227results about How to "Improve retrieval accuracy" patented technology

The invention provides an enhanced document generation and retrieval method based on a knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the following steps: receiving heterogeneous document input, and carrying out dynamic segmentation on the heterogeneous document; constructing a Graph-RAG model, wherein the Graph-RAG model comprises a knowledge graph-document index extraction module and a design document generation module; constructing and updating a knowledge graph based on document segmentation through a knowledge graph-document index extraction module; extracting feature vectors of the document segments by using a feature extraction model, establishing a mapping relationship between the document segments and entities in the knowledge graph through a matching strategy, and constructing a retrieval index; and receiving a query sequence of a user, retrieving the related entities and document segments through the retrieval index, and generating a target design document in combination with a preset template. According to the method, accurate retrieval and high-quality document generation based on semantic understanding are realized, and the technical problems of insufficient semantic understanding, low knowledge association degree, poor generation quality and the like in a traditional document processing method can be solved.

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 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 discloses a retrieval enhancement generation method based on token-level word embedding, and relates to the technical field of artificial intelligence and natural language processing. The method specifically comprises the following steps of: segmenting a text block, namely segmenting a document into paragraphs; enhancing the document information; the text coding is used for converting a text into a numerical vector; segmenting the problem to obtain a character array, encoding the character array, and converting the problem into a numerical vector; calculating the similarity between the problem and the text block by adopting the average maximum similarity to obtain a related text block; performing Prompt construction on the question and the related text block to obtain a prompt text; the prompt text is input into a large language model LLMs, and question answers conforming to the answer format are output; and in combination with the question answers and the related text blocks, quoting and marking the question answers to obtain answers with quotations. According to the method, a token-level text embedding method is utilized to vectorize questions and text blocks, so that the accuracy of RAG retrieval is improved, and the confidence of LLMs answers is increased.

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 a cross-modal image text retrieval method based on deep learning, and provides a novel cross-modal feature extraction and alignment framework, efficient cross-modal retrieval is performed by learning semantic representation of an image and a text in a common feature space, the framework comprises a plurality of modules, and in a feature extraction stage, the cross-modal feature extraction and alignment framework is used for extracting and aligning the images and the texts. BERT Tokenizer and BERT Embedding are adopted for text coding, and the image feature extraction is combined with a Faster R-CNN (Convolutional Neural Network) model and a ResNet-101 model, so that the dimensional consistency of image features and text features is ensured. In the feature alignment stage, fine-grained semantic alignment of image and text features is realized through an I2T Attention module and a T2I Attention module, and the cross-modal matching precision is remarkably improved. In the correlation scoring stage, a correlation scoring matrix is generated by calculating the similarity score between the image features and the text features, so that the accuracy of cross-modal matching is improved. And further optimizing the feature alignment effect through normalization and attention redistribution. In an optimization stage, the system innovatively adopts a shared semantic and sorting loss strategy, integrates an OpenCLIP model framework, efficiently mines a semantic relationship between an image and a text from unlabeled large-scale data through comparative learning, and shows strong transfer learning ability and inference precision.

The invention relates to the technical field of association retrieval, and discloses an internal and external network knowledge association retrieval method based on an RAG technology. The method comprises the following steps: S1, based on query content input by a user, retrieving matched entries from an enterprise internal knowledge base, and reducing a retrieval range through metadata filtering; and S2, converting internal retrieval results into vector representation through an embedded model, and sorting the results according to correlation on the basis of cosine similarity. By combining an enterprise internal knowledge base and external network search, the limitation that a traditional method only depends on a single data source is broken through, the coverage range of knowledge is remarkably expanded, professional and authoritative domain knowledge is provided for the enterprise internal knowledge base, and latest industry dynamic and open information is supplemented by external network search, so that the enterprise internal knowledge base and the external network search are combined, and the enterprise internal knowledge base and the external network search are combined. Through dynamic weight adjustment and a unified sorting algorithm, the most relevant data source can be preferentially selected according to user requirements, so that balance is achieved between content depth and breadth.

The invention relates to the technical field of artificial intelligence, in particular to a model construction method for a GUI agent, a program product and a storage medium, and the model construction method for the GUI agent provided by the invention comprises the following steps: performing pre-training based on a general data set in the GUI field to obtain a base model; a user demonstration operation track is collected online, and a personalized task execution track is obtained; automatically labeling the task execution track to obtain a labeled track; performing task planning training on the base model based on the marked track to obtain a scene planner, and combining the base model and the scene planner to obtain a personalized model; based on the plug-in scene knowledge base, corresponding scene knowledge is retrieved as context supplement during personalized task action planning. The task execution track is automatically labeled, a large model is guided to actively label and understand thinking in human execution operation, the next behavior is further planned, and the adaptive capacity of a GUI intelligent agent to the personalized use environment of a user is greatly improved.

The invention provides a remote sensing image cross-modal retrieval method and device and electronic equipment. The method comprises the following steps: acquiring a user description text, a plurality of remote sensing images and a pre-constructed retrieval model; a remote sensing image feature extraction network based on the model extracts global image features and multi-scale features of each remote sensing image, performs fusion enhancement on the multi-scale features, and fuses the fused and enhanced multi-scale features and the global image features to obtain remote sensing image features; extracting text features of the user description text based on a text feature extraction network; respectively calculating the similarity between the image feature of each remote sensing image and the text feature of the text input by the user; and screening the remote sensing images of which the similarity meets a preset condition from the plurality of remote sensing images to obtain a target remote sensing image. The target remote sensing image highly related to the user description text can be quickly and accurately retrieved from a large number of remote sensing images, so that the retrieval accuracy of the model is improved.

The invention discloses an intelligent question and answer method and system based on semantic vector optimization and dynamic prompt, and the method comprises the steps: inputting a target question into a trained question and answer model, and obtaining an answer corresponding to the target question; wherein the trained question and answer model comprises an input layer, an embedded layer, a semantic vector generation module, a feature compression module, a dynamic prompt generation module, an answer generation module, a linear layer and an activation function layer which are connected in sequence; wherein the output end of the feature compression module is further connected with the input end of the RAG retrieval module, and the output end of the RAG retrieval module is connected with the input end of the dynamic prompt generation module; according to the trained question and answer model, the training process comprises the steps that a semantic vector generation module is constructed, and the semantic vector generation module is pre-trained; and setting the pre-trained semantic vector generation module in the question and answer model, training the question and answer model, and when the total loss function value does not decrease any more, stopping training to obtain a trained question and answer model.

The embodiment of the invention belongs to the field of data processing, and relates to a data retrieval method and device, computer equipment and a storage medium, and the method comprises the following steps: obtaining standard data information; performing feature extraction and semantic representation on the standard data information to obtain semantic vector representation, and constructing a semantic index according to the semantic vector representation; obtaining input retrieval information, and performing semantic understanding and expansion on the input retrieval information to obtain retrieval information representation; converting the retrieval information representation into a retrieval semantic vector, calculating the similarity between the retrieval semantic vector and the semantic index, and screening the standard data information according to the similarity to obtain a candidate data set; and performing priority ranking on the candidate data set to obtain a retrieval result data set, and returning the retrieval result data set to the display interface for display. According to the method and the device, the retrieval result data set with high content correlation and high retrieval accuracy can be effectively obtained according to the input retrieval information.

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 discloses a construction method, system and equipment of a power dispatching knowledge question-answering system and a storage medium. The construction method comprises the steps of obtaining a power dispatching semantic document needing to be processed by the power dispatching knowledge question-answering system; sequentially carrying out character-based segmentation, proposition conversion and proposition combination on the power dispatching semantic document on the basis of the large language model and the related cue words to obtain a plurality of text blocks; based on the large language model and the related prompt words, identifying and extracting entities related to power dispatching and relationships among the entities from each text block; constructing a local graph for each text block on the basis of the large language model and the related prompt words, the entities extracted from each text block and the relationship among the entities; traversing each text block, and combining all the local graphs into a global graph; and on the basis of the large language model and the related prompt words, according to the question type queried by the user, a reply text is correspondingly generated by matching a minimum local graph which is most related to the global graph in the global graph.

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 information retrieval enhancement method and system based on a multi-path recall mode, and the method comprises the following steps: analyzing a user search term to obtain retrieval result data; performing multi-path recall on the retrieval result data to obtain recall information; sorting the recall information to obtain a sorting result; and reordering the ordering result to obtain a final retrieval result. According to the technical scheme, the search result data are obtained by analyzing the user search term; performing multi-path recall on the retrieval result data to obtain recall information; sorting the recall information to obtain a sorting result; reordering the ordering result to obtain a final retrieval result; according to the method, multiple information retrieval strategies are integrated in a flexible and expansible manner, so that the information retrieval precision and efficiency are improved, and the expandability of the information retrieval strategies is not lost.

The invention relates to the field of data processing, in particular to a pedestrian retrieval method and device based on video data, a medium and equipment, and the method comprises the steps: obtaining a first initial collection video according to a first prediction track set, clustering first initial pedestrian segmentation images to obtain a first initial pedestrian segmentation image set, obtaining a first association degree between the first initial pedestrian segmentation image and a target pedestrian by combining a first similarity between the first initial pedestrian segmentation image and a preset image and the number of first video acquisition devices corresponding to the first initial pedestrian segmentation image set, and screening out a first target acquisition video, the influence of interference factors on a retrieval result is eliminated through image segmentation, and the relevance of the same pedestrian in different first initial acquisition videos and the difference between different pedestrians are represented through clustering, so that a first relevance degree is obtained by combining a first similarity and the number of first video acquisition devices; and the retrieval accuracy and the tracking accuracy of the target pedestrian are improved.

The invention discloses a code completion method and device, a storage medium and equipment, and the method comprises the steps: firstly extracting a target code context from a to-be-completed target code file, and extracting a target code snippet from the target code context; then extracting a target embedding vector of the target code snippet, and obtaining a candidate code snippet similar to the target code snippet from a pre-constructed code snippet candidate library based on the target embedding vector; and then, constructing prompt by utilizing the context of the target code and the candidate code snippets, and inputting the prompt into a code generation large language model to obtain a code-complemented target code file output by the model. According to the method, the candidate code snippets similar to the target code snippets are retrieved based on the code snippet candidate library constructed by all the code files contained in the IDE project where the target code file is located, so that the retrieval range is expanded, the retrieval efficiency and accuracy are improved, and the final code completion effect is improved.

The invention relates to the field of data processing, in particular to a data processing method and device for pedestrian retrieval, a medium and equipment, and aims to represent a first clustering reliability degree corresponding to a second initial pedestrian segmentation image set by combining third image similarity between second initial pedestrian segmentation images in the second initial pedestrian segmentation image set. Representing a third correlation degree between the current second initial pedestrian segmentation image and the target pedestrian by combining a first clustering reliability degree corresponding to the second initial pedestrian segmentation image set according to the first clustering reliability degree corresponding to the current second initial pedestrian segmentation image set, the number of track points and the number of devices; therefore, the accuracy of the third association degree is improved, the accuracy of the third target acquisition video is improved, and the retrieval accuracy and the tracking accuracy of the target pedestrian are improved.

The invention discloses an intelligent regulation and control task chain generation method and apparatus, an electronic device and a medium. The method comprises the steps of obtaining content texts of multiple types of regulation and control security regulations and regulation and control operations; inputting the content text into a large language model agent for content extraction so as to extract regulation and control operation sequences and calling contents under various conditions; intelligently generating a knowledge graph in a decision tree form in the knowledge graph through an intelligent agent according to the regulation and control operation sequence and the calling content under various conditions; acquiring a text instruction, performing intention recognition by an intelligent agent, intelligently generating a task chain for regulating and controlling operation logic in the generated knowledge graph after autonomous understanding, and autonomously generating a thinking chain for task execution; and according to the generated thinking chain, the intelligent agent intelligently calls related modules for regulation and control so as to realize intelligent regulation and control. According to the method, the quality and efficiency of obtaining the text information can be improved, the regulation and control thinking chain logic architecture is established by using the decision tree type knowledge graph, the accuracy is improved, and the use threshold of operation is reduced.

The invention discloses a large-scale embedded index data desensitization method suitable for a forestry database, and belongs to the technical field of data desensitization, and the method comprises the steps: obtaining a forestry data description document D1, splitting the document D1 into sentences, and processing the sentences into embedded vectors; a user customizes a query sentence containing the desensitization word, and a query vector is generated; performing hash coding on the embedded vector and the query vector to obtain a corresponding hash function cluster; searching data to be desensitized through cosine similarity calculation; and inputting to-be-desensitized data and the selected desensitization method into the large language model to generate desensitized data, and replacing the corresponding sentences in the D1 to obtain a desensitized forestry data description document D2. According to the method, the input data is converted into the high-dimensional vector through the embedding layer, so that the semantic features of the data can be effectively expressed in the vector space, the retrieval speed and accuracy of large-scale data are improved by means of the locality sensitive hashing technology, the calculation overhead is reduced, and a technical guarantee is provided for real-time desensitization processing of a large-scale data set.

The invention relates to the field of data classification, and discloses an automatic review generation system and method based on artificial intelligence, and the system comprises a retrieval type construction module, a literature screening module, a scale evaluation module, a data extraction and information summarization module, an output and application module, and a system maintenance and update module. Wherein the retrieval formula construction module is used for receiving keywords and retrieval conditions input by a user, generating a retrieval formula and retrieving related literatures according to the retrieval formula; the literature screening module is used for carrying out quality evaluation and screening on the retrieved literatures; the scale evaluation module is used for reading and extracting key data in the screened literature and carrying out scale evaluation; the data extraction and information summarization module is used for summarizing the extracted information and generating a report; the output and application module outputs the generated report; and the system maintenance and updating module is used for carrying out system maintenance and updating. According to the method, the AI automatic construction retrieval formula is realized, the standard is proposed, and the retrieval efficiency and accuracy are improved.

The invention relates to a text attention-based cross-modal representation model construction method and a text attention-based cross-modal representation model retrieval method. Two highly innovative modules, namely an attention guidance-based mask modeling module and a text enhancement module, are added in an online model. Wherein the attention-guided mask modeling module takes the attention in a text coding forward process as a reference and performs self-adaptive masking on words in text description, so that the mask language modeling module can capture semantics associated with masking words for alignment in a masking word prediction process; and therefore, the cross-modal characterization can be aligned more efficiently. The text enhancement module expands text description in a downstream task data set in a lightweight mode, the problems that the text description in the data set is single in word and errors exist are solved, and the retrieval accuracy is improved.

The invention relates to a static and dynamic feature fusion video and text cross-modal retrieval method. The method comprises the following steps: constructing a noun, preposition and verb set for a text; converting nouns and prepositions into text static feature representation Tsf through a text encoder, and converting verbs into text dynamic feature representation Tdf; encoding a video through a video encoder to obtain video static feature representation Vsf; calculating and normalizing an inter-frame difference sequence of adjacent frames of the video, and encoding through a video encoder to obtain a video dynamic feature representation Vdf; calculating the static similarity between the Tsf and the Vsf, calculating the dynamic similarity between the Tdf and the Vdf, and carrying out weighted average on the Tsf and the Vsf to obtain a final similarity score; for a plurality of given texts and videos, k text-video pairs with the highest similarity scores are selected as retrieval results. According to the method and the system, the accuracy and the efficiency of video and text cross-modal retrieval can be improved, and videos related to text description can be effectively retrieved from a large amount of video data.

The embodiment of the invention provides a retrieval method and device based on document space layout, electronic equipment and a medium, and belongs to the technical field of financial science and technology and artificial intelligence. The method comprises the steps that polar coordinate coding is conducted on word segmentation candidate titles and word segmentation candidate paragraphs, Gaussian space layout position calibration is conducted on title word position information and paragraph word position information after polar coordinate coding, calibration word position information is obtained, and retrieval is conducted according to the calibration word position information and retrieval indication information. According to the embodiment of the invention, the initial spatial positions of the paragraph blocks and the title blocks in the document can be preliminarily determined by performing polar coordinate coding on the word segmentation candidate paragraphs and the word segmentation candidate titles, and Gaussian spatial layout position calibration is performed on the paragraph word position information and the title word position information; spatial layout information of words and words can be fused while semantic features of the document are extracted, retrieval is carried out according to position information of calibration words and retrieval indication information, and the accuracy of document retrieval is improved.

The invention relates to the field of artificial intelligence, and discloses an agent-based illegal behavior detection method and device, equipment and a medium, and the method comprises the steps: obtaining a to-be-detected original text; performing text rewriting processing on the original text through a text rewriting agent to obtain a corresponding rewritten text; performing matching retrieval in a preset knowledge base according to the rewritten text to obtain an associated document related to the rewritten text; performing key fragment extraction on the associated document through a fragment extraction agent to obtain a corresponding target fragment; generating a detection task prompt text according to the target fragment and the rewritten text, wherein the detection task prompt text is used for prompting a large language model to execute an illegal behavior detection task on the rewritten text based on the target fragment; and inputting the detection task prompt text into the large language model to obtain a corresponding violation detection result. The illusion of a large language model is effectively reduced, and the accuracy of customer service violation behavior detection in a financial scene is improved.

The invention provides a multi-stage cross-domain image retrieval method and system based on freehand sketch driving, and the method comprises the steps: a first stage is a freehand sketch conversion stage, a generation model is trained by using a freehand sketch and a white ground natural image, and a natural image of a freehand sketch to be queried is generated based on the generation model; the second stage is a feature extraction stage, and features of the generated natural image and a real natural image in a database are extracted; the final stage is a similarity calculation stage, similarity calculation is carried out on the generated natural image and the characteristics of the real natural image in the database, and the real natural image with the highest similarity of the freehand sketch to be inquired in the database can be obtained. According to the method, the free-hand sketch is firstly used for generating the natural image, and then the image retrieval is performed, so that the domain gap between the free-hand sketch and the natural image is reduced, and the retrieval precision of the image retrieval is remarkably improved.

The invention discloses an intelligent retrieval method and system for legal instruments, which is applied to the technical field of legal instrument retrieval, and comprises the following steps: acquiring a to-be-retrieved data text, extracting key information of the legal instruments, including first key information and second key information, and acquiring a legal application scene based on the first key information; based on the second key information, the legal instruments in the legal application scene are retrieved, the similarity between the legal instruments and all the second key information is obtained, and weighted scoring is conducted on the legal instruments by means of the first preset weight of all the second key information, so that display sorting of the legal instruments is conducted; wherein the first preset weight is obtained based on a second preset weight of the second key information; and counting effective retrieval information of the retrieval according to the click condition and the residence time of the legal document, and changing a second preset weight of the second key information for next retrieval. According to the method, the retrieval precision of the legal instruments is effectively improved.

The invention discloses an intelligent data retrieval method based on LLM, and relates to the field of application of large models. Comprising the steps of 1, respectively constructing a text knowledge base and a data resource knowledge base for guiding the analysis direction of a large model, reducing the answer range of the large model and guiding the large model to generate answers, and 2, performing context association on incomplete semantics of a question by using a large model LLM according to the question input by a user to obtain a user question with context information, 3, performing intention recognition and task analysis on the user problem with the context information by using the large model, and determining information required to be queried by the user; 4, performing semantic similarity retrieval in the data resource knowledge base according to a task of large model analysis; and 5, post-processing the retrieved data resources, performing streaming output on a processed result, and performing front-end display.