7 results about "System engineer" patented technology

The invention provides an undercarriage system modeling method based on a natural language demand text, and relates to the technical field of model-based computer aided design, and the method comprises the steps: S1, constructing a SysML meta-model knowledge base and an undercarriage system model knowledge base; s2, analyzing the natural language demand text, and generating a structured demand text of the undercarriage system; s3, mapping a structured demand text entity of the undercarriage system into a node type of a semantic graph; and S4, in combination with a historical model template and real-time retrieval, generating an undercarriage system SysML model according to the semantic map obtained in the step S3, and performing undercarriage system engineering analysis through the undercarriage system SysML model, thereby improving the efficiency and quality of aviation equipment development. On the basis of a retrieval enhancement generation technology and an intention routing mechanism, the modeling efficiency and accuracy of the undercarriage system are remarkably improved; a graph neural network and a semantic vector space model are adopted to realize deep fusion and conflict resolution of cross-domain knowledge; and through an incremental learning mechanism, model continuous evolution and design experience closed-loop iteration are supported.

The invention discloses a system engineering meta-model generation method based on a scene and a DM2 theory, and relates to the technical field of system engineering, and the method comprises the steps: obtaining scene description information related to a target business scene; performing multi-level analysis on the scene description information to obtain a corresponding service model; performing hierarchical modeling and conversion on the business model based on the hierarchical architecture of the DM2 framework to generate structured model data; and according to the expansion requirement of the system engineering field, performing field expansion and integrated processing on the model entity and the semantic relationship in the structured model data to generate a system engineering meta-model. Through the method, the technical problems of incomplete sorting of the meta-model, separation from actual business and poor ductility in a traditional mode are effectively solved.

The application discloses a kind of generation methods, device and equipment of intelligent instrument technical demand and storage medium, comprising: according to the existing technical specification document of intelligent instrument generation online shared modular database;Receive the architecture creation instruction input by architecture engineer, and generate display setting demand according to architecture creation instruction and modular database;When receiving the technical demand creation instruction of total demand party, generate initial intelligent instrument technical demand according to display setting demand;When receiving the function supplement instruction of system engineer party, modify initial intelligent instrument technical demand according to function supplement instruction to generate formal intelligent instrument technical demand.Through the existing technical specification document generation shared modular database, generate formal intelligent instrument technical demand according to the instruction of architecture engineer, total demand party and system engineer.Can adjust intelligent instrument technical demand according to real-time demand, improve the efficiency of intelligent instrument technical demand generation.

The application provides a kind of based on relationship diagram convolution network's architecture view model completion method and system, it is related to complex system and system engineering technical field, by mapping architecture view model structure into multi-type relationship knowledge graph, the element of multi-view model and its multi-type relationship are mapped into unified knowledge graph structure, not only realize unified data representation and structured input, but also support the analysis and reasoning of architecture, so that nodes and relationships can be directly processed by graph neural networks;Then by combining logical constraint rules and feature enhancement mechanism, improve the prediction accuracy and interpretability of R-GCN for missing nodes and relationships;Finally, through R-GCN model reasoning, the architecture model is realized, and the multi-view architecture is automatically semantically inferred, and finally the intelligent automatic completion of the missing elements of the architecture view model is realized.

The invention relates to the technical field of traffic safety, and discloses a train control system credibility evaluation method based on a V model and a check table method, which comprises the following steps: S1, constructing a train control system credibility evaluation framework based on a system engineering V model; the train control system credibility evaluation framework comprises a target decomposition process from top to bottom and a system verification evaluation process from bottom to top; s2, determining key equipment of the train control system according to the train control system credibility evaluation framework, establishing a credibility index system, and formulating an evaluation standard of a bottom layer index; s3, according to the evaluation standard and the collected corresponding related evaluation data, performing credibility coincidence evaluation on each device through a check table method; and S4, summarizing inspection results to obtain credibility evaluation results of the devices and the whole system. The credibility evaluation method provided by the invention has the characteristics of systematization, structuralization and strong traceability, is simple and easy to operate, and can perform comprehensive and accurate credibility evaluation on the train control system.

The invention discloses a knowledge-driven aerospace system engineering task assurance quality risk analysis method, and the method comprises the steps: obtaining aerospace system engineering quality risk data information, which comprises structured data, semi-structured data and unstructured data; processing the spaceflight system engineering quality risk data information to obtain a spaceflight system engineering quality risk data processing result; the aerospace system engineering quality risk data processing result comprises a data behavior pattern recognition result and a knowledge graph; processing the aerospace system engineering quality risk data processing result to obtain a risk assessment result; processing the risk assessment result to obtain a risk control result; the risk control result comprises a risk plan intelligent pushing result and a risk assessment report. According to the method, the knowledge-driven aerospace system engineering task assurance problem knowledge graph and the large model are constructed, aerospace system engineering quality assurance is carried out, and then rapid fault diagnosis and disposal measure recommendation are achieved.

Techniques for generate personalized satisfaction surveys for particular customers are disclosed. A system tracks interactions and events involved in a service request received from a customer. The tracking includes logging interactions between the customer, customer service agents, and service teams. Using the logged information, the system engineers prompts for a large language model to generate a satisfaction survey that includes a survey question tailored to the customer's particular service request. After the system receives a response to the survey, the system submits the content to a machine learning model trained to determine a satisfaction score for the survey.