overview
Data types of aerospace overall design units
The aerospace overall design unit generates a large amount of data in research and development, production, testing and other links, and the data types are rich and diverse. According to the degree of structure of the data, the data of the aerospace overall design unit can be divided into the following categories:
Structured data: This type of data has fixed data formats and fields and is easy to store, manage and analyze. For example, product parameters, equipment status, test data, etc. Structured data plays an important role in the business system of aerospace overall design units, providing strong support for scientific research, production, management and other work.
Semi-structured data: This type of data has certain structural characteristics, but the data format and fields are not fixed. For example, reports, logs, XML/JSON, etc. Semi-structured data has important applications in daily office, project management and business analysis of aerospace overall design units.
Unstructured data: This type of data has no fixed data format and fields, and mainly includes text, pictures, audio and video, web pages, etc. Unstructured data is of great value in scientific research, testing, training and other fields of aerospace overall design units, such as scientific research reports, test records, training materials, etc.
Characteristics of unstructured data and difficulty of governance
Unstructured data accounts for a large proportion in the data system of the aerospace overall design unit and has the following characteristics:
Large amount of data: With the continuous advancement of aerospace technology, the amount of unstructured data has shown explosive growth. Taking scientific research reports and test records as examples, the number of reports and records generated every year amounts to tens of millions.
Diversity: There are many types of unstructured data, including text, pictures, audio and video, web pages, etc., which pose greater challenges to data governance.
Low value density: Compared with structured data, unstructured data has a lower value density and requires in-depth mining and analysis to give full play to its value.
Governance is difficult: The governance of unstructured data involves multiple aspects such as data collection, storage, processing, analysis, and application, and requires high technical and management capabilities.
Faced with the characteristics and difficulty of governance of unstructured data, aerospace overall design units need to take effective measures to improve the level of governance of unstructured data in order to give full play to their important role in the aerospace industry.
Typical applications of natural language processing technology in unstructured data processing scenarios
Natural Language Processing (NLP) technology is one of the key technologies in the field of artificial intelligence. It mainly studies how to let computers understand and generate human language. Typical application scenarios of NLP technology in unstructured data processing scenarios in design units include:
Text mining: Use NLP technology to mine text data such as scientific research reports and test records to extract key information, such as technical indicators and problem descriptions, to provide support for subsequent analysis.
Intelligent Q & A: Based on NLP technology, build an intelligent Q & A system to realize rapid retrieval and answering of unstructured data and improve work efficiency.
Machine translation: Achieve cross-language translation of unstructured data, assist in the collection of scientific and technological intelligence, and promote international cooperation and exchanges.
Automatic summary: Automatic summary of long text data to refine key information for quick browsing and understanding.
Content review: NLP technology is used to conduct content review on unstructured data. Since aerospace design data is related to the information security of the national high-tech industry, there are strict requirements on the scope of knowledge of data content and the compliance of data content. NLP technology can greatly reduce the intensity of manual content review.
Knowledge graph construction: entity recognition and relationship extraction are performed on unstructured data through NLP technology to construct knowledge graph, providing data support for intelligent recommendation, decision support and other applications.
To sum up, the digital construction of the overall aerospace design unit has achieved remarkable results, but it still faces the challenge of unstructured data governance. In the following contents, the author will combine the actual unstructured data processing project experience in the aerospace overall design unit, and comprehensively summarize the technical scheme of the aerospace overall design unit in document data processing.
Technical solution and technical route
Unstructured data is data that has no fixed format or organization. Unlike structured data, unstructured data does not follow predefined patterns or formats and is therefore more difficult to organize and process. In aerospace overall design units, document data mainly includes various design drawings, technical documents, research reports, meeting minutes, email communications, etc. This data usually exists in the form of electronic files and may be stored on employees 'computers, servers or private cloud storage platforms.
Overall technical plan and technical route for system integration
With the rapid development of information technology, aerospace overall design units have accumulated a large number of information systems over the years of system construction. However, due to the lack of information-based top-level planning, these systems are difficult to achieve interconnection, forming a large number of data islands. In order to use modern big data technology to analyze and process these scattered data, existing information systems need to be integrated and integrated. This plan aims to provide an integrated technical solution and technical route to achieve efficient integration of information systems and full utilization of data.
overall technical plan
By building a data integration and sharing platform, the data of each information system can be uniformly integrated and managed. Through the data integration and sharing platform, data exchange and sharing between different systems are realized, data islands are broken, and data utilization efficiency is improved.
At the same time of constructing data integration and sharing platform, establish data governance and quality management system to govern and control the integrated data. Through data governance and quality management systems, ensure the accuracy, integrity and consistency of data, and improve the quality and availability of data.
After data aggregation and concentration, the integrated data is analyzed and mined deeply by using big data analysis and mining technology. Through data analysis and mining, valuable information and insights are extracted to support decision-making.
Provide data visualization tools to visually display analysis results to users in the form of charts, reports, etc. Help users better understand and utilize data through data visualization and display.
During the entire process of data management, a series of security measures are taken, including data encryption, access control, identity authentication, etc., to ensure data security and user privacy protection.
technical route
Through in-depth demand communication and research with the aerospace overall design unit, we will clarify the goals and application scenarios of information system integration, and formulate corresponding technical plans and implementation plans.
Select appropriate technologies and tools for system integration based on demand analysis and planning. Technology selection should consider factors such as system scalability, performance, cost and ease of use. Take the data integration project of the aerospace overall design unit that the author has experienced as an example. Due to the particularity of the aerospace field, and at the same time, in order to cope with the storage, query, analysis, and utilization of massive multi-source heterogeneous data. This project selects an open source big data system that is deeply transformed and customized.
By introducing advanced real-time data processing technology, big data systems have significantly improved data processing speed and efficiency, allowing users to obtain data insights faster. In addition, this version enhances support for data types and is able to handle various data formats including structured, semi-structured and unstructured data, greatly expanding the application scope of the platform. In terms of security, multiple security mechanisms have been introduced, including data encryption, access control and audit logging, to ensure data security and compliance. At the same time, this version ensures the stability and reliability of the system through high-availability design. The application effect of big data systems depends largely on the quality of data. Establish a data governance and quality management system as a data source to manage and control the integrated data. The data governance and quality management system should include modules such as data standard management, data quality management, and data security management.
Use big data analysis and mining technology to conduct in-depth analysis and mining of the integrated data. Data analysis and mining technologies should include statistical analysis, machine learning, data mining algorithms, etc. Provide data visualization tools to display analysis results to users in the form of charts, reports, etc. Data visualization tools should support multiple chart types and data display methods to meet user needs.
Combine the functions provided by the above-mentioned big data system, integrate with corresponding information systems or tools, and put the integrated system online and put into practical application. At the same time, continuous operation, maintenance and optimization are carried out to ensure the normal operation and continuous improvement of the system.
Overall integration plan
Integration solution for labeling tools and big data systems
(1) The first is the integration of source data collection
The data to be annotated comes from big data systems, which include unstructured data (such as design documents, scanned handwritten reports, drawings, three-dimensional models, images, videos, etc.) and structured data (such as text extraction fragments, time series data, etc.). It can be roughly divided into several types of data such as text, image, video, and time series (structured).
Through the data integrated service bus, labeling tools implement Web services in SOAP/REST methods to complete data collection tasks.
(2) Mark the output of successful data and store it in the big data system
Use various data annotation tools to successfully complete the annotation of various data such as text, images, videos, and time series, and also use Web services to achieve distributed storage (HDFS) of big data systems.
Data aggregation and identification tools and big data system integration solutions
(1) The first is the integration of annotation data collection
The annotated data comes from big data systems. Among these initially annotated data, there are still several types such as text, images, videos, and time series.
Through the data integrated service bus, labeling tools implement Web services in SOAP/REST methods to complete data collection tasks.
(2) Data aggregation and processing by identification tools to output stereotyped labeled data and store it in the big data system
Using various data aggregation tools supplemented by identification tools, the aggregation of various stereotyped annotation data such as text, images, videos, and time sequences has been successfully completed. Similarly, through the data integration service bus, the distribution of big data systems is realized using Web services. Storage (HDFS)
Integrated solution for security audit intelligent Q & A support system and big data system
The Security Audit Intelligent Question and Answer Support System is one of the intelligent applications built on the basis of a knowledge base, which is built in a big data system. The data interaction between them needs to pass through a data integration service bus and adopt a Web service way to achieve integration with big data systems.
Intelligent search engine and big data system integration solution
Intelligent search engines are one of the intelligent applications built on the basis of a knowledge base, which is built in a big data system. The data interaction between them needs to be realized through a data integration service bus and a Web service method. Data system integration.
Technical solution for labeling tool
Document labeling tool is a tool used to help users classify and manage documents. It analyzes document content and automatically adds tags to documents, thereby improving the efficiency and accuracy of document management.
The role of document labeling tools
(1) Improve document management efficiency
The document labeling tool automatically adds tags to documents, helping users quickly find the documents they need. Using tags, users can easily classify and archive documents, saving a lot of time in manually classifying and managing documents.
(2) Improve the accuracy of document analysis
The document labeling tool can add accurate tags to documents by analyzing the document content. This helps users better understand the subject and content of the document, thereby improving the accuracy of document analysis.
(3) Promote information sharing and collaboration
Document tagging tools help users quickly find the documents they need and share them with other users. This helps promote information sharing and collaboration and improves team efficiency.
(4) Support personalized recommendations
Document tagging tools can recommend documents to users that are relevant to their interests and needs. This helps users quickly find the documents they need and improves the user experience.
Implementation principle of document labeling tool
(1) Text pretreatment
Text preprocessing is the first step in a document labeling tool. It includes operations such as word segmentation, removal of stop words, and part-of-speech tagging. Word segmentation is the process of dividing the text into words, removing stop words is to remove some common but meaningless words in the text, and part-of-speech tagging is to assign a part-of-speech label to each word in the text, such as nouns, verbs, etc.
(2) Feature extraction
Feature extraction is the core part of document labeling tools. It analyzes the text to extract features that can represent the theme of the text. Commonly used feature extraction methods include bag of words model, TF-IDF, Word 2Vec, etc. The bag-of-words model represents text as a collection of words. TF-IDF considers the importance of words in the text, and Word 2Vec maps words into a vector in a high-dimensional space.
(3) Label generation
Tag generation is the last step in the document labeling tool. It generates corresponding tags for the document based on the extracted features. Commonly used label generation methods include rule-based methods, statistics-based methods, and deep learning-based methods. Rule-based methods map features to labels by formulating a series of rules; statistics-based methods select the most likely label by calculating the association between features and labels; deep learning-based methods train a neural network model. Map features to labels.
(4) Model evaluation and optimization
Model evaluation and optimization is an important part of document labeling tools. It evaluates the model, identifies the shortcomings of the model, and optimizes it. Commonly used evaluation indicators include accuracy, recall, F1 value, etc. Optimization methods include adjusting model parameters, adding training data, using more advanced models, etc.
Document labeling tool is an efficient and accurate document management tool. It analyzes document content and automatically adds tags to documents, thereby improving the efficiency and accuracy of document management. This article introduces in detail the function and implementation principle of the document labeling tool, hoping to be helpful to readers. With the continuous development of artificial intelligence technology, document labeling tools will become more and more intelligent, bringing a better experience to users.
Data aggregation and identification tool technical solution
The data aggregation and identification tool is an intelligent system based on Natural Language Processing (NLP) technology that can automatically identify words with similar or similar semantics and bring them together to create logical associations. These logical relationships help users more accurately find related documents and corpus in subsequent queries and analysis.
Workflow of data aggregation tools
The tool's workflow is roughly as follows:
Semantic analysis: The data aggregation and identification tool first performs semantic analysis on the input text. This step usually includes word segmentation, part-of-speech tagging, named entity recognition, etc. to understand the semantic content of the text.
Semantic clustering: Based on semantic analysis, the data aggregation and identification tool uses semantic clustering algorithms to gather words with similar or similar semantics. These words may come from different documents or corpus, but they are semantically related.
Logical association creation: Once words are brought together, data aggregation and identification tools create logical associations. These logical relationships reflect the semantic connections between words and help users find related documents and corpus in subsequent queries.
Query support: Users can query associated documents and corpus based on logical association relationships. Data aggregation and identification tools can quickly return query results and help users find the information they need.
However, since there is a certain confidence space for the results processed by the algorithm, it is necessary to use manual identification tools to manually check the logical relationships generated by the algorithm. Manual verification can ensure the accuracy and reliability of logical relationships and avoid possible errors during algorithm processing.
Workflow of manual identification tools
The workflow of the manual identification tool is roughly as follows:
Display of logical relationships: The manual identification tool first displays the logical relationships generated by the algorithm, allowing users to intuitively understand these relationships.
Manual verification: Users can manually verify the displayed logical relationships based on their own knowledge and experience. This includes checking whether the convergence of words is accurate and whether the logical relationship is reasonable.
Error feedback: If a problem is found in the logical relationship, users can feedback the error so that the data aggregation and identification tool can adjust and improve it.
Optimization iteration: Based on user feedback, the data aggregation and identification tool will continuously optimize and improve algorithms to improve the accuracy and reliability of logical relationships.
To sum up, the data aggregation and identification tool is an intelligent system that uses natural language processing technology to automatically identify words with similar or similar semantics and create logical associations. It can help users more accurately find associated documents and corpus in subsequent queries and analysis. At the same time, manual verification of logical relationships through manual identification tools can further improve the performance of data aggregation and identification tools and provide users with more accurate and reliable data support.
Technical solution for safety audit intelligent Q & A support system
The safety review sheet is an industry-specific document form in the field of aerospace design. It is used by experts in the business field to inquire about specific design plans proposed by the design unit. The design unit quotes the design basis and design reference scheme used to answer questions raised by experts in the field. The safety review sheet contains a large amount of business knowledge and is a good learning material for designers with little design experience.
Implementation scheme of question and answer support system based on security review sheet
First, collect a large amount of safety review order data, including the design plan proposed by the design unit, the questioning questions from experts in the business field, and the design unit's answers. Then, the collected data is preprocessed, including data cleaning, deduplication, format conversion, etc.
In order to improve the accuracy and reliability of the intelligent question and answer system, it is necessary to evaluate and optimize it. You can work with domain experts to collect user feedback, evaluate the system, and optimize it based on the evaluation results.
Since the security review sheet contains a large amount of business knowledge, the intelligent question and answer system can be used as a good learning tool for designers with little design experience. Therefore, user training and support can be provided to help designers make better use of the intelligent question and answer system for learning and work.
The field of aerospace design continues to evolve and change, so intelligent question and answer systems need to be continuously iterated and updated to maintain their accuracy and reliability. Continuous iteration and update of the system can be achieved by regularly collecting new safety review order data, updating the knowledge base, and optimizing system algorithms.
Intelligent search engine technology solution
Implementation principles of intelligent search engines
First, a large amount of text data is collected from various information systems, and then the data is preprocessed, including noise removal, word segmentation, part-of-speech tagging, etc.
Build a knowledge map based on the collected text data. A knowledge graph is a structured semantic knowledge base used to represent entities, concepts and their relationships. Through knowledge mapping, search engines can better understand users 'query intentions.
When a user queries, entity identification is carried out on the query statement to find out the key entities in it. These entities are then linked to entities in the knowledge graph for subsequent semantic retrieval.
By analyzing the semantic structure of query statements and combining the information in the knowledge graph, we can understand the user's query intention. This includes determining whether users want to understand basic information about an entity or the relationships between entities.
Based on query intent, information related to the user's query is retrieved from the knowledge graph. This can be achieved through graph database queries, such as using graph databases such as Neo4j for semantic retrieval.
The retrieved results are sorted by relevance and then displayed to the user in a list form. In addition, some visualization functions can also be provided, such as visual display of knowledge maps, so that users can more intuitively understand the search results.
During the implementation process, some mature technologies and tools can be adopted, such as natural language processing libraries (such as HanLP, Jieba, etc.), graph databases, deep learning frameworks (such as TensorFlow, PyTorch, etc.), etc.
protocol summary
This paper discusses the current situation of digital construction in aerospace overall design units, and proposes a technical solution for unstructured data processing. This solution realizes data sharing and collaboration between systems by building a data integration and sharing platform, and establishes a data governance system to ensure data quality. The use of big data technology for data analysis and mining, combined with visual display, provides strong support for decision-making. In addition, in terms of unstructured data processing, the integrated application of document labeling tools greatly improves the efficiency of document management. The implementation of this technical plan provides aerospace design units with intelligent and automated data processing capabilities, which will help improve work efficiency and promote the sustainable development of the aerospace industry.
The research in this paper not only provides a feasible solution for aerospace design units to process unstructured data, but also provides technical reference and ideas for subsequent digital construction.