158results about How to "Improve retrieval speed" patented technology

The invention discloses a method for constructing a knowledge graph based on a large language model and a vector library, and relates to the technical field of knowledge graphs, and the method comprises the following steps: carrying out word segmentation, part-of-speech tagging and semantic analysis processing on a collected natural language text by using a pre-trained RoBERTa model to obtain a semantic representation vector of the natural language text; through a Seq2Seq framework and an attention mechanism, obtaining association between the semantic representation vector and the corresponding named entity, and obtaining the named entity through the obtained association; constructing a knowledge graph; fusing the constructed knowledge graph with a relational database of a preset field to generate a fused knowledge graph; named entities and corresponding attributes are obtained through dependency syntax analysis, the entity relation of the named entities is obtained, and a structured triple fusing the named entities, the attributes and the entity relation of the knowledge graph is constructed. In order to solve the problem of high entity linking difficulty in the prior art, the knowledge graph is converted into low-dimensional dense vector representation, and an inverted index database and the like are constructed, so that the difficulty is reduced.

The invention relates to the technical field of database retrieval, in particular to a knowledge database retrieval method based on feature extraction. The method comprises the following steps of obtaining a document to be retrieved, performing local hash de-duplication processing and extracting multi-modal features, performing feature spectrum clustering processing and associated entity analysis on the extracted features, generating a feature associated entity structure, performing document topic modeling and semantic direction positioning, and extracting a semantic topic of the document. The method comprises the following steps: performing word vector retrieval on a to-be-retrieved document and a knowledge database by utilizing a document semantic topic, generating word vector retrieval data, performing sentence vector retrieval, generating a final document retrieval result, integrating word vectors and the document retrieval result, and executing feature retrieval of the knowledge database through an intelligent retrieval engine, thereby realizing efficient knowledge query. The knowledge database retrieval method is more intelligent and more accurate.

The invention provides a large language model intelligent interaction method and system with memory management capability in the technical field of artificial intelligence. The method comprises the following steps: S1, creating a work memory module, a long-term memory module, a memory fusion module and an intelligent interaction module; s2, acquiring external knowledge and integrating the external knowledge into a long-term memory module; s3, the intelligent interaction module obtains an intention sequence based on a user query statement and sends the intention sequence to a work memory module; s4, the memory fusion module retrieves context information from the long-term memory module based on the user query statement and sends the context information to the working memory module; s5, the work memory module constructs a work memory sending intelligent interaction module based on the intention sequence, the context information and the system instruction; s6, the intelligent interaction module outputs a query result based on the working memory; s7, a memory fusion module extracts semantic memory and plot memory from user query statements and query results and stores the semantic memory and plot memory in a long-term memory module. The method has the advantage that the man-machine interaction performance is greatly improved.

The invention relates to an optimization method of retrieval, in particular to an optimization method of index retrieval of a digital library, which adopts the following technical scheme: S1, a dynamic hierarchical index construction technology: firstly, performing multi-dimensional content analysis on digital resources in the library, including themes, keywords, authors and publishing time information; classifying the literatures according to the information, and constructing a content hierarchical structure; s2, an index optimization strategy based on a use mode: performing data analysis on a retrieval behavior of a user, monitoring an access frequency, a query type and an access path of the user in different types of literatures, and identifying a typical user use mode; s3, performing semantic association retrieval optimization; and S4, index updating and resource allocation optimization: when new literature resources are added into a library or existing resources are modified, updating the index in real time, and adjusting an index structure according to content classification and user behaviors.

The invention discloses a context learning-based database query generation method and system and a storage medium, and the method comprises the steps: extracting keywords and phrases from a natural language query, retrieving values similar to the keywords in a database, and returning values most related to the keywords and corresponding columns thereof, retrieving description information related to the keywords and the phrases in a database directory; performing mode selection aiming at the most relevant value of each keyword and the corresponding column, namely screening out the column irrelevant to the natural language query, further selecting a table necessary for generating the SQL query, and finally reducing the mode to a minimum column set necessary for generating the SQL query; and correcting logic and grammar errors of the method model through the database mode, the natural language query, the generated candidate SQL query and the execution result thereof. According to the method, the efficient SQL query statement is generated by intelligently understanding the data context and the query requirement.

The invention discloses an advertisement compliance knowledge question-answering method, device and equipment for foreign enterprises, and the method comprises the steps: obtaining advertisement compliance data of a global data source, carrying out the document structuring and knowledge extraction of the advertisement compliance data, and constructing an advertisement compliance knowledge graph; converting text data in the advertisement compliance knowledge graph into vectors to obtain vector data; semantic analysis is conducted on a query question input by a user, construction is conducted in combination with the vector data, prompt words are obtained, the prompt words are input into a preset large language model, and target answers are obtained; and outputting the target answer based on the advertisement compliance knowledge graph, and visually displaying the target answer in a corresponding answer query graph. And comprehensive and efficient compliance information query and answer services can be provided, so that compliance review requirements of foreign enterprises on advertisements in a multi-language and cross-regional environment can be met.

The invention relates to the technical field of document data retrieval, and discloses an archive data retrieval method, system and device.Firstly, multiple pieces of archive data in a system are cleaned and standardized, document data are preprocessed, and document data processing specifically comprises text data, picture data and video data; semantic representation is carried out on the processed archive data, specifically, a deep learning model is used for creating semantic vector representation for a document; the expressions of the text, the image and the video are fused into a unified vector space, data of various data types of the text, the image and the video are integrated, and cross-modal retrieval is carried out; and outputting a retrieval result according to the similarity ranking. According to the method, the query intention of the user and the content of the document can be more accurately understood, keyword extraction and sentiment analysis can be carried out, the system can be helped to more comprehensively analyze the document, and therefore the correlation of retrieval results is improved.

The invention discloses a data storage method and device for an electric power internet of things and a storage medium, and the method comprises the steps: carrying out the preprocessing of system operation data when the real-time system operation data of the electric power internet of things is collected, determining the priority of the system operation data if the preprocessing is completed, and storing the priority of the system operation data in a storage medium; and storing the system operation data in the storage hierarchy matched with the priority, adjusting the priority of the system operation data according to the storage information and the access information of the system operation data in the storage hierarchy, and migrating the system operation data to the storage hierarchy matched with the adjusted priority. Real-time system operation data collected by the electric power internet of things is preprocessed, the priority of the real-time system operation data is determined, the system operation data is stored in a matched storage hierarchy, and the electric power internet of things can preferentially process and store important and frequently-accessed system operation data, so that the system operation data retrieval and analysis speed is increased, and the system operation data retrieval and analysis efficiency is improved. And the operation efficiency and stability of the power Internet of Things are improved.

The invention relates to the technical field of bioinformatics, in particular to a gene pool data retrieval method and system and a storage medium. The method comprises the following steps: acquiring multi-source genome data, performing quality control on the multi-source genome data based on an FAIR principle, and performing efficient storage and indexing by utilizing a distributed storage system so as to obtain a distributed genome database; performing cross-species homology analysis on the distributed genome database, and constructing a multi-level evolutionary tree model, so as to obtain evolutionary relationship map data; mapping gene structures and functions among different species according to the evolutionary relationship map data so as to obtain cross-species gene mapping data; receiving retrieval query data input by a user; and performing query type identification on the retrieval query data to obtain optimized query data. Through the multilevel evolutionary tree model and cross-species gene mapping, gene function comparison and prediction among different species are realized.

The invention discloses a judicial field class case retrieval method and system based on a large model. The method comprises the following steps: constructing a judicial document vector database by combining a vector model with an obtained judicial document; performing semantic recognition and question enhancement on judicial case or judicial question texts through the large model; generating vectors for the enhanced judicial case or judicial question text through a vector model, and performing similarity retrieval in a judicial document vector database to obtain a primarily screened judicial document set; reordering the primarily screened judicial document set through a Reranker model to generate a finely arranged judicial document set; and inputting the enhanced judicial case or judicial problem text and all judicial documents in the finely arranged judicial document set into a large model for correlation analysis, outputting the judicial documents most related to the judicial case or judicial problem text, and sorting the judicial documents according to similarity to form a data set as a retrieval result. According to the method, the accuracy and efficiency of class case retrieval can be improved.

The invention discloses a shop commodity recommendation method and device, equipment and a medium, and the method comprises the steps: obtaining a search text inputted by a user in response to a search request of the user; extracting a keyword of the search text, and determining a corresponding category according to the search text; in the first stage, shop level recall operation is executed on the shop feature library, and part of shops with shop features matched with the keywords and the category to which the shops belong are recalled to form a shop recommendation list; and in the second stage, performing a commodity level recall operation on the commodity feature data of the commodities of the shops in the shop recommendation list in the commodity feature library, and recalling a part of commodities of which the commodity features are matched with the keywords and the categories to which the commodities belong, so as to form a commodity recommendation list. According to the method, the system pressure is effectively reduced through two stages of level recall operations.

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 discloses a similar surgery picture retrieval method, system and equipment for minimally invasive surgery based on deep learning and a medium, and relates to the technical field of image processing. Respectively extracting original image features in the preprocessed video stream and original text features in the preprocessed text content; performing interactive fusion on the original image features and the original text features by adopting a feature fusion module; using a feature codebook module to map the fusion feature into a codebook vector; a mapping feature decoding module is adopted to decompose the codebook vector into compressed image features and compressed text features, finally, the compressed image features are decoded into similar images, and the compressed text features are decoded into text descriptions corresponding to the similar images; and the operation picture can be quickly and accurately retrieved.

The invention relates to the technical field of archive storage and management, and discloses an archive classification management method which comprises the following steps: constructing an alliance chain property block chain network and an I PFS file system; for the received multi-dimensional electronic archive set, performing clustering analysis on the electronic archive set to obtain an archive classification result of the electronic archive set; based on the number of the electronic archives in the archive classification result, the storage nodes are classified to obtain node classification results, and the node classification results are in one-to-one correspondence with the archive classification results; according to a node classification result, uploading and storing the classified electronic archive file through the corresponding storage node, generating a hash ID table related to the storage node, and classifying the hash ID table; and the block chain network generates the Hash ID table into a block according to the category and a block chain network operation mechanism, and stores the block into the block chain network.

The invention relates to a network protocol information retrieval method and device, computer equipment, a computer readable storage medium and a computer program product. The network protocol information retrieval efficiency can be improved. The method comprises the steps of obtaining a retrieval keyword in response to a network protocol information retrieval request; acquiring a node index and a node relation index corresponding to the information knowledge graph; the information knowledge graph comprises a first node corresponding to the network protocol information and an association relationship between the nodes, the node index records the first node associated with the keyword, and the node relationship index records an association relationship between the first node and other associated first nodes; matching the retrieval keyword with the node index, determining a second node associated with the retrieval keyword, and determining a third node and a node association relationship between the second node and the third node according to the node relationship index; and generating a node connected graph according to the second node, the third node and the node association relationship to obtain a retrieval result.

The invention discloses an abdominal cavity postoperative infection trend prediction system and method, and relates to the field of abdominal cavity postoperative infection prediction.The abdominal cavity postoperative infection trend prediction system comprises a data acquisition module, a data processing module and a data processing module, the comprehensive nursing information of the patient is continuously collected at n time points at a fixed frequency p; the fitting analysis module is used for constructing an original data matrix, and performing statistical feature analysis on the original data matrix through a multi-dimensional dynamic sliding window to obtain a feature data set; the model prediction module is used for constructing and training an infection trend prediction model, importing the characteristic data set of the patient in the current time period into the trained infection trend prediction model, obtaining the infection trend prediction information of the next time period, predicting the infection degree, the infection symptom and the infection reason in the next time period, and outputting the prediction result. And intelligent prediction of the postoperative infection trend of the abdominal cavity is realized.

The human cost data storage method for the power grid engineering system comprises the steps that human cost data related to cost data in the power grid engineering system are acquired, and the human cost data comprise work type data, man-hour data and salary data of constructors; determining a DBSCAN clustering radius range based on the average man-hour salary of each type of work; based on a DBSCAN clustering radius range, performing clustering analysis in a man-hour-salary two-dimensional plane formed by the human cost data to obtain a preliminary classification result; when it is determined that the isolated data points exist based on the preliminary classification result, secondary classification is carried out on the isolated data points based on the distance between the isolated data points and the known clustering cluster, and a final classification result is obtained; and realizing partition storage of the cost data based on a final classification result. By implementing the method, optimized storage of the cost data of the power grid engineering system can be realized.

The invention discloses a block chain Filecoin data analysis platform, which is characterized in that user data is stored on a plurality of nodes in a distributed manner by utilizing a decentralized storage characteristic of a Filecoin network, and the tampering resistance and security of the data are ensured through encryption and a consensus mechanism of a block chain; according to the method, an efficient block chain transaction processing and data analysis algorithm is adopted, a data analysis task is automatically executed through an intelligent contract, the use of data transmission and computing resources is optimized, and the efficiency of data processing and analysis is greatly improved; the advanced encryption technology is adopted to perform end-to-end encryption on the data, and a strict access control strategy is implemented to protect the privacy and security of the user data; all operation records and data analysis results of the platform are recorded on the block chain, so that the transparency and traceability of an analysis process are ensured, the analysis results cannot be tampered and can be verified, and the trust of a user to the analysis results is enhanced.

The invention relates to the technical field of environmental monitoring, and discloses an exclusive encrypted file format of a universal water-gas-sound-residue pollutant data acquisition instrument. File structures including a file header setting section, a data storage format definition section, a self-checking section and an encryption algorithm section are sequentially stored in a file to form an exclusive encryption file format of the universal water, gas, sound and residue pollutant data acquisition instrument, various encryption and checking measures are adopted, and the safety and integrity of data in the transmission and storage process are ensured. The method has the advantages that data are stored in the database and prevented from being tampered and stolen, data storage efficiency and retrieval speed are increased due to reasonable data storage formats and index designs, data management and analysis are facilitated, file damage or data tampering can be timely discovered through CRC32 check and data integrity check codes, data reliability is guaranteed, and data storage efficiency and retrieval speed are increased. And on the basis of an XML structured storage mode and an open file format design, subsequent addition of new pollutant parameters or function extension is facilitated, and good expandability is achieved.

The present application discloses a method for constructing a knowledge graph based on a large language model and a vector library, which relates to the field of knowledge graph technology, including: using a pre-trained RoBERTa model to perform word segmentation, part-of-speech tagging and semantic analysis on the collected natural language text to obtain a semantic representation vector of the natural language text; obtaining the association between the semantic representation vector and the corresponding named entity through the Seq2Seq framework and the attention mechanism, and obtaining the named entity through the obtained association; constructing a knowledge graph; fusing the constructed knowledge graph with a relational database of a preset domain to generate a fused knowledge graph; using dependency syntactic analysis to obtain the named entity and the corresponding attribute, obtaining the entity relationship of the named entity, and constructing a structured triple of the named entity, attribute and entity relationship of the fused knowledge graph. In view of the difficulty of entity linking in the prior art, the present application converts the knowledge graph into a low-dimensional dense vector representation, and constructs an inverted index library, etc., to reduce the difficulty.

The embodiment of the invention provides an archive management method and system based on big data analysis, and belongs to the technical field of data processing. The method comprises the steps of obtaining initial text data and initial image data corresponding to an initial user; performing deduplication processing on the initial text data to obtain target text data, and performing deduplication processing on the initial image data to obtain target image data; performing association relationship analysis on the target text data and the target image data to obtain a target relationship corresponding to the target text data and the target image data; performing text classification on the target text data to obtain a target type corresponding to the target text data; and performing data storage on the target text data and the target image data according to the target type and the target relationship to obtain a target storage result. The problems that in the prior art, the occupied storage space is increased and the data access and retrieval efficiency is reduced due to the fact that texts and images are often used as independent information units to be processed and an effective integration mechanism is lacked are solved.

The invention discloses a document key information retrieval method based on a large language model, a retrieval result is output according to a multi-stage input document and a question input by a user, and the method comprises the following steps: constructing a domain knowledge graph; constructing and training a question extraction model, and obtaining key information and intention of the question input by the user through the question extraction model; retrieving in a knowledge graph according to the key information and the intention to form paragraph description of the key information; screening the document and splitting the document into a plurality of text blocks to obtain a plurality of text blocks most related to the content input by the user; obtaining a corresponding text block with the highest embedding similarity by using a large language model; and integrating and reasoning the corresponding text blocks and paragraph descriptions by adopting a large language model to obtain information about the user input question in the document. The method has the beneficial effects that the accuracy and efficiency of information extraction can be improved, specific requirements of different fields can be met, and powerful technical support is provided for document automatic processing and knowledge management.

The invention provides an unstructured data retrieval method, device and equipment and a readable medium, and is applied to an unstructured data retrieval system, the unstructured data retrieval system at least comprises a gateway layer, a business service layer and a data processing layer, and the data processing layer is utilized to obtain unstructured data; converting the unstructured data into vectors by using a data processing layer, and storing the vectors into a vector storage library; acquiring a retrieval request of the unstructured data by utilizing the gateway layer, and sending the retrieval request to the business service layer; sending the retrieval request to a data processing layer by using a business service layer; based on the vector storage library, retrieving the to-be-retrieved data in the retrieval request by using the data processing layer to obtain a retrieval result; the retrieval results are screened through the business service layer by using the similarity script to obtain a target retrieval result, and the target retrieval result is sent to a gateway layer; and the target retrieval result is output by utilizing the gateway layer, so that the problems of relatively high cost and relatively poor effect of manual weight adjustment are avoided.

The invention discloses an intelligent monitoring and regulation system for a connector, and relates to the field of monitoring data processing, and the key points of the technical scheme are that a data acquisition module acquires comprehensive working data and environmental data of the connector; the data optimization module is used for preprocessing the comprehensive working data, and the preprocessing modes comprise data synchronous calibration, weighting processing on repeated current values or voltage values existing at a single time point, and interpolation method filling on data missing at the single time point; obtaining adjustment data; the feature analysis module is used for performing singular spectrum analysis SSA on the adjustment and calibration data to obtain feature data; and the prediction regulation and control module inputs the feature data into a pre-trained neural network model, a regulation and control decision of the connector is obtained through prediction, all the modules are connected in a wired and / or wireless mode, and intelligent monitoring and regulation and control of the connector are achieved.

The invention discloses an enterprise information data retrieval system, which belongs to the technical field of information data retrieval, and comprises a data storage module, a data retrieval module, a permission endowing module, an index guide module and an associated data supplement module, the data storage module is used for respectively establishing enterprise storage databases for storing enterprise related data, the data retrieval module comprises a retrieval algorithm and is used for quickly retrieving the data in the data storage module according to information input by a user, and the authority endowing module. According to the enterprise information data retrieval system, the functions of information retrieval, keyword extraction, data compression, integrated data storage, authority control, index optimization and external data supplementation can be achieved, a comprehensive, efficient and safe information retrieval platform is provided for an enterprise, employees inside the enterprise can quickly retrieve information conveniently, and the enterprise efficiency is improved. And the response speed of information retrieval is improved.

The invention discloses a comprehensive archive management system and management method, and relates to the technical field of archive management, the comprehensive archive management system comprises a data entry module, an analysis adjustment module, a picture processing module, a data processing module and a data storage module, and the matched comprehensive archive management method is provided. According to the method, analysis processing and angle adjustment processing are carried out on the obtained archive picture, the angle of the picture is corrected, follow-up checking by related technicians and computer recognition are facilitated, then stain removal processing is carried out, so that the content on the picture is clearer, character filling processing is further carried out, the obtained archive content is complete, and the efficiency is improved. In general, clear and complete archive picture data can be obtained, and the method has a good use prospect.

Embodiments of the invention provide a document processing method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a first text of a current dialogue; extracting N first features of the first text; splicing the N first features of the first text into a second feature; acquiring a first hash code of the second feature; in a current document extension tree, a second hash code is retrieved, the similarity between the second hash code and the first hash code is larger than or equal to a first threshold value, the document extension tree comprises a plurality of nodes, each node is associated with at least one document, and the hash codes of the documents associated with the same node are the same; under the condition that the second hash code is retrieved, a document associated with the first target node is determined as a document matched with the current dialogue, and the first target node is a node to which the second hash code belongs; and adjusting the first target node as a root node of the document extension tree. Therefore, according to the embodiment of the invention, the document retrieval speed can be increased.

The invention discloses a lightweight key management method and system based on a national cryptographic algorithm, and relates to the technical field of key management, comprising a user creation process, an information loading process, a secure connection process and a key distribution process. Wherein the key management server creates an SM4 white box for an intra-domain user, physically distributes the SM4 white box, creates a user according to a user name and a user password, and generates user information; user information is loaded and stored in a hash bucket, an SM2 public key of a user is obtained, and the shared key is decrypted by using an SM4 white box; the method comprises the following steps: receiving a Hash bucket, loading local information, decrypting, creating a thread to communicate with a user, searching user information in the Hash bucket after receiving a data packet sent by the user, verifying the user information, decrypting key information by using a shared key, and negotiating to obtain a session key so as to establish a secure connection; and after a key distribution request sent by a user is received, searching user information in the hash bucket and verifying the user information, decrypting the data packet by using the session key, generating a key distribution data packet and replying the key distribution data packet to the user.

The invention provides a power grid system network topology construction method and related equipment. The method comprises the following steps: determining composition structures of a power grid system, entities of different composition structures and entity attributes; determining a connection relationship between the entity and the entity attribute corresponding to the entity, and determining the connection attribute of the connection relationship; wherein the connection attribute represents description of the connection relation; taking the entity and the entity attribute corresponding to the entity as nodes, uniformly arranging the nodes, taking the connection relation as an edge, adding the connection attribute to the edge, and determining a first topological graph of different composition structures; and determining connection nodes among the different composition structures and connection relationships among the connection nodes, and combining the first topological graph according to the connection relationships to form the network topology of the power grid system. According to the method, a power grid system is divided into different composition structures, topological graphs are established for the different composition structures, and finally integration is performed to realize visual display of the network topology of the power grid system.

The embodiment of the invention discloses a data retrieval method and device, a terminal and a computer readable storage medium, and the method comprises the steps: building a mapping relation between sample data features of each piece of sample data in a data set and sample data position information, and building a data index set corresponding to each piece of sample data in the data set based on each mapping relation; in response to a target data feature of the target data, obtaining a target mapping relationship corresponding to the target data feature according to the data index set and the target data feature; and based on the target mapping relationship, determining target data position information corresponding to the target data feature, and searching target data based on the target data position information. In the retrieval process, retrieval can be conducted through the input target retrieval information of the target data and the data index set, it is avoided that all sample data in the data set is traversed, the retrieval speed is greatly increased, and when the sample data in the data set is more and more, the influence on the size of the data index set is small.