767results about How to "Improve retrieval efficiency" patented technology

The invention belongs to the technical field of artificial intelligence, and relates to a power document intelligent question and answer method and system based on a large language model. The method comprises the steps of obtaining document data according to a preset period; preprocessing the document data to obtain an adaptive document; constructing a vector retrieval library through an embedded model according to the adaptive document; obtaining a user query question, inputting the user query question into a vector retrieval library, performing retrieval based on a vector similarity retrieval method and a keyword retrieval method to obtain related text blocks, and screening out the most related text block from the related text blocks through a two-stage rearrangement technology; a user queries the question and the most relevant text block and inputs the question and the most relevant text block into the large language model to obtain preliminary intelligent answer information; and correcting the preliminary intelligent answer information based on a preset self-provincial inversion mechanism to obtain final intelligent answer information. Multiple rounds of questions and answers and context understanding are supported based on a correction mechanism, context consistency can be kept in continuous query of complex technical questions, and the accuracy of multiple rounds of questions and responses is ensured.

The invention provides an intelligent retrieval method and system for unstructured asset content based on a large model, and relates to the technical field of data retrieval, and the method comprises the steps: carrying out semantic expansion on retrieval keywords through a knowledge graph enhancement module, obtaining asset feature vectors through a multi-modal feature extraction model, and constructing a hierarchical asset feature index database; the comprehensive similarity is calculated through a weighted fusion algorithm, and retrieval results are optimized and reordered through semantic comprehension and correlation analysis capacity of the pre-trained large model. According to the method, the retrieval precision and efficiency of the unstructured asset content are improved, and intelligent association analysis and semantic understanding of cross-modal assets are realized.

The invention relates to the technical field of image processing, and discloses a detection method, system and equipment for intelligent welding of pipe pile splicing and a medium. The method comprises the steps that noise reduction is conducted on a target weld seam image through a filtering algorithm, registration and splicing are conducted on the image through feature matching, and a weld seam unfolding image is obtained; segmenting the weld joint expanded image by using a deep learning network, and extracting a weld joint center line and surface features to obtain weld joint feature data; defect detection and recognition are conducted on the weld joint feature data, hierarchical calculation is conducted through a rule set, and weld joint quality data are obtained; parameter mapping is conducted according to the weld joint quality data, a welding parameter instruction is generated, welding process variables are adjusted, and target welding parameters are obtained; and the welding seam quality data and the target welding parameters are stored, and a quality report is generated through data analysis. The detection accuracy of intelligent welding for pipe pile splicing is improved.

The invention provides a ship design knowledge retrieval method and system based on a multi-modal knowledge graph. The method comprises the steps of collecting and constructing a ship design set from the ship design field; forming a hierarchical multi-modal knowledge graph for describing a ship design field concept, physical characteristics and design specifications; enhancing association among different modal data through vector similarity calculation; the hierarchical multi-modal knowledge graph is integrated into a ChatGLM model; positioning knowledge nodes related to user demands in the hierarchical multi-modal knowledge graph; matching and positioning the ship design knowledge related to the query content of the user, and feeding back the ship design knowledge obtained by the intelligent semantic retrieval to the user in a hierarchical form. According to the method, ship design knowledge retrieval based on the multi-modal knowledge graph enhanced ChatGLM is realized, and the information acquisition efficiency of engineering designers and the practical application value of the system are improved.

The invention relates to the technical field of medical data management, in particular to a medical data document classification and marking system which comprises a document structure analysis module, a semantic information identification module, a classification and marking module and a document intelligent label module. According to the method, document visualization and retrieval efficiency is improved by analyzing document content and extracting titles, texts and appendixes, analytic performance and operability of data are enhanced by identifying key information and semantic relations and accurately extracting and labeling medical terms, medicine research data and medical record records, storage is optimized according to priorities in combination with classification and marking, and the method is high in practicability and easy to popularize and use. Clinical values and application scenes of documents are clarified, document management processes are refined, management compatible with clinical data formats is formed through structured storage of marked data and classification label generation, storage normalization and retrieval efficiency are improved, the requirement for accurate information calling in clinical tests is met, document management capacity is improved, and the document management efficiency is improved. And support is provided for improvement of clinical decision-making and research efficiency.

The invention discloses an LLM capability enhancement method of a hybrid enhancement strategy, and particularly relates to the technical field of natural language processing. The method comprises the following steps: obtaining corpus data and external literature data of a target application field, carrying out denoising, word segmentation and semantic indexing processing, and establishing a searchable heterogeneous knowledge base; secondly, classifying the corpus data by adopting a topic analysis and word vector clustering method to form a basic corpus set; performing multi-round parameter updating on the large language model by using a domain fine tuning algorithm based on the basic corpus set, and setting a fusion threshold value between a new parameter and an original parameter; and retrieving related literature fragments from the heterogeneous knowledge base through a matching algorithm, screening content conforming to field correlation by utilizing semantic similarity evaluation, fusing the content with the fine-tuned model, processing a fusion result by adopting a context fusion algorithm and an entity linking method, completing syntactic analysis, and generating a precise solution oriented to the target application field.

The emergency decision support method based on cross-domain knowledge fusion comprises the steps of building an emergency semantic model, building an emergency response graph, building an emergency decision auxiliary model based on the graph and the like, a full-cycle emergency disposal knowledge graph oriented to multiple disaster types is provided, and various emergency disposal knowledge can be effectively integrated and stored. Meanwhile, an emergency decision-making auxiliary model is constructed on the downstream based on the atlas, disaster grade division, multi-hop path query analysis and decision-making recommendation can be carried out according to real-time multi-dimensional disaster data, and the cross-domain text retrieval efficiency is greatly improved; meanwhile, a plan chain recommendation model based on an event chain is provided, disaster chain prediction can be carried out based on rules, refined plan pushing can be given, and a vulnerable community is helped to make emergency risk avoiding and planning in advance in a complex scene.

The invention relates to a tree neighbor context-based big language model RAG optimization method, which comprises the following steps of: reading a directory structure and content of a document, generating a corresponding directory tree, converting the directory tree into a Markdown format containing a title and a level, and automatically extracting according to a document template if no title exists; segmenting the document into knowledge blocks according to the priority; after the knowledge blocks are recalled, the context is expanded by using a tree adjacent context method, and a new knowledge block list is formed; grouping and sorting the expanded knowledge blocks according to document identifiers, splicing reference data based on different fields and confidence coefficients, and generating a character string set from the original document; and splicing prompt words by using the constructed reference data and the new knowledge block list, and sending the spliced prompt words to the large model for processing. According to the scheme, the performance and practicability of the large model in the information retrieval and answer generation process are comprehensively enhanced by optimizing context information, emphasizing key content, adapting to various document formats, improving user experience and the like.

The invention discloses a surveying and mapping geographic information data management method and system based on cloud computing, and relates to the field of geographic information, and the method comprises the steps: constructing a semantic segmentation model based on a convolutional neural network Deep Lab, and carrying out the feature extraction and semantic annotation of a surveying and mapping geographic data set through the semantic segmentation model, and obtaining a geographic element set; performing spatial division and indexing on the geographic element set by adopting an R tree spatial indexing method to obtain a hierarchically organized geographic element tree; constructing a graph neural network model based on a graph convolutional network GCN, and performing topological relation modeling on the geographic element tree by using the graph neural network model to obtain a time-space attribute graph containing geographic semantics and topological relations; and constructing a space-time prediction sequence model based on the space-time diagram neural network STGCN, and performing evolution trend prediction on the space-time attribute diagram by adopting the space-time prediction sequence model. Aiming at the problem that dynamic prediction of geographic elements is difficult to realize in the prior art, the spatial-temporal evolution trend of the geographic elements is predicted, and dynamic prediction is realized.

The invention relates to the technical field of intelligent assistant systems, and discloses an intelligent assistant system and method based on an AI technology, and the system comprises a user interaction module, a knowledge graph module, a natural language processing module, and a reasoning and decision module. The user interaction module realizes input and output interaction with a user; the knowledge graph module constructs a multi-domain knowledge system; the natural language processing module uses a BERT model to understand user intentions; and the reasoning and decision-making module fuses rule reasoning and machine learning prediction to realize accurate response. Particularly, an index construction unit is arranged in the reasoning and decision-making module to improve the retrieval efficiency, a reasoning engine adopts a Markov logic network to carry out logical reasoning, and a decision-making optimization unit continuously optimizes a decision-making strategy through reinforcement learning. The AI technology is comprehensively applied, so that the knowledge coverage, the response accuracy, the logical reasoning ability and the decision intelligent level of the intelligent assistant system are remarkably improved, and more efficient and convenient intelligent assistant services are provided for users.

The invention provides an intelligent data retrieval optimization method based on a large model and deep learning. The method comprises the following steps: performing data cleaning, labeling and index construction on original data; performing semantic analysis on user query by using a large-scale pre-training language model to generate deep semantic representation of the query; performing semantic matching in a distributed database according to deep semantic representation queried by a user, and retrieving related high-dimensional feature vectors; classifying and sorting the matched high-dimensional feature vectors through a deep learning network; dynamically adjusting parameters of the pre-training language model and the deep learning network in combination with online learning and a real-time feedback mechanism; and a cache strategy and an index optimization technology are adopted, and a retrieval result most relevant to user query is output. According to the method, by combining an advanced language model and a deep learning technology, efficient and accurate intelligent data retrieval is realized, and the retrieval experience of a user is remarkably improved.

The invention discloses an intelligent retrieval management method and system based on OCR and document analysis, and relates to the technical field of intelligent retrieval management. The system comprises a document scanning and processing module, a semantic analysis and label generation module, a feature extraction and mapping module and a user demand analysis and retrieval module. The document scanning and processing module is used for converting a paper document into an image file by using a scanner and forming a text data set; the semantic analysis and label generation module performs semantic analysis on each text in the text data set to form a semantic label; the feature extraction and mapping module extracts keywords in the text data, generates word vectors of a keyword set, and constructs a mapping matrix of tags and text word vectors; and the user demand analysis and retrieval module analyzes the user demand data, generates a feature matrix queried by the user, evaluates the matching degree between the text data and the user demand, and sorts and outputs retrieval results.

The invention discloses an intelligent file cabinet management method based on the RFID technology, and relates to the technical field of intelligent file management. According to the method, intelligent retrieval is adopted, file positioning is fast and accurate through double indexing of content attributes and RFID tags, and a user only needs to input conditions such as keywords, themes or dates, so that the file can be rapidly and accurately positioned; according to the method, relevant archives can be screened out through similarity matching, the actual storage positions of the archives can be positioned, the retrieval efficiency is remarkably improved, meanwhile, a virtual digital library is established to conduct digital mapping on the archives, and dependence on actual storage space is reduced; under the condition of dense storage, the position information of the archives is recorded through the RFID tags, so that the archives can be flexibly put on a shelf or readjusted in position without influencing the management and positioning of the archives, meanwhile, the archives are digitized when being put in storage, and all content attributes, classification tags and position information are synchronously stored in a virtual digital library; even if the paper archives are actually damaged or lost, the digital information of the paper archives is still completely stored in the digital library.

The invention discloses a personalized digital human image creation and generation system based on deep learning, which relates to the technical field of digital human generation and comprises a multivariate data acquisition module, an image creation module, a model storage module, a matching module and a rendering interaction module. The multivariate data acquisition module collects facial images and whole body photos through a camera, and records voice samples through a microphone. According to the invention, by constructing the multivariate data acquisition module, the image creation module, the model storage module, the matching module and the rendering interaction module, full-process automatic processing from data acquisition to digital human image generation is realized; the multivariate data acquisition module can comprehensively capture the appearance and voice features of a user, and the image creation module extracts feature vectors by using a deep learning technology and generates a personalized digital human image by using a generative adversarial network technology.

The invention provides a multi-stage mixed retrieval method and system. The method comprises the following steps: acquiring an original database; obtaining retrieval content; rewriting the retrieval content by using the large model to obtain N query questions; performing full-text retrieval on the original database by utilizing the N query questions to obtain a first retrieval result; performing vector retrieval on the original database by using the N query questions to obtain a second retrieval result; performing weighted sorting on the first retrieval result and the second retrieval result, screening the sorted retrieval results according to a length threshold value, and defining M retrieval results after screening as final retrieval results; and taking the final retrieval result and the N query questions as cue words, transmitting the cue words to the large model, and outputting an answer result by the large model. According to the method, the advantages of full-text retrieval and natural language retrieval are combined, and the retrieval efficiency and the retrieval accuracy are improved.

The invention relates to the technical field of knowledge base construction, in particular to a knowledge service system construction method, which comprises a knowledge acquisition step for collecting knowledge data from knowledge sources of multiple fields; the preprocessing step is used for preprocessing the knowledge data; the classification step is used for classifying the obtained knowledge data; the similarity calculation step is used for calculating the similarity between different categories of knowledge bases; the first judgment step is used for judging whether the similarity is greater than a first threshold value or not; the association step is used for establishing association between the knowledge according to the similarity; the second judgment step is used for judging whether the similarity is greater than a second threshold value or not; the labeling step is used for constructing a new upper label; a label directory generation step for establishing a label directory; a query demand acquisition step for acquiring a knowledge retrieval demand of a user and generating a query request; the knowledge retrieval step is used for conducting knowledge recommendation according to the query request. The method has the effect of improving the searching efficiency.

The invention belongs to the field of image retrieval, and particularly relates to a multi-scale cross-modal-based satellite remote sensing image text retrieval method and system, a terminal and a medium, and the method comprises the following steps: constructing a multi-source multi-scale cross-modal remote sensing image semantic understanding model; processing a plurality of source satellite remote sensing images by using a multi-source multi-scale cross-modal remote sensing image semantic understanding model to obtain source feature vectors of the source satellite remote sensing images; storing the source feature vectors in a vector library; processing the input image retrieval information or text retrieval information by using the multi-source multi-scale cross-modal remote sensing image semantic understanding model to obtain an image retrieval feature vector or a text retrieval feature vector; and performing similarity comparison on the image retrieval feature vector or the text retrieval feature vector and a source feature vector in a vector library, and screening out a target source satellite remote sensing image. According to the method, the robustness of text and image features is improved, a unified storage and management carrier for multi-source data features is constructed, and the management efficiency and the retrieval precision and efficiency are improved.

The invention relates to the technical field of image recognition, in particular to a neural tumor image analysis and recognition method, which comprises the following steps of: based on an Image Net image data set, performing pre-training of a learning model by adopting a convolutional neural network, obtaining feature recognition capability, performing feature extraction, and generating a model after basic training. According to the method, meta learning, transfer learning, a conditional generative adversarial network, a medical image registration method based on deep learning, synchronous theoretical analysis and a distributed calculation framework are comprehensively utilized, so that the neural tumor image analysis and recognition performance is remarkably improved; and particularly, the method has obvious advantages in the aspects of processing few samples, rare tumor types and multi-modal image data. The deep learning model is pre-trained on a large-scale general image data set, and fine tuning is performed in combination with a meta-learning algorithm, so that the adaptability and learning efficiency of the model to few sample data are significantly improved, and the problem of insufficient accuracy during processing of rare or special phenotype neural tumor images is effectively solved.

The invention discloses a multi-source information fusion query method based on RAG Fusion, which comprises the following steps: receiving original query data input by a user, and carrying out preprocessing and expansion processing on the original query data to obtain an expanded query set; retrieving each extended query in the extended query set in a preset vector library by using a plurality of retrieval strategies to obtain a document set corresponding to each retrieval strategy, and reordering all documents in the document set to obtain a document list; dynamic context construction is carried out on documents in the document list, and a context document set with weights is generated; performing sentence-level segmentation on each document in the context document set, extracting fragments related to the original query data from segmented sentences by utilizing a pre-trained question and answer model, and performing duplicate removal and combination on the extracted fragments to generate an information unit set; and inputting the original query data, the context document set and the information unit set into a pre-trained large-scale language model to generate a target answer.

The invention discloses an operation and maintenance platform data acquisition system based on big data, and relates to the technical field of big data, and the system comprises the steps: capturing a data packet from a network flow of an operation and maintenance platform, and storing the data packet in a buffer area; performing preliminary screening on the data packets in the buffer area, and allocating priorities for the data packets based on a dynamic weight scheduling mechanism to complete preliminary preprocessing of the data packets; based on a circular sorting and block optimization technology, distributing the plurality of decomposed analysis tasks to a plurality of threads for parallel processing; formatting the analysis result, and performing batch parallel processing according to the data scale of the analysis result; formatting the analyzed data, writing the formatted data into a time sequence database according to a time sequence, and generating an index based on the tag field; through a dynamic cache management mechanism, the cache size is dynamically adjusted according to the throughput and the maximum delay of the network flow, and the problems of data loss and processing delay are avoided.

The invention discloses an RFID reader-writer intelligent fault diagnosis method and system based on a knowledge base retrieval enhancement large model, and the method comprises the following steps: carrying out the multi-level segmentation of an RFID reader-writer fault knowledge base document, obtaining an RFID fault text, extracting an RFID fault text vector from the RFID fault text, and storing the RFID fault text vector in a vector database; converting the received query problem text into a query problem vector, and retrieving an RFID fault text vector in the vector database according to the degree of correlation with the query problem vector to obtain a target RFID fault text; and generating a prompt statement and prompt information according to the context prompt word of the target RFID fault text and a preset prompt template, inputting the prompt statement and the target RFID fault text into a large language model to generate a diagnosis suggestion, and displaying the prompt information and the diagnosis suggestion to a user. The technical problems of low text retrieval efficiency and poor accuracy in the background art can be solved.

The invention provides a garden soil organic pollutant degradation method using rhizosphere microbial communities, and relates to the technical field of soil remediation, and the method comprises the following steps: matching strain parameters according to pollutant types and concentrations, and determining a plurality of strain type thresholds and strain density thresholds; matching plant parameters according to pollutant types, pollutant concentrations and environmental conditions, and determining a plurality of plant type thresholds and plant density thresholds; and based on a plurality of strain type thresholds, strain density thresholds, plant type thresholds and plant density thresholds, executing remediation scheme optimization of the pollution distribution area, and determining an optimal remediation scheme. By means of the method, the problems that a traditional method mainly depends on literature retrieval and data lookup to conduct soil remediation analysis, useful information cannot be quickly retrieved, the matching degree of the retrieved information and the soil degradation requirement is low, and consequently the precision and practicability of soil remediation scheme setting are poor can be solved; and the soil remediation efficiency and remediation quality can be effectively improved.

The invention provides a large model service system and method, computer equipment and a readable storage medium. A cue word engineering module configures a cue word template; generating a target text according to the prompt word selected by the user; the retrieval enhancement generation module receives a basic text uploaded by a user, converts the basic text into word vectors, stores the word vectors in a database, and retrieves prompt words from the database according to question information input by the user to generate a target text; the plug-in development module receives the configuration file, analyzes the configuration file to obtain API interface information, calls extended data to retrieve cue words, and generates a target text based on the cue words; and the agent module defines the execution combination and sequence of the cue word engineering module, the retrieval enhancement generation module and the plug-in development module according to the user-defined operation of the user on the system execution chain. By adopting the method, the flexibility of large model service, the efficiency of model retrieval and the safety of model service are improved, diversified business scene requirements are further met, and the user experience is improved.

The invention provides a semantic similarity analysis method for science and technology project management, and belongs to the technical field of information, the semantic similarity analysis method for science and technology project management comprises the following steps: preprocessing collected project documents, including text cleaning, word segmentation and stop word removal; extracting vocabulary features in the document, and converting the vocabulary features into numerical vector representation; calculating the semantic similarity between the documents or terms; and applying a similarity analysis result to document management and information retrieval: performing automatic preprocessing on the document, including text cleaning, word segmentation, stop word removal and standardization processing, so that a system can quickly prepare document data, prepare for subsequent analysis, automatically extract vocabulary features in the document by using an advanced word embedding model (such as Word2Vec, GloVe or BERT), and perform data processing on the document. And the information is converted into a numerical vector for representation, so that the requirement of manual intervention is greatly reduced.

The invention discloses a media file query and retrieval method and system based on an AI dialogue scene, the method combines a mixed mode of vector retrieval and keyword retrieval, realizes accurate retrieval of cross-modal data, and can process various media files including texts, pictures, videos, PPT and the like. According to the method, a double-round scoring mechanism of a large language model (LLM) is combined, firstly, the relevancy of file content is scored, then secondary scoring is carried out according to the matching degree of a file title and user query, and it is ensured that the file most relevant to query is called. In addition, the system can further utilize a user feedback mechanism to continuously optimize the retrieval precision, and the user experience is improved. Compared with the prior art, the method has the advantages that the accuracy and the efficiency of multi-modal data retrieval are remarkably improved, the limitation of a traditional AI dialogue system during multimedia file processing is solved, and the method is suitable for intelligent dialogue application scenes needing to quickly and accurately obtain various types of media files.

The invention relates to the technical field of optical disk storage, in particular to an optical disk box storage management system based on hierarchical management, which comprises a data analysis module, a weight calculation module, a strategy making module, a medium selection module, a dynamic adjustment module and a performance evaluation module. According to the method, the optical disk data is deeply analyzed, and the access frequency, the file number and the modification frequency of the data are recorded, so that the storage requirement and the change of a data access mode can be more effectively identified, the accurate recording and weight distribution of data attributes and the sensitivity analysis of optical disk files are realized, and a more detailed storage management strategy is constructed; storage positions and priorities can be adjusted according to actual use conditions of data, real-time data requirements are responded, a mechanism is adjusted in real time to improve response speed and operation efficiency of a system, continuous adaptability and efficiency of a storage strategy are ensured through regular performance evaluation, and reliability and safety of long-term data storage are guaranteed.

The invention provides a multi-agent cooperation method and system for an engineering construction project, and relates to the field of railway engineering management, and the method comprises the steps: training a railway engineering field expert model; dialogue information input by a user is processed by the expert model to generate a prompt instruction; the content perception intelligent agent generates a query statement according to the prompt instruction and returns the query statement to the expert model; the expert model calls a mixed retrieval agent for retrieval according to the query statement; the chart generation agent generates a chart according to the chart making request and returns the chart to the expert model; the data analysis agent performs data analysis according to the data analysis request and returns the data to the expert model; and integrating and outputting the retrieval result, the chart and the analysis result by the language question and answer agent. According to the method, various agents can be effectively cooperated to exert respective advantages, the retrieval speed and accuracy are improved, and a more comprehensive and understandable retrieval result can be output according to the input natural language.

The invention provides an agricultural park energy supply and consumption system regulation and control strategy generation method driven by a knowledge graph, and the method comprises the steps: extracting time series data feature indexes of structured data, completing the interval tagging processing, and forming a structured data feature tag library according to the complex and diversified scheduling operation demands of an agricultural park energy supply and consumption system; and combining knowledge extraction, fusion and storage of other semi-structured data and non-structured data to construct an agricultural park energy supply and consumption system knowledge graph. According to the method, the input feature information matrix is formed on the basis of the characterization processing of the to-be-optimized operation scene of the agricultural park energy supply and consumption system and in combination with the basic information, so that the corresponding historical strategy stored in the knowledge graph is retrieved layer by layer from the to-be-optimized operation scene, the system information visualization and the interactive query are realized, and meanwhile, the user experience is improved. Intelligent generation of the regulation and control strategy of the energy supply and consumption system in the agricultural park is completed, and support is provided for intelligent decision making of the energy supply and consumption system in the agricultural park.

The invention relates to an information retrieval method and device, equipment, a readable storage medium and a program product. The method comprises the steps that under the condition that an information query statement input by a user is received, multiple pieces of metadata are extracted from the information query statement; taking the plurality of metadata as filtering conditions, and performing retrieval in a dialogue record database to obtain a plurality of target topic segments; each target topic segment comprises a plurality of dialogue records and corresponding topics; determining an information query prompt word based on the plurality of target topic segments; and based on the information query cue word and the large language model, obtaining an information retrieval result generated by the large language model. By adopting the method, the accuracy of a retrieval result can be improved.

The invention discloses a knowledge base information processing method and device, a storage medium and computer equipment, relates to the technical field of information processing, and mainly aims at solving the problems that an existing literature retrieval method is low in efficiency, poor in retrieval result correlation and low in accuracy. Obtaining standard text data; building a journal paper knowledge base based on the standard text data; performing vector representation processing on the standard text data, and constructing a mixed index corresponding to the standard text data based on the obtained knowledge vector; receiving a query text of a user, and rewriting the query text based on a preset hybrid retrieval strategy to obtain a target query text; and determining target journal paper data corresponding to the target query text from the journal paper knowledge base based on a pre-trained retrieval enhancement generation model and the mixed index, and generating a reply text based on the pre-trained retrieval enhancement generation model and the target journal paper data.