructed, and the level and timing of reading intervention are determined; and a front-end chart engine is used in combination with a back-end graph to generate a visual ability report for reading result interpretation, so that the reading level and literacy of a reader are improved.

This application relates to a bidirectional feedback dynamic collaborative optimization system and method based on a large language model. The method includes: acquiring raw instruction data from multiple data sources; performing positive feedback optimization on the raw instruction data to form positively optimized instruction data; matching and sampling the positively optimized instruction data to generate training data; training a large language model using the training data and monitoring the training process to generate process analysis data; acquiring the training results of the large language model and generating result analysis data based on the training results; generating negative feedback instruction data through the process analysis data and the result analysis data; and applying the negative feedback instruction data to the positive feedback optimization process to achieve dynamic collaborative optimization of training data quality and large language model performance. This application can construct a closed-loop system for the co-evolution of data and model, achieving simultaneous improvement in data quality and model performance.

The application discloses a domain-oriented MBSE process definition method, comprising the following steps: S1, performing object analysis, identifying system structure, interaction boundary and responsibility division; S2, performing research and development process analysis, constructing a multi-level MBSE process structure; S3, performing matching analysis of objects and processes, forming a process structure tree expanded according to levels; S4, performing research and development role definition, dividing modeling roles and clearly defining task boundaries and tool adaptation; S5, performing matching analysis of roles and processes, establishing role allocation relationships and task responsibilities; S6, performing model transfer analysis, defining model input and output, transfer path and delivery responsibility; S7, performing domain-level MBSE process definition, constructing a reusable standard process template; S8, performing project-level process tailoring, generating a customized MBSE process scheme adapted to project requirements. The application constructs a ternary matching mechanism, and realizes MBSE process role modeling and model closed-loop collaborative management.

The application discloses a kind of modular reconfigurable production line control system integration method, it is related to industrial automation technical field, establishes virtual mapping based on physical attribute parameter, forms production line digital twin basic model framework;Based on real-time data acquisition of field sensor, establishes the dynamic mapping mechanism of digital twin body synchronous with physical production line state;Reconfiguration scheme is imported in virtual environment, the combination process is previewed by production line digital twin model, and key performance indicators are analyzed.The present application constructs virtual production line model through digital twin technology, supports the reconfiguration scheme to be previewed and verified in virtual environment, significantly reduces the trial-and-error time and cost required by traditional physical debugging, cloud AI algorithm further optimizes reconfiguration strategy, combined with edge real-time control, realizes closed-loop optimization from virtual verification to physical execution, so that production line can quickly complete local or overall reconfiguration according to production demand, greatly improves the response speed and flexibility of production line.