917 results about "Relational table" patented technology

The invention belongs to the technical field of equipment health monitoring and anomaly detection, and discloses an industrial time series data learning fusion and anomaly detection method, which comprises the steps of constructing an IP-PLC mapping relation table, collecting multi-modal data, constructing a physical constraint parameter list, and generating structured data and a storage index. Time domain features and frequency domain features are extracted, a spatial topological graph is constructed, node spatial feature vectors and edge association strength are extracted, spatial association feature vectors are generated, a constraint rule base is constructed, and an enhanced feature set is formed; aggregating the enhanced feature set and the constraint rule base, generating a multi-dimensional feature matrix and a global reference parameter table, further constructing a global reference system, obtaining an equipment-level anomaly probability matrix, and generating a working condition-level anomaly probability matrix; hierarchical optimization is carried out through hierarchical modeling, and an optimization parameter set is generated; constructing an alarm response mechanism, and performing reverse updating to form closed-loop iteration; and an interpretable and extensible solution is provided for equipment health management in a complex industrial scene.

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 relates to the technical field of data monitoring, and discloses an intelligent data monitoring method and a monitoring platform, and the intelligent data monitoring method comprises the steps: constructing a dynamic heterogeneous hypergraph model, and representing the complex relation and time sequence evolution characteristics among multi-source data nodes; based on a dynamic heterogeneous hypergraph model, realizing high-order relationship representation and calculation, and obtaining embedded representation of nodes and relationships; performing heterogeneous graph information transmission and aggregation by utilizing embedded representation of nodes and relationships, and capturing complex interaction characteristics among multiple nodes; according to the complex interaction characteristics, an abnormal propagation rate model is established, and accurate prediction of an abnormal diffusion path is realized; based on the abnormal propagation rate model and the abnormal diffusion path, multi-level collaborative anomaly detection is implemented, and collaborative faults across subsystems are identified; according to the invention, the abnormal propagation path can be predicted in advance, the abnormal prediction accuracy is improved, and the system fault response time is advanced.

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 invention relates to the technical field of the Internet of Things and artificial intelligence, and discloses an equipment maintenance fault intelligent analysis method and system based on the Internet of Things, and the method comprises the steps: representing an entity and a relation as a time function, and constructing a time sequence knowledge graph; monitoring vector change in the knowledge embedding space, detecting a concept drift phenomenon and mining an equipment performance evolution mode; constructing an equipment state transition model by using a recurrent neural network, predicting the future state of the equipment and evaluating the fault risk; collecting feedback processing knowledge conflicts and updating the model; generating multi-time-scale fault risk early warning and maintenance suggestions; according to the method, the knowledge acquisition efficiency is improved, dynamic knowledge support of the full life cycle of the equipment is provided, early recognition of gradient faults is realized, the method has adaptive knowledge updating capability, multi-dimensional fault analysis is provided, maintenance resource configuration is optimized, and the equipment management efficiency is improved.

The invention discloses a computer network security situation awareness system and method, and relates to the technical field of computer network security, and the method comprises the steps: generating a dynamic gene pool through an antigen-antibody matching algorithm based on an initial quantum trust vector, and building a mapping relation table of an antigen feature vector and an antibody defense strategy; triggering a quantum alarm according to an entanglement entropy mutation event in the real-time quantum state flow data, matching a biological antibody strategy corresponding to a current network attack feature based on a mapping relation table, and fusing the quantum alarm and the biological alarm to generate an attack path topological graph; the network security situation is perceived by analyzing the quantum state change of the abnormal node ID and combining an attack path topological graph and a dynamic gene pool. Quantum entanglement pairs are distributed to network nodes through the quantum key distribution center, and initial quantum trust vectors are generated, so that the unknown attack resisting capability of the network is remarkably improved, and efficient and accurate network security situation awareness is realized.

The invention relates to the technical field of wireless communication and edge computing fusion, in particular to a wireless network resource allocation method based on edge intelligence, which comprises the following steps: S1, collecting a multi-dimensional parameter set of network edge nodes in real time; s2, converting node position coordinates into a network topological structure map, and mapping service type labels and bandwidth requirements into service requirement feature vectors; s3, generating a domain division matrix based on weight distribution of the network topological structure atlas; s4, outputting a resource block configuration tensor according to the time-frequency feature of the business demand feature vector; s5, generating a resource allocation mapping relation table; and S6, generating and executing a node-level resource scheduling instruction set. According to the invention, by constructing a resource scheduling mechanism fusing a topological structure and service features, precise resource allocation and dynamic load balancing of a cooperative domain level and a node level are realized, and the efficiency and the resource utilization rate of multi-task concurrent processing in an edge computing environment are improved.

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 discloses a knowledge graph knowledge expansion method, which comprises the following steps of: extracting a sub-graph of a target entity through a knowledge graph sub-graph extraction method based on K-hop neighborhood and link length constraint to reduce resource consumption, and then extracting neighborhood relation representation information and neighborhood relation link representation information by utilizing an attention mechanism; fusing the two kinds of relationship representation information by utilizing a relationship fusion method based on a gating mechanism, and further performing relationship prediction completion; and finally, model optimization is performed by combining comparison loss and cross entropy loss, more negative samples are increased through a subgraph structure-based negative sampling method, feedback on sparse data is enriched, the defect of the cross entropy loss in generalization performance is made up, and effective expansion of the knowledge graph is realized. According to the method, the relationship prediction accuracy of the knowledge graph is improved, the data sparsity problem of the knowledge graph is effectively relieved, the manual annotation cost is remarkably reduced, and excessive dependence on expert knowledge is reduced.

The invention relates to the technical field of monitoring video processing. The invention provides a monitoring video playback method and device based on a multi-modal model. The method comprises the following steps: fragmenting real-time monitoring videos of different service scenes according to specified duration to generate a video fragment sequence; further generating a unique index identifier for each video clip in the video clip sequence, and establishing a mapping relation table between the index identifiers and the video clips; according to the method, CNN and VGGish models are adopted to extract multi-modal features from real-time monitoring videos of different business scenes, a Transform model is adopted to extract global dependency relationship features, the extracted multi-modal features and the global dependency relationship features are fused to obtain fused features, natural language description is further generated to construct a vector space of cross-modal retrieval, and the cross-modal retrieval is realized. Storing the multi-modal features into a vector database to form a storage path of each video clip, wherein the multi-modal features comprise audio features and behavior recognition features; and receiving a query statement input by a user, converting the query statement into vector representation, and performing similarity search in the vector database to obtain a video clip corresponding to a to-be-queried statement. According to the method, retrieval can be carried out more accurately, and the playback efficiency is 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 an unbiased scene graph generation method for relieving long-tail distribution. The method comprises the following steps: S1, constructing a model; s2, data preprocessing; s3, object feature extraction; s4, constructing a graph learning structure (GLS); s5, a regional message passing network (RMPN); s6, generating a pseudo label; s7, defining a loss function; s8, performing model training; s9, generating a pseudo tag and a triple; and S10, carrying out iterative training and optimization. According to the unbiased scene graph generation method for relieving long-tail distribution, the correlation between entities is calculated by utilizing GLS, the relation graph is optimized, the relation representation of head and tail categories is enhanced, meanwhile, the RMPN improves the semantic representation of objects and relations through an information transmission mechanism, pseudo labels are generated on the basis of unlabeled relations in a training set through a pseudo label generation mechanism, and the robustness of the unbiased scene graph generation method for relieving long-tail distribution is improved. And in combination with a high-confidence screening mechanism, generating a learning sample of a pseudo-triple enhanced tail category.

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 discloses a large model retrieval processing method and device. The method comprises the following steps: obtaining query data; determining K document blocks reaching a preset similarity with the query data, wherein K is a positive integer; dividing the K document blocks into at least one set, wherein the correlation among the document blocks contained in each set meets a set condition; key value data of the at least one set is obtained at least through a key value storage structure, the key value storage structure stores the key value data in a tree structure mode, and the connection relation between nodes in the tree structure represents the dependency relation between the different key value data. The key value data stored in the key value storage structure are cached in different types of cache modes.

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 belongs to the technical field of digital assembly process design, and discloses a product model data structured processing method, electronic equipment and a storage medium, and the method comprises the steps: constructing a data storage structure of a product model assembly, which comprises an attribute table and a relation table; identifying components, parts, standard components and assembly digital models in the product model data; extracting attribute information of components, parts, standard components and assembly digital models in the product model data, and storing the extracted attribute information into an attribute table; and extracting contact relation information of the parts and the standard parts in the product model data and assembly relation information of the assembly digital model, and storing the extracted contact relation information and assembly relation information into a relation table. According to the method, the problems of low efficiency, large workload and poor accuracy of existing three-dimensional model data structured processing are solved.

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 virtual machine network connectivity test method and device, a medium and a product, relates to the technical field of cloud computing, and is applied to a detection service, the detection service is configured with a single preset detection network card, the preset detection network card is connected with a plurality of l2gateway ports, each l2gateway port corresponds to a virtual router, the IP address and the MAC address of each l2gateway port are the same, and the IP address and the MAC address of each virtual router are the same. Comprising the following steps: determining a target network label corresponding to a to-be-tested target virtual machine from a preset mapping relation table; packaging based on the target network label to obtain a detection message, and sending the detection message to a target l2gateway port matched with the label by using a preset detection network card; and determining a target virtual router matched with the target l2gateway port, so that the target l2gateway port forwards the detection message to a target virtual machine in the target virtual router to perform a network connectivity test.

The invention provides a general document data conversion method and device, and the method comprises the steps: carrying out the business scene analysis and rule field extraction of an input text of a user through a large model business flow processor and an intelligent agent, generating a recommendation rule template, and carrying out the processing of the recommendation rule template; updating a configuration field of the recommendation rule template according to an adjustment requirement text input by a user to generate a target document conversion rule template; and according to the target document conversion rule template, performing general data mapping conversion on the source document data to generate a document data model of the target document, and realizing dynamic code-free automatic configuration by configuring the document conversion rule template, so that the reusability is improved, and the user experience is improved. Automatic conversion of isomorphic data and heterogeneous data to a general document model in a complex scene can be met, the delivery period is shortened, and the delivery cost is reduced; multiple cancel-after-verification relationships can be recorded according to the cancel-after-verification relationship table, an arbitrary cancel mechanism is supported, the bill redundancy is reduced, and the flexibility of cancel-after-verification relationship processing in bill conversion is improved.