148 results about "Dynamic query" patented technology

The invention discloses an intention classification method and device based on vector retrieval and context awareness and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of extracting business metadata and associating the business metadata with typical problem examples to generate a standardized service description document; encoding the standardized service description document into a high-dimensional semantic vector through a pre-training language model, and constructing a neighbor search index to store the high-dimensional semantic vector; splicing the user identity information and the current question text into an enhanced query statement, and encoding the enhanced query statement into a context-aware dynamic query vector through a semantic model; performing similarity retrieval based on the dynamic query vector to obtain candidate intelligent services, performing business domain filtering, context weighted sorting and dynamic priority rearrangement, and outputting target recommendation services; by collecting interactive behavior data of a target recommendation service, quality scoring is performed on service descriptions and problem examples based on a preset evaluation rule, and the service descriptions and the problem examples of which the quality scores are lower than a quality threshold value are updated.

The invention belongs to the field of intelligent question-answering systems, and relates to a financial intelligent question-answering method and system based on mixed retrieval and dynamic query, and the method comprises the steps: constructing an FAQ library and a financial office database; receiving a user question, calling a historical chat record of the user, and calling the large language model to perform intention recognition; calling a large language model to perform semantic rewriting in combination with the real intention of the user to generate a standardized query statement; obtaining a candidate list from the FAQ library by adopting a mixed retrieval mode; if no candidate questions and answers exist in the candidate list, replying the standardized answers; otherwise, screening candidate questions and answers of Top-N, judging whether the candidate questions and answers of Top-1 are personalized questions or not, if yes, calling a financial office database to generate a query result, and replying according to the query result and the candidate questions and answers of Top-N; according to the method, semantic retrieval and keyword retrieval are mixed through the user intention classification result and the conflict detection rule, the candidate list is obtained according to the mixed retrieval mode, and the retrieval recall rate is increased.

Embodiments of the disclosed technologies include receiving a first query including at least one first query term and configuring at least one prompt to cause a large language model to translate the at least one first query term into a set of functions that can be executed to obtain at least one second query term and generate and output a plan that is executable to create a modified version of the first query based on the at least one second query term. The plan is obtained by applying the large language model to the at least one prompt as configured. The plan is executed to determine the at least one second query term and create the modified version of the first query. The modified version of the first query is executed to provide, via the user interface, a response to the first query.

Methods and systems for optimally weighting search results in a hybrid search framework are described. Responsive to a search query, query embeddings are obtained using corresponding embedding generators. The query embeddings are provided to search operators corresponding to the embedding generators to obtain corresponding search result sets having search results and associated scores. Optimal weights for each of the corresponding embedding generators are determined using a machine learning model, based on the search query. The search result sets are combined, based on the determined weights and the associated scores, yielding a combined search result set. The disclosed methods and systems dynamically optimize weights applied to search result sets that are retrieved using more than one vector-based search operator (e.g., where each search operator performs a vector-based search using embeddings generated by a corresponding embedding generator), for generating more relevant search results within the hybrid search framework.

The invention discloses a database query execution method and device based on dynamic query compiling cache optimization, and belongs to the field of database management systems. Receiving a query statement input by a user and constructing an abstract syntax tree; generating a corresponding identifier according to the abstract syntax tree, searching a matched machine code, and loading and executing the matched machine code which can be reused to obtain an execution result; generating a corresponding executable plan tree for the query statement input by the user without finding the matched machine code or the query statement input by the user with the matched machine code incapable of being reused, generating and optimizing the machine code through dynamic compiling, obtaining the optimized machine code, and loading and executing the optimized machine code to obtain an execution result; and then an execution result is sent to the user, and machine codes in the cache are cleaned regularly. According to the method, whether the machine codes in the cache are multiplexed or not is accurately determined, so that unnecessary compilation overhead is reduced, and query execution efficiency is improved.

The invention relates to a teacher behavior recognition method based on a cross-modal dynamic query network, and belongs to the field of artificial intelligence education application. The problem that in the prior art, adaptation to education scene dynamic interactivity, semantic complexity and environment interference is insufficient is solved. The method comprises the following steps: extracting video spatio-temporal characteristics through a time-space transformation network, and generating a semantic prototype by combining a comparison language image pre-training model; a multi-region attention module is adopted to realize hierarchical feature fusion, and semantic related visual clues are screened; generating a self-adaptive query vector driving classification decision by using a dynamic query decoding module; and outputting a behavior recognition result based on a cross attention mechanism. The method has the technical effects that the characterization distinction degree of complex teaching behaviors is remarkably improved, the robustness of a dynamic classroom environment is enhanced, the reliability of multi-class behavior decisions is optimized, deep education semantic understanding is realized, and the high-efficiency and low-consumption landing application requirements are met.

Systems, apparatuses, methods, and computer program products are disclosed for dynamic query optimization. An example method includes receiving, by communications hardware, a first query and determining, by a query analysis engine, whether the first query corresponds to a stored query of a historical query repository. The method also includes determining, in response to determining that the first query does not correspond to the stored query, determining, by the query analysis engine, a parameter set indicating a current state associated with a system involved in an execution of the first query, processing, by a reinforcement learning engine, the first query and the parameter set in accordance with a plurality of policies, selecting, by the reinforcement learning engine and based on the processing, a first policy from the plurality of policies, and generating, by the reinforcement learning engine and based on the first policy, a query execution plan for the first query.

The invention discloses a micro-report generation method and device and a storage medium, and relates to the technical field of data processing, and the micro-report generation method comprises the steps that a natural language question input by a user is received, the natural language question is matched with a preset question example library, and a target question example is determined; obtaining a business index in the natural language question, and determining a data model corresponding to the business index; based on the target problem example, the business index and the data model, generating a calculation semantic configuration; generating a target SQL query by combining the corresponding model information, the data source information and the authority control rule based on the calculation semantic configuration; and generating the micro-report by executing the target SQL query. According to the method, the target question example is determined by matching the natural language question with the preset question library, and the semantic configuration is dynamically generated and calculated in combination with the specific business index and the data model, so that the target SQL query is generated, and the effect of immediately responding to the dynamic query according to the natural language question is achieved.

The invention relates to a diabetes mellitus question-answering method, system and device based on a knowledge graph and a medium, and belongs to the technical field of medical intelligent question-answering. The diabetes mellitus question-answering method based on the knowledge graph analyzes a question sentence of a user based on a pre-trained intention classification model so as to identify a user intention; analyzing the user question based on a pre-trained named entity recognition model to recognize a question entity; performing dynamic query on the constructed diabetes knowledge graph based on the user intention and the question entity to obtain a knowledge graph query result; encoding the user question by adopting a retrieval enhancement generation model, and performing semantic retrieval on the constructed diabetes semantic retrieval library based on the encoded user question to obtain related knowledge corresponding to the user question; and the knowledge graph query result and related knowledge are input into the constructed large language model to obtain the diabetes mellitus question and answer, so that the accuracy and specialty of the answer are improved.

The invention discloses an intelligent customer service dialogue generation method based on multi-modal interaction and dynamic knowledge enhancement, and the method comprises the specific steps: S1, intention recognition: recognizing the intention of a user according to the content inputted by the user; a dynamic query vector is generated based on the user intention and historical interaction records, weighted retrieval is carried out in a double-source knowledge base combining a knowledge graph and document paragraphs, and the weight of knowledge can be dynamically updated according to user feedback; s3, reply generation: inputting the user intention and the knowledge fragments related to the intention into the large language model to obtain a plurality of candidate replies; s4, performing compliance verification; and S5, outputting response. Through multi-modal fusion understanding, dynamic knowledge enhancement and controllable content generation, the interaction accuracy, knowledge timeliness and service individuation level of intelligent customer service are remarkably improved.

The invention relates to the technical field of carbon emission prediction, and particularly discloses a railway infrastructure construction process carbon emission prediction method and system.The method comprises the steps that firstly, carbon emission basic data in the railway infrastructure construction process are extracted from railway infrastructure related IFC files, and meanwhile a related carbon emission feature database is constructed; on the basis of the Hash similarity between each carbon emission basic data and each carbon emission characteristic data element in the database, querying a corresponding carbon emission factor to calculate the carbon emission, and further introducing a deep learning algorithm; a plurality of candidate carbon emission characteristic data elements with relatively high similarity are subjected to semantic analysis and dynamic query matching fitting to deduce corresponding carbon emission factors, so that carbon emission calculation is realized. According to the method, the carbon emission characteristics of novel composite materials and construction activities can be effectively handled, and the generalization ability of carbon emission prediction is improved, so that powerful support is provided for carbon emission management in the railway industry.

The invention relates to the field of application security detection, and provides a mobile application security assessment and automatic detection method, which adopts a data processing technology based on artificial intelligence to carry out syntactic analysis and semantic understanding on an obtained code of a mobile application program so as to obtain a code semantic structured coding feature. Meanwhile, semantic embedding coding is carried out on a set of security vulnerability rules extracted from the security vulnerability rule base to obtain a set of security vulnerability rule semantic embedding coding features; then, a detection result is automatically obtained based on dynamic query analysis representation of a set of code semantic structured coding features and security vulnerability rule semantic embedded coding features, and a security alarm prompt is generated in response to the detection result under the condition that the confidence coefficient of security vulnerabilities in the mobile application program exceeds a preset threshold value. In this way, the accuracy and reliability of the security vulnerability detection result can be effectively improved.

The invention discloses an intelligent decision data inquiry system and method based on a generative large model, and belongs to the technical field of artificial intelligence. Natural language inquiry input by a user is received, and an inquiry intention and a target field are extracted; extracting structured and unstructured cost data from the real estate project database, and constructing a candidate field set; performing semantic matching by utilizing the generative large model to generate an intention field mapping matrix; constructing a field dependency graph based on a mapping result, and extracting a minimum closed-loop field set; performing credibility evaluation on the fields, and constructing a weighted field set; generating a dynamic query template, outputting a natural language answer, and attaching a field tracing path; if the field is missing or the credibility is insufficient, automatically generating a reverse query until a credibility requirement is met; according to the method, automatic association and accurate question and answer between complex semantic query and heterogeneous cost data are realized, and the method has good interpretability, traceability and intelligent interaction capability.

The invention discloses a battlefield multi-agent dynamic cooperation method based on an attention mechanism. The method comprises the following steps: S10, acquiring local observation information; s20, establishing a strategy network and a value network, embedding a multi-head attention mechanism in each of the strategy network and the value network, allowing each agent to dynamically query the internal representation of a teammate, and converting a search problem of an exponential level joint action space into dynamic attention on key information; s30, the strategy network generates action probability distribution of the intelligent agent through the attention layer, and the intelligent agent is forced to depend on a cooperation signal; the value network outputs the value estimation of each agent and provides a reference function; the collaborative loss function calculates similarity penalty between agents based on the embedded representation output by the attention layer; and S40, the training process is updated by adopting a strategy gradient method, strategy network parameters are updated to maximize accumulated rewards, value network parameters are updated by minimizing total loss, and a highly cooperative joint strategy is output. According to the invention, the agent is ensured to always integrate team information during decision making.

The invention relates to the technical field of big data processing, in particular to a method, a device, equipment and a medium for processing and exporting report data with a large data volume, and the method comprises the following steps: receiving a report exporting request initiated by a user, and analyzing the content of the request to obtain a report identifier, exporting parameters and a target format; loading report template information corresponding to the report identifier, and extracting a report style; if the report comprises a change area and the data volume of the change area exceeds a threshold value configured by a user, loading fixed area data at one time, and obtaining a change area data stream based on the export parameters; creating a worksheet, traversing a report template according to rows, sequentially processing fixed area data and variable area data streams, and rendering format attributes in real time; dynamically adjusting the coordinate range of the merged cells, and outputting the processed worksheet to a file; and the dynamic SQL query statement is assembled to obtain the required data, so that the data obtaining efficiency is improved, the high-efficiency processing of the large-data-volume report is realized, and the report exporting speed is effectively improved.

The invention belongs to the technical field of map construction, and discloses an online vectorization map construction method based on dynamic query management. Extracting perspective image features; generating aerial view view angle features based on the perspective image features; initializing map element query; inputting the initialized map element query and the aerial view view angle features into a map decoder based on dynamic query management, and optimizing the map element query; taking the output result of the Lth layer of the map decoder based on the dynamic query management as the final prediction of the attribute of the map element; and carrying out loss calculation and training. By optimizing and dynamically managing map queries in a map decoder, the computational consumption of low confidence map queries is effectively reduced. Under the same computing resources, the model is allowed to process a larger number of map queries, so that the construction precision of the vectorized map is improved. The research of automatic driving on-line map construction is further promoted, and the deployment of an on-line vectorization map model is facilitated.

A system and method are disclosed for interfacing with one or more databases using natural-language queries. The system translates a natural-language input into an intermediate formal representation, such as a Concept Query Language (CQL), which references domain concepts rather than database-specific structures. A data access subsystem maps these domain concepts to database-specific queries using a domain dictionary, enabling seamless translation across heterogeneous databases and database management systems (DBMSs). The system supports distributed data retrieval, error recovery, and dynamic query planning. A presentation subsystem formats the results into user-friendly outputs such as charts or tables. The architecture allows for modular grammar and dictionary configuration, enabling rapid adaptation to new domains, schemas, or user roles without procedural code changes. This approach improves accessibility, maintainability, and scalability of database interactions by abstracting technical complexity from end users.

The invention provides a dynamic auditing and risk control supervision method based on multiple Agents, aims to solve the problems of insufficient heterogeneous data processing capability, inaccurate risk identification and low auditing efficiency in the prior art, and constructs a heterogeneous data analysis Agent, a retrieval enhancement generation Agent, an intelligent risk identification Agent and an automatic auditing process Agent based on a multi-Agent system framework. Comprehensive analysis and processing of various data sources are achieved, and the retrieval efficiency and coverage rate of risk elements are improved through vectorization retrieval and dynamic SQL query. The risk identification capability is dynamically optimized through a pre-training model in combination with historical risk case analysis, and high-reliability compliance review is realized in combination with a regulation content library. The method can support various automatic auditing processes, including text review, financial analysis and error correction review, and generation of a standardized auditing result report, obviously improves auditing efficiency and precision, reduces compliance risks, and is widely applicable to the fields of financial auditing, enterprise risk control, compliance check and the like.

The invention provides a multi-data-source online interface service system, which belongs to the technical field of data services and comprises a data source management module, an interface configuration module, a dynamic parameter processing module, a data caching and synchronizing module, a visual integration module and a security management module. A user can connect various data sources through custom configuration, support data extraction in a full scale acquisition or dynamic SQL query mode, and support parameterized requests to realize data dynamic screening. And after the interface service is modified, the returned data can be synchronized to the large visual screen in real time, so that the instantaneity of data display is ensured. Through the method and the device, the problems of single data source, rigid configuration, update delay and the like in the traditional interface service are solved, the flexibility and the response efficiency of the data service are remarkably improved, and efficient technical support is provided for a big data visualization scene.

The invention provides a project-customized adaptive data query and display method, system, device and medium, and belongs to the technical field of data query, the method comprises the following steps: determining the business meaning of each business entity in a key field in a form scene, and solidifying the business meaning in ERP (Enterprise Resource Planning) system configuration; carrying out association relation configuration on the key fields according to the requirements of the business entities; the association relationship comprises an association table, an association field and an association condition; configuring a target field which needs to be brought out by each service entity in the form scene; and in response to a service demand, determining an association relationship of the key fields according to a service entity type, constructing a dynamic SQL query statement by using the determined association relationship, obtaining target data, and filling the target data into a target field of the form scene. According to the method, the personalized requirements of different projects or departments are met, the data query and display content is flexibly configured, dynamic configuration adjustment is supported, the method adapts to the change of service requirements, and the secondary development workload is reduced.

The embodiment of the invention discloses a database dynamic query optimization and resource scheduling method and device and a medium, belongs to the technical field of databases, and solves the problems that the mode of manually adjusting PostgreSQL resource parameters is low in efficiency and causes resource waste. Obtaining query feature data and resource state data corresponding to the database, and performing time sequence processing on the resource state data to obtain a resource prediction result; inputting the query feature data and the resource state data into a preset AI strategy optimization model to output an optimization execution plan and optimization resource allocation parameters; inputting the optimization execution plan into a dynamic injection module; dynamically adjusting the operation parameters of the PostgreSQL based on the optimized resource allocation parameters and the resource prediction result; and in response to the plan injection instruction, selecting a required execution plan in the dynamic injection module so as to execute the optimized query task based on the adjusted operation parameters and the required execution plan.

The invention provides an intelligent cognitive movement rehabilitation task generation method based on knowledge graph dynamic adaptation, and the method comprises the steps: collecting the behavior response data of a patient in real time through a multi-modal sensor array, and inputting a pre-trained state evaluation neural network model after preprocessing; outputting a four-dimensional ability state vector representing cognitive load, motion coordination, attention and fatigue degree, taking the vector as a dynamic query condition, starting multi-hop semantic reasoning in the cognitive motion rehabilitation knowledge graph, screening a candidate task set matched with the current state of a patient and associated with a rehabilitation target, and performing task selection on the candidate task set; according to the matching relation between the capability state and the task difficulty, execution parameters such as task presentation duration and action amplitude are dynamically adjusted, task sequence sorting is optimized based on the path distance, and finally generated personalized rehabilitation tasks are presented in a three-dimensional virtual scene mode through an interaction terminal. A closed-loop control process of real-time data acquisition, state evaluation, task generation and execution is formed, and dynamic personalized adaptation of the rehabilitation training process is realized.

The invention relates to the technical field of relational databases, in particular to a dynamic query optimization method and system based on user SQL statement analysis in a database, and the method comprises the steps: receiving an SQL statement submitted by a user, carrying out the grammar analysis of the SQL statement, and carrying out the query of the SQL statement based on the grammar features and query historical data according to the behavior features and the database operation state; the optimization strategy is applied to the query execution plan, an efficient query execution path is generated, and query is executed; the method has the beneficial effects that the query execution plan can be dynamically generated or adjusted through deep analysis of the SQL statement of the user, and complex query operation can be executed in an optimal mode. For high-complexity SQL (Structured Query Language) such as multi-table connection and nested query, the query execution time can be remarkably shortened, and the overall response speed of a database is improved.

The invention provides a personalized interactive intelligent report method based on a large language model. The method comprises the following eight steps: multi-modal prompt engineering, SQL expert model fine tuning, output correction and safety performance correction, intention perception and context construction, dynamic query generation and execution, multi-granularity insight extraction and narrative arrangement, interactive report presentation and multi-round dialogue. And a set of intelligent and adaptive data analysis report generation system is constructed. The system supports grassroots policemen to perform real-time interaction in a natural language form, automatically generates a structured query language (SQL) query or calls a preset application programming interface (API), and ensures query accuracy, security and high efficiency in combination with a multi-agent review mechanism and a dynamic knowledge graph. Meanwhile, the system has the deep data insight ability, narrative text abstracts and visual charts can be generated, and multi-round dialogue analysis is supported. The intelligent level and application efficiency of data analysis in the public security industry are remarkably improved, and the data use threshold is lowered.

The invention provides a vulnerable road user trajectory prediction method and system based on motion query enhancement, and the method comprises the steps: carrying out the alignment of the current position and direction of each motion sample through a normalization process, carrying out the clustering of the end points of the normalized motion samples, taking an obtained clustering center as a generated static query, and carrying out the prediction of the trajectory of a vulnerable road user. Using extended static queries (DSQ) to extend the coverage of the static queries to provide more diverse motion patterns; extracting and fusing track features and map features to obtain map features and enhanced track features for subsequently obtaining updated query features of each layer, establishing connection between dynamic queries output by a shallow decoder and a deep decoder by using cross-linked dynamic query (BDQ), and coordinating query optimization between decoding layers; and performing prediction result integration on the plurality of homogeneous models to obtain a final prediction trajectory. According to the method, the characteristics capable of reflecting the motion trend of the vulnerable road user can be more effectively extracted, so that the prediction result is more accurate.

Methods and systems for optimally weighting search results in a hybrid search framework are described. Responsive to a search query, query embeddings are obtained using corresponding embedding generators. The query embeddings are provided to search operators corresponding to the embedding generators to obtain corresponding search result sets having search results and associated scores. Optimal weights for each of the corresponding embedding generators are determined using a machine learning model, based on the search query. The search result sets are combined, based on the determined weights and the associated scores, yielding a combined search result set. The disclosed methods and systems dynamically optimize weights applied to search result sets that are retrieved using more than one vector-based search operator (e.g., where each search operator performs a vector-based search using embeddings generated by a corresponding embedding generator), for generating more relevant search results within the hybrid search framework.

The invention belongs to the technical field of database management, and particularly discloses a dynamic query-based adaptive execution engine optimization method, which comprises the following steps of: acquiring real-time multi-dimensional data of a database system; determining an optimal execution plan of the next time period based on the real-time multi-dimensional data; performing transaction consistency verification on the optimal execution plan; and under the condition that the verification is passed, seamlessly switching the current execution plan to the optimal execution plan. According to the method, on one hand, the problem of sudden increase of service flow or resource supply and demand mismatching in a low peak period can be avoided, the flexibility is improved, on the other hand, paths can be automatically switched in network fluctuation or node failure, and the labor cost is reduced.

The invention relates to a zero sample semantic segmentation method and device based on adaptive prompt and dynamic query. The method comprises the steps of obtaining image data and corresponding category semantic information, and performing preprocessing to obtain an input image and an input category semantic text; constructing a zero-sample semantic segmentation model based on adaptive prompt and dynamic query, wherein the model comprises a visual encoder, a text encoder, an adaptive visual prompt module, a dynamic semantic query module and an image segmentation prediction module; inputting the input image and the input category semantic text into the zero sample semantic segmentation model, designing a loss function combining segmentation supervision and different modal alignment constraints, and performing model training and optimization; and under conduction type or induction type zero sample setting, testing the zero sample semantic segmentation model by using an image to be processed, and outputting a corresponding segmentation result. And the zero sample semantic segmentation accuracy and robustness in a complex scene are remarkably improved.

The invention discloses a video clip positioning method based on self-adaptive static enhancement of a pre-trained large model, which comprises the following steps of: constructing a training sample set, constructing a video clip positioning model, generating a static query according to a dynamic query, respectively encoding to obtain a dynamic query feature and a static query feature; the method comprises the following steps of: segmenting a video, coding to obtain video features, fusing the features by adopting a dynamic and static interaction mechanism based on memory enhancement to obtain dynamic interaction features and dynamic-static interaction features, and reasoning to obtain a moment query and an initial prediction time period; fine adjustment is carried out on the initial prediction time period according to dynamic interaction features, dynamic-static interaction features and moment query, a final prediction time period is generated, a training sample set is adopted to position a video clip positioning model, and the trained video clip positioning model is adopted to carry out video clip positioning. According to the invention, the accuracy of video clip positioning can be obviously improved.

The application provides a weak road user trajectory prediction method and system based on motion query enhancement, which comprises the following steps: aligning the current position and direction of each motion sample through a normalization process, clustering the end points of the normalized motion samples, taking the obtained cluster centers as generated static queries, using extended static queries (DSQ) to extend the coverage of the static queries to provide more diverse motion patterns; combining the extraction and fusion of trajectory features and map features to obtain map features and enhanced trajectory features, which are used to obtain the update query features of each layer subsequently, using cross-connection dynamic queries (BDQ) to establish a connection between the dynamic queries output by the shallow and deep decoders, coordinating the query optimization between the decoding layers; and integrating the prediction results of multiple homogeneous models to obtain the final prediction trajectory. The application can more effectively extract features that can reflect the motion trend of weak road users, making the prediction results more accurate.