498results about How to "Improve query efficiency" patented technology

The invention belongs to the technical field of electric power data security management and intelligent question answering, and particularly relates to an electric power data security policy large model question answering system and method based on relation pooling. The system comprises a power data security policy knowledge base, a language model, a question processing module, an answer generation module and a user output module. Data of the electric power data security policy knowledge base comes from an electric power data security policy file; the language model converts a natural language problem into a structured query for knowledge base retrieval; the question processing module retrieves policy terms related to the natural language question to serve as retrieval results; the answer generation module generates a final answer according to the natural language question and a retrieval result of the knowledge base; by introducing the intelligent question-answering system combining the large language model and the knowledge base, the problems of low efficiency, inaccurate interpretation, incapability of dynamically updating and the like in the existing power data security policy query and management are solved.

The invention provides a natural language SQL (Structured Query Language) conversion method based on a data platform and a large language model, which relates to the technical field of data processing and comprises the following steps of: performing vectorization storage on metadata, blood relationship, indexes and historical query information, performing intention judgment and vector matching on a user query request, and determining a query condition based on a matching result. An association path is constructed according to data consanguinity, and an optimal SQL statement is generated by utilizing a historical query optimization table connection sequence and combining index matching. According to the method, intelligent conversion from the natural language to the efficient SQL can be realized, the query efficiency is improved, and the use threshold of a user is reduced.

The invention provides an unstructured database federated learning collaboration method and system based on swarm intelligence, and relates to the technical field of structured data, and the method comprises the steps: training a query pattern recognition sub-model, a path planning sub-model and a resource allocation sub-model through federated learning, and constructing a swarm intelligence collaboration network. After a user query request is analyzed by the query mode recognition sub-model, candidate query paths are generated and optimized by fusing an ant colony algorithm and a particle swarm algorithm, and a query execution scheme is generated. And the resource allocation sub-model performs resource scheduling accordingly. And generating a performance optimization target for updating local model parameters of each node, and fusing the updated parameters through a security aggregation algorithm to guide the next query optimization. According to the method, the query efficiency of the unstructured database is improved, and the resource consumption is reduced.

The invention provides a time-space dynamic graph construction method and system for an engineering construction project, and relates to the field of railway tunnel engineering management.The method comprises the steps that construction data entities are extracted, and an entity set is generated; taking entities in the entity set as nodes to be added into the graph, and configuring attributes for each node; adding a relation edge for each node according to a logic structure between the construction data; adding a time label and a space label to each node so as to form a data graph; associating each entity in the data atlas with a time chain and a space chain to form a data atlas attached to the time-space chain; combining a node merging strategy and a graph neural network to optimize a graph structure; and dynamically modeling dimensional changes of entities and relationships in time by means of a time sequence diagram neural network. According to the method, the dynamic change graph of the relationship between the construction data entities is constructed, so that the spatial-temporal information and the entity relationship of each process are accurately expressed and efficiently associated, and the retrieval efficiency and the management level are favorably improved.

The invention provides a different types of database visualization table management method and system, and relates to the technical field of computers.The method comprises the steps that a database general operation system comprising a general operation interface and a dialect abstract base class is constructed, and a query performance evaluation system is established based on performance data; and predicting an optimal database connection mode through deep reinforcement learning and generating a multi-stage execution plan. Meanwhile, an anomaly feature knowledge base based on a graph neural network is constructed, and anomaly prediction and preventive processing are carried out based on the anomaly feature knowledge base. A multi-stage execution plan is executed after a database operation request is subjected to grammar adaptation, and a distributed transaction processor dynamically adjusts a transaction isolation level and a concurrency control parameter. And finally, according to the terminal and the data volume, selecting a proper data display mode to carry out visual presentation.

The invention discloses a data analysis method and system and a storage medium, and is applied to the field of natural language processing. The method comprises the steps of obtaining a natural language input by a user; constructing a semantic data model for converting the natural language into a semantic query statement; combining the semantic data model with a user-defined rule to construct a semantic parser; inputting the natural language into the semantic parser for parsing to obtain a semantic query statement; inputting the semantic query statement into a semantic corrector for correction to obtain a target semantic query statement; converting the target semantic query statement into a structured query statement through a semantic translator; and inputting the structured query statement into the database for query to obtain a query result.

The invention discloses an intelligent bid evaluation expert extraction and management system, and relates to the technical field of expert extraction systems. Comprising a data cleaning and compression module, a database logic and table relation design module, an expert information collection standard and automation module, an information real-time updating and principal management module, an extraction algorithm design module, a safety and authority management module and a real-time monitoring and auditing function module. Through a self-adaptive sparse auto-encoder and a dynamic clustering technology in the data cleaning and compression module, abnormal values in a bid evaluation expert database can be detected and cleaned, data compression is performed by adopting a self-adaptive dictionary coding algorithm, and the storage efficiency and query performance of the database are improved; based on a multi-modal information fusion and reinforcement learning method, accurate collection, dynamic updating and intelligent management of expert information are realized. The randomness and fairness of the bid evaluation expert extraction process are ensured by using a mode of combining deep reinforcement learning and a quantum random number generator in an extraction algorithm design module.

The invention belongs to the technical field of intelligent power grids, and particularly relates to an intelligent power grid monitoring data storage system which comprises a data acquisition module, a data preprocessing module, a hierarchical data storage module, an intelligent data retrieval and management module and a data analysis and visualization module. Power grid operation data are collected in real time through various sensors; the data preprocessing module is used for cleaning, denoising and formatting the collected data and identifying data abnormal values by adopting a dynamic threshold detection method; the hierarchical data storage module adopts a hierarchical storage strategy of hot data, temperature data and cold data, stores the data in different media respectively, and introduces a time sequence database; the intelligent data retrieval and management module retrieves the stored data by using a multi-condition complex query algorithm, and improves the query efficiency through an intelligent data indexing and retrieval mechanism; and the data analysis and visualization module generates a report and a visualization chart through an advanced data analysis algorithm.

The invention relates to the technical field of logistics distribution data fusion, in particular to a data fusion method and system based on a logistics distribution knowledge graph. The method comprises the following steps: extracting original data and carrying out independent representation learning and unified representation processing; extracting uniformly expressed context information and carrying out dynamic perception embedding to obtain semantic enhancement features; fusing time-space and dependency features and physical characteristic features based on semantic enhancement features, performing message passing and relation inference by using a graph neural network, and finally determining a multi-mode relation set; global entity clustering and local feature fine matching are carried out based on semantic enhancement features, hierarchical entity alignment is carried out in combination with the multi-mode relation set, and an aligned entity set is obtained; and constructing an entity filling graph according to the aligned entity set, supplementing multi-dimensional relation attributes, and finally generating a logistics distribution knowledge graph. According to the invention, through a logistics distribution data fusion technology, a more comprehensive and more intelligent logistics distribution knowledge graph is realized.

The invention discloses an anomaly detection method and system based on a dynamic traceability graph, and belongs to the field of network anomaly detection.The anomaly detection method comprises the steps that the traceability graph is constructed, and node features of all nodes and features of edges connected with the nodes are spliced to serve as state feature vectors of the corresponding nodes; continuously monitoring system traceability information and dynamically updating the traceability graph; when a new edge et is generated, executing the following steps: inputting the et, a state vector st-of a graph structure surrounding the et at a t-moment and a generation moment t of the et into the graph attention network to obtain an embedded vector z of the et, and updating a source node and a destination node of the et; inputting z into a decoder, and predicting the type of et to obtain a prediction vector P (et); the decoder comprises a long short-term memory network and a plurality of full connection layers which are connected in sequence; calculating a reconstruction error RE between the actual edge type vector of the P (et) and the actual edge type vector of the et; if Th is greater than Th, judging that abnormity exists, otherwise, judging that no abnormity exists. According to the invention, the detection precision and real-time performance of anomaly detection can be improved.

The invention discloses a software query information management method and system, and belongs to the technical field of software information security, and the method specifically comprises the following steps: after a query request of a user is received, creating a dynamic topology network architecture according to semantic features of query contents; creating a virtual query container corresponding to the dynamic topological network in the isolated memory space, wherein the virtual query container generates a dynamic encryption channel bound with the current topological structure; dynamically embedding user permission labels into topological network nodes through an attribute-based encryption algorithm, and establishing a mapping relation between permission characteristics and network paths; when distributed query is executed in the virtual query container, the topological node connection weight is dynamically adjusted according to the real-time network load; before a query result is fed back to a user, data elements are recombined based on a reverse entropy value of an original topology network architecture; and when an abnormal access feature is detected, triggering a topological network structure dynamic failure mechanism.

The invention relates to the field of technical data acquisition, and particularly discloses a data acquisition method and device for an industrial Internet of Things and a storage medium, and the method comprises the following steps: S1, constructing a dynamic sensor network, and collecting the operation data of industrial equipment in real time through a multi-modal sensor; s2, dynamically adjusting a data acquisition frequency # imgabs2 # based on an environment noise coefficient # imgabs0 # and an equipment health index # imgabs1 #; s3, performing hierarchical compression and encryption on the acquired data, and allocating a compression rate # imgabs4 # and encryption strength # imgabs5 # according to data type importance # imgabs3 #; s4, executing load balancing through an edge computing node, and distributing a computing task to an optimal node; and S5, storing the processed data into a distributed database according to spatial-temporal correlation, and generating a multi-copy disaster recovery index. Through dynamic acquisition, intelligent processing, optimized storage and adaptive strategy adjustment, accurate and efficient data acquisition is realized, data security and processing performance are flexibly guaranteed, storage query efficiency and disaster tolerance capability are improved, and storage resources are utilized to the maximum extent.

The embodiment of the invention discloses a data query method and device, electronic equipment, a medium and a program product. The method comprises the following steps: receiving a target data query request sent by a user side; the target data query request carries a target natural language query statement input by a user of the user side; performing intention recognition on the target natural language query statement to obtain a target intention recognition result; the target natural language query statement and the target intention recognition result are input into a target large model, a target database query statement is output, and the target large model is obtained through training on the basis of multiple known database query statements and natural language query statements of the intention recognition result; verifying whether the target database query statement meets a preset database query condition or not; and if yes, in response to the target data query request, executing the target database query statement to obtain a target data query result.

The invention discloses a data processing method and device, a storage medium and electronic equipment. The method comprises the steps that a dependency collection plug-in is created under a construction node in a project object model file of a target application, the project object model file is analyzed through the dependency collection plug-in, initial dependency data of the target application is determined, and the target application is set to be deployed on at least two platforms with different platform identifiers; monitoring different stages in the construction process of the target application, and determining change dependency data of the target application; processing the initial dependency data and the change dependency data into target dependency data, and uploading the target dependency data to a target database; and in response to the target query message, querying and displaying the target dependency data from the target database. The technical problem of low query efficiency depending on data is solved.

The embodiment of the invention provides a database table field index establishment method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the steps of obtaining a target query statement and statement execution plan information; splitting the target query statement to obtain statement key data; determining the total row number of the candidate database table based on the historical index database; extracting index use data from the candidate database table based on the statement execution plan information; determining a new field index from the candidate column indexes based on the candidate distinction degree of the candidate column indexes; verifying the new field index based on the statement execution plan information, the historical index database, the new region division degree of the new field index and the candidate region division degree to obtain an index verification result; and if the index verification result is that the index establishment verification is passed, performing field index establishment based on the new field index. According to the embodiment of the invention, the database table field index more suitable for the current query request can be automatically established, so that the data query efficiency is improved.

The invention discloses a high-frequency vehicle track map matching method, which comprises the following steps of: (1) dividing a track T to be matched into a plurality of sub-tracks based on a reference line distance, and extracting key track points in the sub-tracks; (2) extracting a road where the key track point is located and a road type of the road, filtering the extracted road based on the road type, and storing filtered road data by adopting an R-Tree structure; and (3) matching an optimal matching path of the track T to be matched from the road data stored in the R-Tree structure through a hidden Markov probability matching model. From the perspective of reducing the scale of original data, trajectory and road network data are simplified. Then, space and direction analysis is carried out on the simplified trajectory, a micro-region splicing strategy is adopted to accelerate shortest path search, and a path deviation function is designed to improve the topology of a matching result; in addition, the extraction process of track motion key points is refined by fusing more road semantic information, so that a better matching result is obtained.

The invention provides a cache consistency maintenance method and device and a multi-core system.The method comprises the steps that a write-in pointer and an eviction pointer of a directory are obtained, and the directory comprises cache line sharing information; configuring an emergency threshold and a conventional threshold, wherein the emergency threshold is smaller than the conventional threshold; if the sum of the write-in pointer and the emergency threshold value is greater than the eviction pointer, setting a high priority, and preferentially performing directory eviction; if the sum of the write-in pointer and the emergency threshold value is less than or equal to the eviction pointer and the sum of the write-in pointer and the conventional threshold value is greater than the eviction pointer, setting a low priority, and evicting the directory; wherein the directory comprises the shared information of the cache lines, and the directory eviction means that the shared information of the cache lines which are not used for the longest time in the directory is evicted; and repeating the steps until the sum of the write-in pointer and the conventional threshold value is smaller than the eviction pointer. According to the method, available space in the catalog is guaranteed to the maximum extent to store new data, the method is suitable for a small system, catalog coverage can be guaranteed, query efficiency is improved, and query power consumption is reduced.

The invention discloses a database query plan optimization method and system, and belongs to the technical field of databases and artificial intelligence. AI model training: training the model by using the preprocessed historical query data according to a specific model architecture, a training mode and resource and time arrangement, and dynamically adjusting the model according to the change of the database; query analysis: evaluating the complexity of the query and the possibly involved data volume, and allocating a complexity level and a data access volume range for the query; query plan generation: generating and evaluating a plurality of candidate query plans based on prediction and query analysis results of the AI model, and selecting an optimal query plan as a final execution scheme; and performing query execution and monitoring. According to the method, the query execution time can be greatly shortened, and the database response speed is increased; system resources are utilized more effectively; dynamic changes of the database can be adapted, and the query plan is automatically updated according to structure and data distribution adjustment.

The invention provides a data processing method and apparatus, and an electronic device. The method comprises the steps of obtaining a linked list array of a specified size and a to-be-updated P2L reverse mapping table; determining the number of an L2P forward mapping table to which each logic offset address belongs; for each L2P forward mapping table, calculating relative offset between each logic offset address belonging to the table and a specified logic offset address, and calculating a remainder value obtained by dividing the number by a specified size; adding each relative offset corresponding to the L2P forward mapping table to a linked list corresponding to the remainder value; and updating the physical offset address corresponding to the logic offset address indicated by each relative offset in each linked list. According to the mode, the linked list array with the specified size is used, each relative offset corresponding to the L2P forward mapping table is uniformly recorded in each linked list by adopting the mode that the specified size is remainder by adopting the serial number of the L2P forward mapping table to which the logic offset address belongs, so that the query efficiency can be improved, and the consumption of a memory is reduced.

The invention relates to the technical field of information retrieval, in particular to an intelligent query system and method based on semantic analysis, and the system comprises an entity relation mapping module which collects user query data, carries out part-of-speech tagging and syntactic analysis operation on user query, constructs connection mapping according to syntactic relations among key elements, and sends the connection mapping to an entity query module; and calculating the correlation degree between each pair of keywords in the mapping, quantifying the attribute interaction strength, and generating a keyword relation mapping table. According to the method and the device, the precision and the efficiency of information retrieval are improved by deeply analyzing the query data of the user and combining the technical innovation of natural language processing. When part-of-speech tagging and syntactic analysis are carried out on a user query statement, key elements are accurately identified, connection mapping is constructed, and accurate capture of core information is ensured. And the information retrieval precision is enhanced by quantifying the association degree between the keywords and the attribute interaction strength. And a coherence score is calculated, so that the system can screen out the most relevant information.

The present application discloses a graph database load balancing method and device based on request type, which relates to the field of graph database query technology, and solves the problem in the prior art that several complex queries with long time consumption and heavy load may all fall on the same query service, causing the query service to be under great pressure and slow response, because only focusing on the resource utilization of the server and ignoring the characteristics of the query request itself. The method includes: constructing a mapping table, and according to the complexity of the query request, independently polling and routing the requests for complex queries according to the type, and independently polling and routing the requests for non-complex queries. Since the complex queries are routed to different query services, the effective dispersion of complex query requests of the same type is achieved, and the pressure on each query service to process data is balanced and small, thereby effectively improving the overall query efficiency and reducing the query time.

The invention discloses a character string data read optimization method based on a B + tree and a learning index. A learning index generated based on a machine learning (ML) model focuses on integer data of a fixed size, but is lack of consideration for character strings of variable lengths and is poor in performance in character string data. In order to solve the problem, the invention provides a character string slicing method which can convert a character string with a variable length into an integer with a fixed size for prediction and extract a common prefix from a character string key to reduce a model fitting error. According to the method, the characteristics of character string data are fully considered, complex multi-dimensional linear regression is simplified into one-dimensional linear regression, and the model calculation overhead is reduced. Besides, the invention provides a self-adaptive strategy to select a proper index to manage the calculation cost, the method for dynamically selecting the index nodes can utilize the advantages of the two index types to the maximum extent, and the calculation overhead is effectively reduced while the index efficiency is ensured.

The embodiment of the invention provides a semiconductor test data query method and device, equipment, a storage medium and a program product. The method comprises the steps of obtaining a query request input by a user; processing the query statement based on a set combination relation table to obtain a processing result; the set combination relation table comprises a preset splitting rule, a preset query statement and a storage address of a pre-calculation field result; if the processing result is that processing fails, splitting the query statement based on a set combination relation table to obtain a plurality of split fields; performing matching based on the plurality of split fields and a plurality of first pre-calculation fields corresponding to a set combination relation table to obtain a matching result; and determining a target query result for the query mode corresponding to successful processing based on the query mode corresponding to the matching result or the processing result. According to the embodiment of the invention, the query efficiency can be improved by processing and matching the query statements based on the set combination relation table.

The invention relates to the technical field of electronic component data processing, in particular to a database construction method and system based on electronic components. The method comprises the following steps: carrying out component information acquisition through an electronic component transaction platform to obtain an original component data set; performing component relation mapping construction according to the original component data set to obtain a component association graph; performing multi-dimensional attribute extraction on the component association map to obtain component attribute vectors; performing component similarity calculation and clustering analysis according to the component attribute vector to obtain a component cluster; and extracting pin information according to the component cluster to obtain a cluster pin data set. According to the method, multi-dimensional condition screening, sorting and result explanation are realized, an accurate, reliable and easily understood component replacement recommendation scheme is provided for a user, the user is helped to quickly find a proper component, and the electronic design efficiency is improved.

The invention relates to the technical field of data management and query, and discloses a multi-format data quick query method based on RAG, which comprises the following steps: step a, a user uploads a target file through a front-end interface, and the file is any one of PDF, Word or Excel formats; b, carrying out format verification on the uploaded file; c, selecting a corresponding analysis tool according to a file format, and performing text and structured data extraction on the file by using a PyMuPDF library, a python-docx library or an openpyxl library; and step d, respectively storing the analyzed data into a PostgreSQL (Structured Query Language) database and a Faiiss database. By combining structured data storage and non-structured data vectorization storage, efficient vector similarity retrieval is carried out by utilizing Fiss, and the response time of data query is greatly shortened. Compared with a traditional query method based on full-text search, the method has the advantages that the result most related to user query can be quickly and accurately returned from a large amount of data, so that the data query efficiency is improved.

The invention provides a query result set caching method for a database, which can be applied to the technical field of databases and data processing. The method comprises the following steps that: a data node caches a query result set, and directly returns a corresponding cache result set after judging a transaction state when receiving the same query command; the data node records the transaction state of the relation table through the state table, and updates the result set cache by judging the change of the transaction state; and when the data nodes relate to data operation in a modification table, modification table structure making and transaction operation, the number of result set caches needs to be controlled and the result set caches need to be reset through parameters corresponding to corresponding object records in a modification state table, and the result set caches are cleaned regularly. The invention further provides a query result set caching device for the database, electronic equipment and a storage medium.

The invention relates to the technical field of data processing, and discloses an HTAP database construction method and device, equipment and a medium, which can select a plurality of interested target column names and a plurality of target row records from a plurality of column names and a plurality of row records stored in an initial row storage table. And creating a proxy key index column in the initial row storage table, and in the proxy key index column, starting from a preset initial proxy key index identifier, allocating progressive increasing and continuous proxy key index identifiers to each target row record to obtain a target row storage table. And for any target column name, creating column data corresponding to the target column name according to the target column name and each proxy key index identifier. And creating a target column storage table according to each target column name and the corresponding column data. A hybrid transactional processing and analytic processing HTAP database is created that includes a target row store table and a target column store table. According to the database created by the method, the column data query efficiency can be improved based on the target column storage table.

The invention discloses a black box confrontation attack method based on local region disturbance, which can be applied to digital image attack and physical attack, and comprises the following steps: (1) positioning a salient region in an image by adopting a CAM type activation graph, and determining a local region highly sensitive to disturbance in an original image; (2) further grouping the salient regions to obtain sub-regions; (3) designing an objective function of local confrontation disturbance; (4) adding disturbance to the sub-regions; (5) calculating the size of a loss function of the sub-regions after the disturbance is added, and sorting the sub-regions; and (6) selecting an optimal sub-region, and carrying out disturbance of random search according to a target function. And (7) when an iteration stopping condition is satisfied, outputting the generated confrontation sample.

The invention relates to the technical field of data processing, and particularly discloses a city management event processing method and system based on Beidou grid codes. The method comprises the steps of performing grid coding on geographic space data in a city space range based on Beidou grid codes, and generating a basic grid model; when a city management event is reported, obtaining reported event data; processing the event data to determine the event type, geographic position and attribute of the city management event; determining a Beidou grid code associated with the city management event by using the geographic position; judging whether similar events exist in the city management events or not; if the similar events exist, judging whether the events are repeated events or not; if the city management event is not the repeated event, the event type and attribute of the city management event are mapped to a space grid pointed by the Beidou grid code; and packaging each block of data in the basic grid model to provide the data for a subsequent batch processing flow. Based on the invention, data support can be provided for urban management planning.

The invention relates to the technical field of IP address detection, and discloses an IP address attribution subject intelligent identification system and method based on deep learning, and the method comprises the steps: firstly collecting and integrating public IP address related data which comprises a mapping relation among an IP, a domain name and an attribution subject; performing data integration and data cleaning on the data, analyzing and extracting attributes and features of the data by utilizing feature engineering, and portraying an IP (Internet Protocol), a domain name and a subject from the data of associated features, naming features, geographic positions, operators, service types and network topology; inputting the IP, the domain name and the subject after portraying into an IP affiliation subject identification model to train the identification model, and deeply mining the association among the attributes and characteristics of each dimension of the IP, the domain name and the subject; and calling the model to identify the ownership subject, integrating other IP attributes, and then outputting a final identification result. According to the method, the problems of low IP address attribution subject identification precision, slow response and difficulty in adapting to network environment change in the prior art are solved.