3results about How to "Query speed" patented technology

The application discloses a kind of chip finding method, system and storage medium based on knowledge graph, method includes: crawling chip model, chip original factory and 3 kinds of entity information of application field, obtain data source;The unstructured information in data source is structured, and structured information is inserted into knowledge graph;Chip knowledge graph database is constructed;According to the query requirement of user, requirement integration, analysis, translation are carried out;Chip knowledge graph database returns the query result to user.The system includes: crawler module, data processing module, chip knowledge graph, requirement acquisition module, finding module and output module.The application is based on knowledge graph technology, and information such as chip, manufacturer and application field is collected into graph, and the user can find the required chip from multiple dimensions, especially in the case of incomplete product information, targeted search can be carried out, with high search efficiency and accurate search results;It is conducive to the popularization and application of semiconductor chips.

The invention discloses a log data compression and retrieval method and electronic equipment, and the method comprises the steps: obtaining to-be-compressed log data, dividing vocabularies, traversing the vocabularies, and searching a current traversed vocabulary in a dictionary tree; if so, obtaining a target ID mapped in the dictionary tree; determining a target hash value and searching a corresponding linked list; if a node for recording the storage information of the log data exists in the linked list, triggering compression, otherwise, adding a corresponding node in the linked list and then triggering compression, and generating an alarm when abnormity occurs based on the frequency of vocabularies in the dictionary tree; when the compression is triggered, replacing the current vocabulary with the target ID; and traversing the next vocabulary until traversing is finished, updating corresponding storage information, determining an ID in the dictionary tree according to the retrieved vocabulary during retrieval, searching the corresponding linked list, and decompressing log data corresponding to the storage information in the linked list by utilizing mapping of the ID recorded in the linked list to the vocabulary. Therefore, the problems of complex data management and high maintenance cost caused by separation of compression and index in the prior art are solved.

The application discloses a cross-document type retrieval method and system based on differential nested coding. The method includes six steps: document preprocessing, document type identification, differential coding, index construction and optimization, online retrieval and result fusion. First, the document is standardized and preprocessed to extract structured information. Second, the BERT classifier is used to analyze the document features and identify the document type. Then, according to the document type, the corresponding encoder is selected to generate the original coding vector. Next, the differential nested coding index is constructed and optimized. In the online retrieval stage, the user query is preprocessed, the relevant document type is predicted, and the approximate nearest neighbor retrieval is performed. Finally, the retrieval results are sorted by a stepwise weight fusion strategy, and the top-100 retrieval results are returned. Through differential coding and index optimization, the application realizes efficient retrieval of different types of documents, and has significant practical value and application prospect.