711 results about "Relational table" patented technology

The present invention relates to the technical field of knowledge graph completion tasks, and particularly relates to a knowledge graph link prediction method. The method comprises: using a precoding model to obtain an embedding layer vector, and constructing a corresponding masked triple; adding a corresponding position code to each element in the masked triple, so as to obtain a corresponding input sequence, inputting the input sequence into a trained main masking model, and outputting an entity classification probability; and on the basis of the entity classification probability, predicting potential candidate entities. The method further comprises: concatenating semantic information corresponding to the embedding layer vector and structural information obtained by an embedding model, so as to obtain fused head entity and relation representations, and constructing a corresponding fused masked triple; and adding a corresponding position code to each element in the fused masked triple, so as to obtain a corresponding fused input sequence. The present invention uses a precoding method, thereby effectively reducing the training burden on a model, and improving the inference speed of a model; and a fusion module is used before inputs are fed into a main masked model, thereby ensuring the integrity of textual description information and improving prediction accuracy.

The invention discloses a conversion method and system based on SysML and Modelica model semantic mapping. The method comprises the following steps that S1, a Modelica standard model library is imported into a SysML modeling tool; s2, completing the definition of behavior elements, and forming an SysML model instance; s3, exporting a Modelica model which can be executed by the Modelica simulator; s4, establishing a mapping relation table between SysML model elements and Modelica model elements; s5, when a change event occurs in the SysML model, updating the content of the corresponding Modelica model; s6, the Modelica model is imported into the SysML model, and a corresponding SysML model instance is generated; s7, simulation result mapping and behavior modeling generation are executed, a state transition diagram structure is formed, and structure updating of internal block diagrams in the system model is completed. According to the method, bidirectional conversion and joint simulation optimization between models are realized, and the method is suitable for cross-platform modeling, collaborative design and functional verification scenes of a complex system.

The present invention relates to the technical field of algae proliferation risk prediction. Disclosed is a machine learning model-based algae proliferation risk prediction method. The present invention comprises collecting water quality data, geographic information and historical algae proliferation event data and preprocessing the collected data. In the present invention, a graph structure between monitoring points is constructed on the basis of spatial features, each monitoring point is represented as a node in a graph, and a relationship between nodes is represented as an edge, so that spatial dependency between monitoring points can be effectively captured. The weight of each edge is defined as the reciprocal of the distance between the nodes, so that the model can more accurately reflect the effect of adjacent monitoring points on each other. By constructing the graph structure to represent the relationship between the monitoring points, large-scale spatial data can be efficiently processed. A graph neural network is capable of performing a convolution operation on a graph structure, effectively aggregating information of adjacent nodes, thereby improving the spatial relationship expression capability and the model prediction accuracy.

The invention discloses a retrieval enhancement generation system and method for customizing multilevel intention recognition. The retrieval enhancement generation system comprises a multilevel intention label and document management module, a multilevel intention recognition module and a retrieval enhancement generation module. The multi-level intention label and document management module can self-define and build a corresponding relationship among a multi-level intention label, an intention label and a document level by level to form a label document corresponding relationship form; the multi-level intention recognition module recognizes intentions of the user level by level according to the multi-level intention labels and the multi-level intention labels established by the document management module, and gives corresponding intention labels; and the retrieval enhancement generation module performs vector similarity search in a document range delineated by the intention tag, and transmits a vector similarity search result to the large language model for result generation. Through step-by-step subdivision of the user intention, the document retrieval range is gradually reduced to the corresponding intention label, so that the answer generated by the large language model in the generation stage is more accurate.

The invention relates to the technical field of government affair information management, in particular to a cloud-based government affair information management method, which comprises the following steps of: S1, adding a sensitivity label for each piece of government affair data in a cloud storage layer; s2, extracting data from the government affair database and generating a dynamic data view with an authority mark; s3, establishing a mapping relation table between routing keys and data source addresses; s4, acquiring a requester department function code, a business scene code and a target data identifier; s5, matching a target data source, and based on the authority mark in the dynamic data view, executing a desensitization operation on the sensitive field; and S6, if the genetic marker bit of the original data is true, superposing the data security classification in the original tag and the current security classification to form a new tag. According to the method, by constructing the sensitivity label and the dynamic authority control mechanism, safe inheritance and accurate access of government affair data in cross-department circulation are achieved, and the safety and compliance of data management are remarkably improved.

The invention relates to the technical field of computers, and provides a distributed AI large model warehouse downloading acceleration method, which comprises the following steps that: a newly added node completes synchronization of networking and a file mapping relation table between the newly added node and nodes of the whole network based on a gossip protocol; each node constructs a Hash ring through a consistent Hash algorithm based on the node identifier, determines a target Key according to the deployment identifier, and clockwise selects a next node of the target Key as a main node in the Hash ring; when a local intranet node receives a downloading request, firstly searching a target file in a local file list; if the target file is not found locally, initiating an intranet back-to-source request to a target node recorded in the file mapping relation table to obtain the target file; and if the intranet is not hit, a public network back-to-source request is initiated to the main node to obtain the target file, a decentralized networking mechanism based on the gossip protocol and a file mapping relation sharing mechanism are adopted, so that repeated downloading among the nodes is effectively avoided, bandwidth waste is reduced, and public network bandwidth occupation is remarkably reduced.

The invention provides a query statement generation method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence, in particular to the field of computers, the field of databases and the field of deep learning. The query statement generation method comprises the steps of performing intention recognition on a demand text based on business data to obtain a demand intention; in response to the demand intention meeting a preset demand condition, processing the demand intention by using a language model to obtain an execution logic structure, the execution logic structure comprising a plurality of execution logic elements based on structured representation, and a structured relationship among the plurality of execution logic elements representing an execution process of executing the query statement; determining target mode information matched with the text semantics represented by the execution logic element from preset database mode information; performing semantic understanding on the plurality of execution logic elements and the target mode information by utilizing a language model, and outputting a plurality of pieces of intermediate structured data for describing the query statement; and generating a query statement according to the intermediate structured data.

The invention relates to the technical field of information security, and particularly provides a medical Web service multilayer dynamic protection method and system, and the method comprises the steps: obtaining access source information, terminal equipment information and user behavior information when a user initiates a data access request; obtaining an access risk score for representing a current access risk level according to the access source information, the terminal equipment information and the user behavior information; querying a pre-constructed mapping relation table about the risk score and the risk strategy according to the risk score, so as to obtain an access permission and a data display strategy for the current data access request; performing data processing on medical data which is prepared to be returned and corresponds to the data access request according to the access permission and the data display strategy, and then sending the medical data to the user terminal; the method can effectively solve the problems that high data leakage risk events cannot be processed and medical data leaks due to the fact that real-time and dynamic data leakage risk assessment cannot be carried out.

The invention discloses a cross-system full-link metadata processing method and device.According to the method, a separated metadata storage architecture is adopted, entity attributes are stored in a JSON document form, relationships between entities are stored in an independent relationship table in a quintuple structure, and efficient storage and rapid query are achieved; through a topology driving processing framework, depth-first traversal is carried out based on a hierarchical structure of a directed acyclic graph, and multi-thread concurrent processing is started at a mode level, so that the processing efficiency of large-scale heterogeneous metadata is improved; the entity life cycle event scheduler is used for monitoring entity change events, triggering index updating, relation maintenance and other operations, and real-time response of metadata change is ensured. Meanwhile, in combination with a search index synchronization mechanism, a hierarchical entity association model, a connector abstraction factory and a fingerprint cache optimization mechanism, efficient management, real-time synchronization and deep association analysis of cross-system metadata are realized. According to the method, the storage efficiency, the real-time performance, the concurrent processing capability and the expansibility are remarkably improved.

The invention discloses a real-time data stream processing method for multi-device collaborative management of a dome cinema, which relates to the technical field of cinema device data management, and comprises the following steps of: establishing communication connection between a central management node and a plurality of playing devices, collecting device identifiers, projection area numbers and geometric position parameters of the playing devices, and establishing a central management node; generating an equipment mapping relation table; realizing local clock correction of the playing equipment based on a unified time synchronization protocol; the central management node generates a control instruction stream according to the equipment mapping relation and the playing content, the instruction stream comprises space frame distribution information and fusion control parameters, and the space frame distribution information and the fusion control parameters are distributed to the playing equipment through communication channel numbers; and the playing device completes image geometric transformation and edge fusion processing according to the received control instruction and then executes synchronous playing. According to the invention, multi-device automatic mapping is realized, the time synchronization precision is improved, the image playing is coordinated and consistent, and the collaborative display capability and the playing stability of the dome cinema are improved.

The invention discloses a resource supervision system and method for a steel pipe manufacturing industry, and relates to the technical field of resource supervision. The system comprises a data association and mapping module, a dynamic binding rule module, a verification standard and risk division module and a real-time monitoring and updating module. The data association and mapping module associates and divides resource attributes and process parameter data and establishes a mapping relation table; the dynamic binding rule module analyzes the associated features, and establishes a basic and extended binding rule in combination with product attribute requirements; the verification standard and risk division module is used for presetting a verification standard, dividing a resource mismatching risk level and an early warning threshold value; the real-time monitoring and updating module collects real-time data for comparison and verification, triggers early warning, pushes adjustment suggestions, and records a result to update a mapping table. According to the invention, accurate matching and dynamic supervision of resources and processes are realized, and the product quality and the production efficiency are improved.

The invention discloses a multi-terminal detection score offset correction method and system, particularly relates to the field of financial risk control, is used for solving the problem of steady-state offset generated in the merging process of multi-terminal detection scores, and comprises the following steps: receiving cross-terminal original behaviors and collecting metadata under unified time reference and unified field specifications; the method comprises the following steps: extracting a terminal fingerprint table according to a sampling beat, a sensing path, a rendering mode and a quantification strategy, generating a time alignment table, dividing a stable fragment, a change fragment and a transition fragment by taking the terminal fingerprint table and the time alignment table as conditions, and constructing a segmented order-preserving mapping relation table; a cross-terminal consistency constraint graph is generated based on the segmented order-preserving mapping relation table to form a correction rule set, rules are verified in an offline playback window, a micro-correction list is output, and the correction rule set and the micro-correction list are read in the online merging stage to execute correction and output a unified scale score. And meanwhile, generating a feedback abstract, submitting, filing and publishing a version.

The application provides a pollution site portrait construction method and system based on knowledge graph reasoning, which comprises the following steps: collecting key data in a pollution site investigation report; obtaining a pollution land block structured data table of each pollution land block based on the pollution site investigation report, and obtaining a knowledge graph ontology structure; integrating the pollution land block structured data table to form a pollution site structured data table; obtaining a node table, a relationship table and an attribute table of each node in the knowledge graph ontology structure to obtain a triple table; establishing a graph database based on the triple table, and performing reasoning based on the graph database to obtain known pollution information of the pollution site; performing potential risk reasoning on the triple table based on a graph neural network to obtain potential pollution information of the pollution site; and obtaining a pollution site portrait based on the known pollution information of the pollution site and the potential pollution information of the pollution site. The method of the application evaluates the pollution site based on known facts and potential risk reasoning, and improves the accuracy of the evaluation.

The invention discloses a door lock collaborative identity recognition method and system, and the method comprises the steps: obtaining a locally stored MAC-face binding relation table according to face recognition verification completed by a resident through an intelligent door lock; according to the moving path of the resident from the floor entrance to the home door lock, identity recognition is completed through cooperation of multicast communication and the home door lock, an identity confirmation result is obtained, and a directional multicast network is constructed; according to a leaving event that a resident opens a door lock from a room, triggering a passing door lock to collect leaving person information, and obtaining a leaving person identity confirmation result; and selecting to send a unicast or multicast confirmation message by the own door lock according to the completion degree of identity confirmation of the leaving person, and triggering the passing door lock to send an IGMP leaving message to the router to obtain a simplified multicast forwarding table. According to the embodiment of the invention, seamless identity cooperation of the whole process from home returning to home leaving can be realized, and the automation degree of home security and protection and the user experience are improved.

The invention relates to a full-unloading regular matching system and method based on an FPGA (Field Programmable Gate Array). The system comprises a regular rule compiling unit and an FPGA full matching unit, the regular rule compiling unit is used for receiving a regular rule set, extracting each rule fixed feature substring, compiling the uniform format isomorphic NFA, and constructing a mapping relation table of the substrings and the corresponding NFA; the FPGA full matching unit comprises a parallel high-speed character string matching engine array, a character string-NFA mapping table module, a full-quantity regular data memory, a reconfigurable general NFA engine module and a matching result output module; and the reconfiguration engine module is used for executing full unloading regular matching on the input network traffic: scanning the traffic through the engine array, screening to-be-verified traffic containing a fixed feature substring and outputting a substring identifier, querying the NFA identifier through the mapping table module, and loading NFA matching by the reconfiguration engine module. By adopting the method, low delay and high throughput performance of regular matching in a high-speed network environment can be ensured.

The invention discloses a hierarchical semantic enhanced large model zero sample learning method, which is characterized by comprising the following steps: firstly, performing semantic enhancement on category names by using a large language model to generate rich text description, and constructing a dynamic and hierarchical semantic prototype by combining original semantic information, the prototype comprising global concepts, local attributes and relation representations; secondly, extracting global semantic features and local detail features of the image by adopting a vision-language large model and convolutional neural network double-branch structure, and fusing the global semantic features and the local detail features through an attention mechanism to obtain enhanced visual representation; finally, multi-level global alignment, local alignment and relation alignment are designed and jointly optimized, accurate mapping of enhanced visual features and hierarchical semantic prototypes is achieved on multiple granularities, and classification of invisible categories is finally completed. The objective of the invention is to solve the problem of limited model generalization ability caused by insufficient semantic representation and single vision-semantic granularity in the existing zero sample learning method, and to enhance semantic representation by introducing knowledge of a large language model and innovatively implement hierarchical alignment, so that the robustness of the zero sample learning method is improved. And the recognition precision and robustness of the model in traditional and generalized zero sample learning scenes are remarkably improved.

The invention discloses an optimal control method and system for a central air conditioner. The method comprises the steps that a starting instruction is responded; selecting an operation parameter combination from a preset database and sending a test request to the central air-conditioning system; energy efficiency detection results are received, wherein the energy efficiency detection results comprise energy consumption, equipment efficiency and user thermal comfort indexes; determining a next round of test combination based on the detection result and the mapping relation table; and selecting the combination with the highest energy efficiency from the tested combinations as a final result and configuring the final result to the system. Energy efficiency self-adaptive optimization is achieved by establishing the preset database and the mapping relation table in combination with a multi-round test mechanism, the test is intelligently guided according to the divided energy efficiency value interval, blind traversal is avoided, and the search efficiency is improved. A termination test strategy is set to effectively control a period and reduce interference, a load prediction model is introduced to make optimization more targeted, energy waste caused by lagging response is avoided, key parameters are collaboratively optimized, comfort is improved, and energy consumption is reduced.

The invention relates to the technical field of knowledge graphs, and particularly discloses a knowledge graph construction method for structural information aggregation, which comprises the steps of data collection, entity semantic understanding, structural context aggregation, weighted relation representation and fusion graph construction. According to the scheme, discrete, disordered and heterogeneous entity attribute information in a cross-source knowledge graph is converted into continuous, ordered and structured entity attribute vectors to serve as carriers of structural information aggregation; step-by-step aggregation from a local triple structure to a graph-level semantic structure is realized, structural semantic roles of entities in different knowledge graphs are reserved, selective modeling is performed on adjacency relations through an attention mechanism, relation semantic similarity is introduced, and consistency and discrimination of entity representation are enhanced; entity-level and structure-level closed-loop enhancement processes are realized, and unification of semantic space of the knowledge graph and improvement of knowledge density are realized through alternate execution of multiple complementation and alignment.

The invention discloses a method, a system and equipment for carrying out resource management on a database and a medium. The method comprises the following steps: creating at least one resource group in a database, and defining configuration information of each resource group; based on the binding relation table of each resource group, binding each resource group, and determining the binding type of each resource group; based on the binding type of each resource group, updating the configuration information of each resource group through a resource group operation interface to obtain target configuration information of each resource group; executing a corresponding query operation based on the target configuration information of each resource group and the state of each resource group; monitoring whether the state of each resource group is changed or not, triggering a corresponding resource adjustment strategy when the state of each resource group is changed, and recording change information; according to the method, resources of different tenants are isolated through a resource group mechanism, so that database-level resource isolation and fine management are realized, and the stability of a multi-tenant environment is improved.

The invention discloses a method for archiving, restoring and reading a DLF file generated by a record file and a video. The method comprises the following steps: acquiring original data of the record file and the video; generating a mapping relation table between the record file and the video time axis; carrying out structured processing on the record file to generate a record metadata file in an XML (Extensible Markup Language) format; creating a DLF file container; segmenting the video file into a key frame sequence, and generating a dynamic video OFD component; embedding the structured record file into an expansion layer of a dynamic video OFD component, and establishing a space-time association index of a record text and a video key frame; registering a dynamic interaction event in the guide file, and constructing a multi-layer nested structure; generating an interactive directory tree, automatically creating a navigable bookmark identifier based on a record paragraph keyword, and performing dynamic retrieval; and performing digital signature verification and archiving on the combined DLF file, thereby realizing associated reading in an archive system.

The invention discloses a fault recovery method of a storage pool, computer equipment, a storage medium and a product, and relates to the technical field of computers, and the method comprises the following steps: preferentially identifying and scanning a metadata area after the storage pool is offline, screening persistent effective metadata before offline, and storing the effective metadata in a database; metadata reconstruction is taken as a core to preferentially complete integration and restoration of a mapping relation between a logic address and a physical address instead of waiting for synchronous reconstruction of business data, so that the dilemma of long-time logic unavailability of a storage pool caused by synchronous processing of the metadata and the business data in a traditional restoration scheme is effectively broken through; according to the method, the target mapping relation table is quickly generated through conflict resolution and deletion completion processing, so that the interruption duration of the storage pool from offline to online can be remarkably shortened while the mapping accuracy is guaranteed, and the service access delay caused by unready metadata can be avoided.

The invention relates to the technical field of power systems, and discloses an automatic checking and correcting system and method for relay protection setting values of a booster station, and the system comprises a configuration analysis module which is used for analyzing an SCD configuration file of a transformer substation, extracting parameters of a protection device, and importing a setting value list to establish a mapping relation table; the data acquisition module is used for acquiring a constant value, a soft pressing plate state and a communication message of the IED equipment in real time through an MMS protocol; and the intelligent verification module is used for executing constant value comparison, pressing plate state verification and communication abnormity responsibility judgment. The invention develops a handheld intelligent inspection system, and solves the three major problems of low efficiency, high error rate and prominent safety risk in the checking operation of the relay protection constant value of the traditional transformer substation. According to the scheme, full-process automation of configuration analysis, data acquisition, intelligent verification, safety correction and report generation is realized, so that the routing inspection of the secondary equipment is upgraded from an extensive mode to a precise operation and maintenance normal form, and a guarantee is provided for safe and stable operation of a power grid.

The invention discloses a risk decision scheme determination method and device based on financial service workflow, equipment and a storage medium, and relates to the technical field of credit extension businesses, and the method comprises the steps: adding a to-be-processed parameter identifier in an initial parameter relation table constructed based on a scheme request parameter of the financial service workflow, updating the to-be-processed parameter identifier and the parameter value to obtain a target parameter relation table; analyzing the consumption relationship of each task node in the target parameter relationship table, and generating a parameter dependency relationship graph by using a parameter dependency link constructed based on a directed acyclic graph structure and the consumption relationship; generating a relation algebraic expression by using a minimum parameter passing closure determined based on the parameter dependency relationship graph and a target parameter relation table; and processing the relational algebraic expression to obtain a to-be-transmitted parameter, transmitting the to-be-transmitted parameter in the minimum parameter transmission closure so that the task node executes a query task of the to-be-transmitted parameter, and determining a risk decision scheme based on an execution result. Therefore, the efficiency of determining the risk decision scheme can be improved.

The invention provides a product recommendation method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of artificial intelligence, the technical field of big data, the technical field of block chains and the technical field of privacy computing. The method comprises the following steps: acquiring structured data and unstructured data of a target user; extracting preference features based on the unstructured data, and obtaining a time sequence preference portrait by using a time sequence attention model; obtaining a target user portrait based on the time sequence preference portrait and the structured data; obtaining a multi-dimensional sequential relation graph, and reasoning the multi-dimensional sequential relation graph by using a graph neural network to obtain relation representation; executing collaborative filtering scoring based on the target user portrait and the relationship representation, and generating an intermediate product score; taking the generated intermediate product score as an input, and outputting a candidate product score based on a recommendation optimization function containing a space-time weight; and performing product recommendation based on the candidate product scores.

The invention discloses a human and house data comprehensive treatment method, system and equipment based on a multi-level address model, and a medium, belongs to the technical field of data treatment, and aims to solve the technical problem of how to effectively integrate human and house data sources of multiple parties and improve the accuracy, consistency and availability of human and house data. The defects that in the prior art, people and house data are scattered, address description is not standard, the incidence relation is inaccurate, data quality is uneven and a unified identification system is lacked are overcome. According to the technical scheme, the method comprises the steps that a five-level core data table comprising a community table, a building table, a house table, a people and house relation table and a personnel information table is constructed; the method comprises the following steps: accessing original human and house data from a plurality of data sources, performing intelligent analysis and standardization processing on address information, and obtaining data after standardization processing; establishing a house unique identification system and a human-house multi-dimensional relation model based on the standardized data; evaluating and repairing the data quality; and standardized human and house data service is provided for the outside through the data service interface.

The embodiment of the invention provides a task scheduling method, system and equipment, and the method comprises the steps: firstly, obtaining the health level of each abnormal node in a node cluster; then, in response to a scheduling request of a to-be-scheduled task, determining a task priority of the to-be-scheduled task; and then, according to a level mapping relationship, a target node for executing the to-be-scheduled task is screened out from the node cluster, and the level mapping relationship represents a mapping relationship between the task priority and the health level of the abnormal node. And finally, distributing the to-be-scheduled task to the target node. Through the technical scheme of the embodiment of the invention, all computing resources in the distributed system can be fully utilized, and the influence caused by performance aging of the hardware nodes is solved on the basis of not expanding and replacing the hardware nodes.

The invention discloses an optical remote sensing ground feature relationship semantic understanding system and method in a localized environment, and the system constructs a remote sensing scene relationship analysis module, a multi-modal scene knowledge base module, a remote sensing scene graph construction module, a remote sensing scene representation module and a multi-modal sample pair construction module. Carrying out programmed modeling on the ground feature relationship by utilizing a code big language model adaptive to a domestic platform, and generating a scene graph triple; through a double-branch double-time phase comparison learning network, time sequence vision and structure knowledge are fused, and cross-modal joint representation learning is realized. According to the method, the problems of incomplete surface feature relationship expression, inaccurate modeling and lack of real-time semantics in the prior art are solved, deep optimization is carried out in the aspects of data annotation, heterogeneous computing power management and the like aiming at the localized environment, and the accuracy and integrity of semantic understanding of the surface feature relationship and the operation efficiency of the semantic understanding on a domestic software and hardware platform are remarkably improved.