2940 results about "Datasheet" patented technology

The invention relates to the technical field of intelligent manufacturing, discloses a papermaking equipment fault tracing method and system based on a process knowledge graph, and discloses the papermaking equipment fault tracing method and system based on the process knowledge graph. The method and the system comprise data acquisition and preprocessing, papermaking process knowledge graph construction, fault event detection and matching, fault tracing and propagation path reasoning, and maintenance scheme recommendation and optimization. The method overcomes the limitation that the traditional method is difficult to capture the cross-equipment, cross-process and cross-time sequence deep causal association and fault propagation path of the papermaking equipment. By constructing a comprehensive papermaking process knowledge graph, equipment operation state data, process parameter data, production quality data, equipment structure principle, process flow knowledge, fault mode knowledge, maintenance experience and other heterogeneous knowledge are subjected to deep fusion and semantic association, so that the system can exceed the correlation of the data surface; and an internal mechanism and a propagation chain of the fault are deeply excavated.

The invention provides a method for generating an SQL (Structured Query Language) by a natural language based on bidirectional mapping and semantic parsing, which relates to the technical field of database query and comprises the following steps of: extracting natural language query elements and packaging the natural language query elements into structured data, and establishing a mapping relationship from a query field to a service attribute and a physical data table by adopting a bidirectional Hash index technology; and automatically identifying multi-table association keys, performing semantic extension and compliance verification, generating an abstract syntax tree, performing processing according to user permission, and finally converting the abstract syntax tree into an SQL statement conforming to a target database syntax specification. According to the method, the accuracy and the efficiency of converting the natural language into the SQL are improved, and the flexibility and the safety of the system are enhanced.

The invention discloses a multi-table combined natural language query SQL (structured query language) generation method, which comprises the following steps of: 1, constructing a database meta-knowledge graph, and establishing a triple storage comprising a table structure, a primary and foreign key relationship and business description for each data table; 2, receiving a natural language query request, and calculating the topic relevancy between query semantics and each database table through a pre-trained topic matching model; 3, dynamically constructing a view, and logically associating the database tables of which the theme relevancy exceeds a threshold value to form a temporary view; 4, generating a context enhancement prompt, and combining the temporary view structure, the field semantic description of the view and the view content sample to form a structured prompt; 5, inputting the natural language query and the structured prompt into the large language model to generate candidate SQL statements; and step 6, executing verification and iterative optimization on the candidate SQL statements, verifying logic correctness through SQL execution plan analysis and result sampling, and triggering and prompting a reconstruction mechanism when detection is abnormal.

The invention relates to the technical field of vibration monitoring, in particular to a slurry pump motion monitoring management system based on data analysis, which comprises a multi-source acquisition module, a period judgment module, a trend focusing module and a dynamic management and control module. According to the method, vibration data are collected through a three-axis acceleration sensor, a fundamental frequency amplitude is extracted through FFT, time-frequency characteristics and periodic distribution are fused in combination with an extreme value interval sequence, the data representation dimension is enhanced, the rising slope is processed through moving average, the periodic variation is calculated, random noise is restrained, and trend continuity is enhanced. Monotonicity test is combined with dynamic threshold judgment to improve anomaly recognition sensitivity; a sliding window is used for segmenting multi-cycle data; a sudden change interval is positioned based on a pressure amplification rate second derivative; the limitation of a frequency domain on transient feature recognition is broken through, a pressure amplification rate three-dimensional vector is constructed, cosine similarity is calculated, and a working condition difference degree is quantified to dynamically bind a maintenance instruction; and closed-loop feedback is formed to improve the early warning and decision matching degree.

A method for AI-driven natural language search includes receiving a user query for one or more items from a user, processing the user query by searching against at least one relational database associated with the query, where the relational database is generated by extracting features from electronic documents of a plurality of items associated with the one or more items and by identifying specifications or respective values corresponding to the extracted features of the plurality of items, generating one or more query results based on the processing of the user query, where the one or more results include at least one item identified from the plurality of items and a justification for explaining an irrelevance of the at least one item, and transmitting the one or more query results to a user device for presentation to the user.

A structured query language statement generation method, executed by an electronic device, and comprising: determining a natural language text representing a query task, and determining a preset database, the preset database comprising at least one data table, and each data table comprising at least one field (101); on the basis of the natural language text, determining, from the preset database, at least one task-associated field and at least one task-associated data table which are associated with the natural language text (102); rewriting the natural language text on the basis of the task-associated field to obtain a rewritten natural language text (103); acquiring prompt information for the rewritten natural language text, the prompt information being used for guiding a query statement generation model in generating a structured query language statement (104); performing question classification on the rewritten natural language text to obtain question type information of the rewritten natural language text (105); and generating the structured query language statement of the rewritten natural language text by means of the query statement generation model on the basis of the task-associated field, the task-associated data table, the prompt information, and the question type information (106).

The invention relates to a power system scheduling method and device based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a power system, wherein the multi-modal data comprises power grid topology data, meter information, wind and light energy output prediction data, maintenance plan text data and energy comprehensive data; performing feature extraction, space-time alignment processing and hierarchical attention learning on each modal data to obtain a fusion feature vector; training the scheduling model by using the fusion feature vector until a total loss function meets a preset condition, the total loss function comprising a power flow equation constraint term; the method comprises the following steps: acquiring real-time multi-modal data of a power system, and obtaining a real-time fusion feature vector through feature extraction, space-time alignment processing and hierarchical attention learning; and inputting the real-time fusion feature vector into the trained scheduling model to obtain a real-time scheduling decision, and scheduling the power system according to the real-time scheduling decision. The method can improve the dispatching precision of the power system.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-agent traceable analysis method, device, equipment and medium, and the method comprises the steps: receiving a target theme and a data source list, collecting a multi-source document, and carrying out the preprocessing of the multi-source document to generate a preprocessing document set; configuring an analysis agent based on a semantic clustering result, and setting an analysis direction to form an analysis agent set; generating a structured note and index data table, and executing cross-document comparison to form an analysis output set; and receiving a feedback instruction to adjust the analysis agent set, triggering incremental processing to update the analysis output set, generating a theme research and judgment report, and keeping mapping consistency. According to the method, the multi-source document is fused through semantic clustering and a multi-agent cooperation mechanism, semantic association and traceable analysis are achieved, agent configuration is optimized in combination with interactive feedback, and the accuracy and the intelligent level of report generation are improved.

The invention discloses a query statement generation method and electronic equipment, and relates to the technical field of query statement generation, and the query statement generation method comprises the following steps: obtaining a historical query record in response to a received natural language query request of a user; judging whether query relevance exists between the natural language query request and a historical query record or not, and if yes, determining a candidate data table set based on a historical query data table; calling a pre-trained language model, and performing first reasoning on the natural language query request and the candidate data table to generate an initial query statement; executing the initial query statement on a first data table set constructed based on the candidate data table set, verifying whether a query score of an initial query result meets a preset availability standard threshold value or not, and if yes, taking the query score as a target query statement. According to the method and the device, the problems of input information redundancy, high overhead loss, low statement verification efficiency and insufficient correction capability in related technologies are solved, and efficient and accurate generation from the natural language query request to the query statement is realized.

The invention provides an intelligent question and answer method and device based on large model multi-source information fusion and electronic equipment, relates to the technical field of computers, in particular to the fields of big data, intelligent search and the like, and can be used for application scenes such as intelligent question and answer. According to the specific implementation scheme, pre-retrieval is conducted from a data table structure information base and a data table field information base according to a user problem, and a pre-retrieval result is obtained; retrieving from a business knowledge base, a business rule base and a question number template base according to the user question to obtain a knowledge enhancement result; according to the pre-retrieval result and the knowledge enhancement result, utilizing a query instruction generation model to obtain a query statement; and querying from the data table set according to the query instruction to generate an answer corresponding to the query word. The scheme can improve the professionality of intelligent questioning and answering, and is suitable for application scenes of various industries.

The invention relates to the technical field of data processing, and provides a field rule matching recommendation method and device based on multi-dimensional feature fusion. Obtaining user input information, wherein the user input information comprises a target data table and a current business target; performing feature extraction on fields in the target data table through a field feature extraction module to obtain field multi-dimensional features; performing attribute modeling on the data quality rules in the preset rule base through rule attribute modeling to obtain rule multi-dimensional attributes; calculating matching scores of the field multi-dimensional features and the rule multi-dimensional attributes through a multi-dimensional scoring model; and dynamically adjusting the weight of each dimension in the multi-dimensional scoring model according to the service target priority through a rule recommendation engine, and outputting Top-N recommendation rules based on the adjusted weight and the matching score. According to the scheme, the problems of low quality rule matching precision, poor efficiency and insufficient scene coverage of a traditional method can be solved.

The invention relates to a BI intelligent question answering system and method based on business rule retrieval and AI workflow, and belongs to the field of intelligent data processing. The system comprises a user interaction module, a knowledge base module and an AI workflow module. The method comprises the following steps: (1) business rule retrieval: taking a BI analysis problem written by a user interaction module as a target, retrieving business data table information from a knowledge base module, judging a business data table of the user problem in combination with a big language model, obtaining business indexes contained in the table according to the business table, retrieving BI business information related to the user problem, and performing business rule retrieval; and (2) AI workflow execution: taking a BI analysis problem as input, gradually scheduling workflow nodes of business rule retrieval, cue word construction, large language model interaction and database interaction, realizing an accurate Text-to-SQL function and a data visualization function, and further realizing a question-answer ability of converting the BI analysis problem input by a user into a BI analysis result.

The invention discloses a database query method and device, electronic equipment and a storage medium, and relates to the technical field of big data or other related fields, the method comprises the following steps: obtaining a to-be-executed database query statement, and analyzing a target data table related to the database query statement and index configuration information of the target data table; performing validity detection on query conditions of the database query statement based on the index configuration information to obtain a detection result; adjusting the execution mode of the database query statement according to the detection result to obtain an optimized database query statement; and executing the optimized database query statement to obtain a database query result. Through the method and the device, the technical problem of low database query efficiency in related technologies is solved.

An iterative method for monitoring a computing device characterized by metric data to be monitored, including, for each iteration, of collecting metric data over a predetermined interval of time, detecting a seasonality pattern of said metric data over said predetermined interval of time, determining an interval-specific model representing the detected seasonality pattern, calculating modelled data using said determined model and the collected metric data, comparing the calculated modelled data with the collected metric data to calculate a score characterizing the difference between the calculated modelled data and the collected metric data, calculating an anomaly likelihood for each data of the collected metric data using the calculated score, detecting an anomaly on a data when probability that the value of said data is an anomaly is greater than a predetermined threshold.

The invention relates to the technical field of artificial intelligence, and particularly provides an automatic bid invitation file generation method and system, a terminal and a medium, and the method comprises the steps: receiving project demand information input by a user, the project demand information comprising one or more of a structured text, an unstructured text, a technical drawing and a data table; performing multi-modal analysis processing on the project demand information to generate a unified project feature vector; performing matching and reasoning on the item feature vectors and a bid invitation domain knowledge graph; the matching and reasoning process adopts a dynamic correlation threshold strategy based on query complexity and node importance to screen out entity nodes related to current project requirements; and calling a domain large model to receive the item feature vector and the inference result from the knowledge graph to generate a first draft of the bid invitation file, including matching a bid invitation file template, writing bid invitation terms, and adding traceability information to each generated target term. The bid invitation file generation quality and efficiency are improved.

The invention provides a photovoltaic knowledge question-answering method, system and device based on a ReAct architecture, and relates to the technical field of photovoltaic information. The method comprises the steps that a photovoltaic industry knowledge base is obtained, original photovoltaic industry knowledge documents are processed through analysis, abstract generation and abstract vectorization, and a three-layer storage structure containing an abstract vector index table, a document metadata table and a complete document storage library is formed; obtaining a user question, initializing a ReAct cycle and creating a memory storage structure according to the user question and a retrieval tool set constructed based on the three-layer storage structure; the ReAct circulation is executed until the end, and each round of circulation comprises the steps that questions, system cue words and memory data are integrated to generate a context, a round tool and parameters are obtained through reasoning of a large language model, and accordingly the tool is called to retrieve a knowledge base, update memory and remove duplication; after the loop is terminated, an answer is generated based on the memory data. According to the method, when the photovoltaic problem is solved, the key diagnosis link and the solution can be deeply given out without omitting the key diagnosis link and the solution.

Embodiments of the present disclosure relate to multimodal AI-based search for digital assets via an indexing and / or search pipeline. With respect to the indexing pipeline, some embodiments obtain first data and second data associated with a first digital asset. Such data represents different data types or modalities of the same digital asset. After obtaining the first and second data, some embodiments then generate a composite index. After the composite index is built such index can then be used to execute a query via the search pipeline. To execute the query some embodiments compute a relevance score for each digital asset, of multiple digital assets, based at least in part on a measure in which each digital asset satisfies one or more parameters or conditions for two or more data types of the query. Various embodiments then rank each digital asset and present one or more associated indicators.

The invention discloses a method for generating statements from natural languages to SQL (Structured Query Language) based on a large language model agent. The method comprises the following steps of: constructing an LLM (Logical Language Model) guide table mode index for a target database; obtaining a user natural language question and extracting a keyword; searching in an LLM guidance table mode index according to the keyword, and screening related data tables; splicing the table mode information and the keywords of the related data table into a user natural language problem, and constructing an enhanced cue word; based on the large language model intelligent agent, generating SQL statements according to the enhanced cue words and verifying the SQL statements, if the SQL statements succeed in verification, obtaining the required SQL statements, and if the SQL statements fail in verification, performing iterative closed-loop correction on the generated SQL statements through an SQL statement closed-loop correction workflow; if the corrected SQL statement is successfully verified, the required SQL statement is obtained, and if the corrected SQL statement fails and the number of times of correction reaches a threshold value, the suggestions that the natural language problem of the user has defects and is modified are output. According to the method, the accuracy and success rate of SQL statement generation can be improved.

The invention relates to a tunnel crack monitoring method, system and device and a medium, and belongs to the technical field of tunnel monitoring, and the monitoring method comprises the steps: obtaining a three-dimensional model and a geological parameter library of a to-be-monitored tunnel, and constructing a sensor network topology structure; deploying a sensor network based on the sensor network topology structure; receiving static strain data and dynamic vibration data, collected by the sensor network, of each monitoring point in the tunnel; performing space-time alignment on the static strain data and the dynamic vibration data, eliminating environmental interference based on a pre-trained noise spectrum feature library, and generating a cleaned structured data table; extracting a strain gradient characteristic value and a dynamic chaos characteristic value in the structured data table, inputting the strain gradient characteristic value and the dynamic chaos characteristic value into a multi-stage classification model for crack type identification and risk grade evaluation, and generating a crack risk grade map; and generating corresponding maintenance prompt information according to the crack risk grade map. According to the invention, the comprehensiveness and accuracy of tunnel monitoring can be improved.

The invention relates to the field of data processing, in particular to a government affair field data query method and device based on multiple agents, and the method comprises the steps: receiving a user question, and carrying out intention recognition through a large language model to obtain a structured query intention; after intention recognition fails, a supplementary question request is initiated by using a clarification mechanism, and a structured query intention is acquired based on the supplementary question request; obtaining priori knowledge, converting the priori knowledge into priori knowledge embedded vectors, and summarizing the priori knowledge embedded vectors into a vector index; searching a plurality of pieces of reference data and database information corresponding to the question rewritten by the user according to the question rewritten by the user; querying the corresponding data table by using the large language model, and generating a complementation prompt word of the SQL query statement by using the prompt word template; and screening out a target SQL query statement according to the completion prompt word, obtaining a query result, and returning the query result to the user. According to the method and the device, the SQL query statement generation accuracy can be improved.

The invention discloses a communication network multi-source big data analysis method based on distributed database collaboration, and relates to the technical field of communication data analysis, and the method comprises the steps: 1, generating a session contract number, a table snapshot number, a flow site set and a transaction time point at the start of a session, registering a collaborative metadata table, judging semantic compatibility, and setting a state; step 2, pushing down three anchors to a lake-warehouse, a message bus and a transaction library according to the serial number, performing freezing reading according to the anchors to obtain a lake-warehouse segment, a streaming segment and a transaction segment, performing alignment extraction on a joint feature set in the step 3, generating output through a deterministic operator chain, and storing the extracted joint feature set in a database; the method comprises the following steps: step 1, setting a session contract number, a field source and an operator relationship, reading an anchor code as a blood relationship code and writing the blood relationship code into a result table, and step 2, resetting three anchors and multiplexing a query plan during reproduction verification and cross-domain migration, and integrating blood relationship equivalence, result equivalence, freezing consistency, registration difference and migration fingerprints to realize session-level consistent reading, recalculation and auditing.

The invention discloses a Direct3D deep cutting behavior compatible method based on an atomization shader, which comprises the following steps: establishing a scene combination metadata table in a DXVK compiling stage, constructing a shader template library containing atomization components and variants, and generating an executable file of the DXVK; after the DXVK is started, deep processing API calling of the D3D application is intercepted, parameters are extracted to generate an atomization parameter set, and scene combination metadata are matched or newly added in a scene combination metadata table to obtain a recommendation strategy and shader component combination; a conversion mode is adaptively selected according to scene metadata to calculate a Vulkan depth value, and a depth comparison operation is adapted to generate a Vulkan depth conversion parameter; the DXVK maps the parameters into DRM depth control parameters irrelevant to hardware and transmits the DRM depth control parameters to a kernel, the DRM packages the DRM depth control parameters and converts the DRM depth control parameters into hardware instructions matched with the GPU, the DXVK loads a shader template and binds the parameters, original logic is spliced to generate a Vulkan shader module, a Vulkan command stream containing the hardware instructions is generated and submitted to the GPU after a pipeline is bound, and finally the GPU executes the Vulkan command stream to obtain a depth rendering result conforming to D3D native logic.

The invention belongs to the technical field of cloud storage, is suitable for the financial field and the medical field, and discloses a data table migration method, device, equipment and medium, and the method comprises the following steps: obtaining the name of an original database, a connection character string and a configuration file of a to-be-migrated data table, and analyzing the configuration file of the to-be-migrated data table to obtain a data table to be migrated; obtaining a table name and a table owner of the to-be-migrated data table; connecting the original database according to the connection character string of the original database, generating a table existence verification statement through the table name and the table owner of the to-be-migrated data table, and verifying the validity of the to-be-migrated data table according to the generated table existence verification statement; renaming the to-be-migrated data table in the target database according to a preset naming rule and performing compatibility processing; the migration instruction of the data table is generated in the target database, and the compatible data table to be migrated is migrated into the target database according to the generated migration instruction, so that the migration efficiency of the data table is effectively improved.

The invention discloses an association mapping method and system based on an API and a database sensitive field. Comprising the following steps: generating a tracking identifier for a current HTTP request through a global interceptor, and storing the tracking identifier and request context information into a thread context; intercepting an SQL (Structured Query Language) statement sent to a database by the application program, and analyzing the SQL statement to extract a data table name and a field name set accessed by a current HTTP (Hyper Text Transport Protocol) request; identifying sensitive fields in all access fields in the field name set; generating an associated log record based on the accessed sensitive field set, the SQL statement, the tracking identifier and the request context information; and analyzing the API response data to obtain a response field set, and establishing an association mapping relationship between the accessed sensitive field set and the response field set, so as to create complete data flow link information based on the association mapping relationship and the association log record. The data security of the sensitive data can be improved.

The invention discloses a cross-heterogeneous database data migration method and system, and the method comprises the steps: building a multi-data-source connection among multiple data sources, and enabling the multi-data-source connection to be used for representing the incidence relation between a source database and a target database; obtaining a source data table based on the migration sql, and querying source data corresponding to the source data table from a source database; converting the source data based on a preset model mapping rule to obtain target data; determining a target database into which the source data is migrated according to the multi-data source connection, and writing the target data into the target database to realize data migration; and performing data verification on the target database according to the checking rule to obtain a migration result, and generating a result report according to the migration result. According to the method, the source data is converted into the target data through the preset model mapping rule, migration obstacles caused by structural differences among heterogeneous databases are effectively solved, the consistency of the data is ensured through the checking rule, and the accuracy of data migration is improved.

The invention discloses a row-level data permission authorization method and device, equipment and a medium, and relates to the technical field of computers. Comprising the following steps: registering basic data into a preset database; configuring a corresponding field management user with a field management authority for the preset service field; creating or updating a record in a user data permission table based on the user permission application to determine a permission relationship between the user identifier and the entity data; registering a service data table and a mapping relation between an entity column and an entity type in the service data table in a preset service field based on a user instruction of a field management user; and dynamically generating a permission filtering condition based on the query SQL and the user identifier so as to generate an SQL statement with permission filtering, and querying corresponding service data from the service data table based on the mapping relationship so as to generate a corresponding query result based on the service data. Therefore, the efficiency and flexibility of line-level data permission authorization can be improved on the premise of ensuring data security.

The invention provides a whole vehicle logistics site selection and transportation path planning method and device and electronic equipment, and the method comprises the steps: converting the address information of each distribution network point in a preset region range into corresponding geographic position information; cleaning and preprocessing the address information, the geographic position information and the sales volume data of each distribution network to obtain target data, and clustering the target data to obtain a preset number of initial clusters corresponding to a preset area range; and performing iterative optimization on the preset number of initial clusters based on the target data and a preset freight rate data table to obtain a preset number of optimized clusters corresponding to a preset area range, and performing vehicle logistics transportation path planning based on the target data, the optimized clusters and the preset freight rate data table, and obtaining a target whole vehicle logistics transportation path corresponding to the preset area range. By adopting the method, the scientificity and the accuracy of site selection of the whole vehicle logistics transfer warehouse can be improved, and integrated coordination of site selection of the whole vehicle logistics storage center and whole vehicle logistics transportation path planning is realized.

The invention discloses a zero-configuration knowledge graph construction method, equipment and a medium, belongs to the technical field of knowledge graphs, and aims to solve the technical problem of how to overcome the hard coding defect in traditional knowledge graph construction, realize a more flexible construction mode with lower difficulty in use, further realize data source entity association and semantic fusion and improve the knowledge graph construction efficiency. According to the technical scheme, field name semantic analysis and data content analysis are conducted through a dynamic semantic analysis algorithm, and intelligent database deep analysis is achieved; the same entities in the multiple tables are automatically recognized and merged through a cross-table entity intelligent merging algorithm, and multi-table collaborative intelligent search is achieved; constructing an intelligent knowledge graph: automatically selecting an optimal construction strategy through a multi-strategy relation construction engine according to keyword role features, and independently constructing a local knowledge graph for each data table; and optimizing and intelligently exporting the knowledge graph.

The invention relates to the technical field of city data representation. The invention discloses an urban space general representation learning method and device based on multi-modal spatio-temporal data fusion, a terminal and a storage medium, which can improve the applicability of urban general representation and enable the urban general representation to be applicable to diversified urban analysis tasks. The method comprises the following steps: acquiring multiple modal spatio-temporal data of each space unit in a target city, and setting a corresponding view for each modal spatio-temporal data; on the basis of each modal spatio-temporal data of each space unit, generating a representation of each space unit in a single view under a view corresponding to each modal spatio-temporal data; generating a multi-view fusion representation corresponding to each space unit based on the representation of each space unit in a single view under the views corresponding to all modal spatio-temporal data, and forming a multi-view fusion representation matrix by the multi-view fusion representations corresponding to all the space units; and performing global aggregation processing on the multi-view fusion representation matrix to obtain urban general representation.

A method for enabling users to conversationally interact with a data table comprises receiving by a virtual assistant server from a user device, a natural language query targeted at a data table. The virtual assistant server determines one or more table intents or one or more table sub-intents of the natural language query. The virtual assistant server determines one or more table operations to execute on the data table based on the determined table intents or table sub-intents. The virtual assistant server iteratively executes each of the determined table operations on the data table, where a final version of the data table results after completion of one or more of the iteratively executed determined table operations. Subsequently, the virtual assistant server determines a response to the natural language query based on the final version of the data table and outputs the determined response to the natural language query to the user device.