602 results about "Field mapping" patented technology

The invention provides a cross-domain data integration and fusion method based on a large model, a terminal and a storage medium, and the method comprises the steps: carrying out the format conversion and cleaning of cross-domain original data through a unified data access adaptation layer, and outputting a standardized data set; carrying out semantic understanding integration by utilizing a large model, analyzing field names, generating field semantic tags in combination with a business term knowledge base, judging business relevance by calculating similarity, and generating an inter-domain field mapping relationship; and performing technical standard conversion and data fusion processing based on the mapping relationship, and performing intelligent quality treatment by adopting a mode of combining a quality treatment standard library and a large model. According to the method, a large model is introduced to assist data understanding and treatment, so that the technical problems of insufficient semantic understanding, non-uniform standards and the like in existing cross-domain data integration are effectively solved, and the accuracy and automation level of data integration are remarkably improved.

The invention belongs to the technical field of multi-target global dynamic production scheduling, and discloses a manufacturing resource dynamic production scheduling data comprehensive processing method, which comprises the following steps: acquiring multi-source heterogeneous data, constructing a field mapping table, constructing an equipment fingerprint database in combination with historical equipment performance data, dynamically adjusting confidence coefficient weight, and further judging an equipment state. Based on an equipment state judgment result, integrating operator report data and multi-source heterogeneous data through a preset rule base trigger event, and generating an abnormal event flow with a context tag; based on the abnormal event type and the equipment state judgment result, a recommendation scheme is constructed through a production scheduling knowledge graph, and a production scheduling instruction set is generated; according to the production scheduling instruction set, generating a local optimization scheme and a global optimization strategy, and forming a final execution scheme; calculating a resource utilization rate and a bottleneck process index through the 3D virtual workshop model, and generating a difference analysis report; according to the method, global resource scheduling and closed-loop iteration are realized, and the production efficiency and the system robustness are improved.

The invention relates to the technical field of data exchange, in particular to a data exchange system of a nested tag structure, which comprises a data carrier adaptation module, a structure analysis module, a field mapping module, a logic driving module and a script generation module. According to the method, carrier identifiers are generated through comparison of MIME types, file header features are positioned through pattern matching, the recognition accuracy and adaptability are improved, a nesting relation is calculated through depth-first traversal of file paths, node sequences are generated through analysis in combination with a DOM tree, the label analysis efficiency is optimized, and a mapping table is generated through algorithm matching of field identifiers and database codes; the method comprises the following steps: dynamically constructing a field mapping relation, reducing manual configuration, constructing a decision matrix based on a mapping table, executing logical operation to generate a status bit and triggering a threshold write-in instruction, enhancing strategy dynamic adjustment capability, reducing semantic ambiguity through multi-algorithm collaboration and a dynamic decision mechanism, improving data exchange robustness, and supporting real-time state response. And the transmission timeliness of the industrial Internet of Things is ensured.

The invention provides an intelligent analysis and execution method and system for database cascading operation, and relates to the technical field of database management.The intelligent analysis and execution method comprises the steps that a target table and an association table are extracted through a recursive query algorithm, field mapping analysis is carried out to generate a dependency weight matrix, cyclic dependency is recognized based on the matrix and decomposed into directed acyclic subgraphs, and the directed acyclic subgraphs are analyzed; node depth values and breadth values are calculated for topological sorting to generate an execution sequence; performing node classification on the execution sequence, organizing the execution sequence into a batch processing task group, calculating an optimal execution path through an adaptive path optimization algorithm, generating a distributed transaction control instruction, and allocating the task group to an execution thread pool for execution by using a consistent Hash algorithm; monitoring an execution process, dynamically adjusting thread pool resources according to a performance prediction value, executing cascade rollback when an exception occurs, and reconstructing a transaction execution path; according to the method, the execution efficiency and stability of the database cascade operation can be effectively improved, and the system resource consumption is reduced.

The embodiment of the invention provides a multi-source heterogeneous data integration method and device fusing a large model conversion operator. The method comprises the steps that heterogeneous data are collected from a multi-source heterogeneous data source, preprocessing operation of data cleaning is carried out, and preprocessed data features are obtained; inputting the preprocessed data features and the target format into a large language model, and generating a conversion operator including data structure analysis, field mapping and type adaptation; and based on the conversion operator, performing distributed parallel data conversion and integration in a distributed computing framework, and integrating and verifying the integrated data. According to the scheme, by introducing the intelligent reasoning ability of a large language model, the efficiency optimization of distributed calculation and the quality verification mechanism of the whole process, the core problems of the traditional data integration technology in the aspects of rule stiffness, high manual dependency and quality control deficiency are systematically solved.

The invention relates to the technical field of data processing, in particular to a multi-AI algorithm collaborative intelligent management system based on a large model, and the system comprises a data access module which collects original messages of terminal equipment of multiple manufacturers; the protocol adaptation module analyzes a device communication characteristic spectrum through a quantum entanglement separator, and outputs a standardized time-space event stream; a federation modeling module creates a parallel calculation instance, and a quantum tunneling mechanism is utilized to fuse the feature vectors to generate an equipment association topology model; the chaotic scheduling module generates an atomic task fractal network and assigns the atomic task fractal network to edge nodes; the interface optimization module calculates an interface field mapping relation through a quantum Boltzmann machine; and the closed-loop correction module triggers equipment communication characteristic spectrum re-calibration. Multi-source device communication differences are eliminated through quantum protocol analysis, federated quantum fusion breaks through algorithm collaboration barriers, chaotic fractal scheduling realizes resource dynamic optimization, a closed-loop collaboration system of device access to interface configuration is formed, and the problems of protocol incompatibility of multi-source heterogeneous devices and AI algorithm collaboration obstacles are solved.

The invention discloses a data import and field intelligent matching method for a low-code platform, and particularly relates to the technical field of low-code platform data processing. Constructing a field initial feature vector, extracting a standard field and historical matching features thereof from the target data model, and calculating a semantic matching score matrix by using a multi-source feature fusion model; screening the initial mapping table based on a dynamic threshold value, performing statistical deviation analysis in combination with a field value distribution difference measurement feature delta, and when delta exceeds a preset threshold value, calling a value mapping correction model to complete rematching to obtain an optimized mapping table; if it is detected that user history adjustment exists in the same source file type, re-correction is carried out based on a user behavior log, and a final field mapping table MP3 is output; finally, the MP3 is applied to data import, and automatic matching and binding are achieved. The method improves the accuracy and automation level of field matching, and is suitable for a multi-source heterogeneous data integration scene.

The invention discloses a multi-source heterogeneous data synchronization system, and relates to the field of computer information management application. The system fuses key technologies such as flow dynamic regulation and control, self-adaptive load balancing and intelligent semantic mapping. Through a real-time network state monitoring and self-adaptive flow regulation and control algorithm, the data transmission rate is dynamically adjusted according to the server resource utilization rate, the data priority and the network bandwidth, data backlog and system overload are avoided, and the robustness and the real-time performance of a synchronization task are remarkably improved. The intelligent field mapping engine is based on semantic analysis, word vector representation and a machine learning model, automatically recognizes field semantic consistency of heterogeneous data sources, generates an optimal mapping rule and dynamically optimizes a mapping result, the manual configuration cost is reduced, and the mapping accuracy rate reaches 98% or above. The system has high expandability and self-learning ability, is suitable for a large-scale multi-source heterogeneous data integration scene, and provides an efficient, intelligent and self-adaptive full-stack solution for a data synchronization task.

The invention belongs to the technical field of data quality management, and particularly discloses a power transmission and transformation equipment manufacturing full-process data checking method and system, a terminal and a medium, and the method comprises the steps: collecting original business data of each stage of power transmission and transformation equipment manufacturing, and carrying out the standardization, format normalization and preliminary abnormity elimination; uploading the processed data to a cloud processing module, and executing semantic fusion and field mapping processing; calling a corresponding checking rule set according to the manufacturing state, and executing field-level and record-level quality checking on the structured data; generating an abnormal identifier for the unqualified data, feeding back the abnormal identifier to a client interface, and storing the qualified data to the block chain module to realize credible evidence storage; and finally, generating a data quality checking report and performing structured visual display. According to the method, the problems that existing manufacturing data semantics are not uniform, rules cannot be dynamically adapted, abnormity is difficult to identify with high confidence, and data evidence storage is missing are solved.

The invention discloses a data warehouse intelligent construction method and device based on a large model. The invention provides a data warehouse intelligent construction method and device based on a large model, and belongs to the technical field of big data processing. The method comprises the following steps: a user registers to-be-collected business system data source information through a data source registration entry, and submits the information as a data processing task; receiving a data processing task through a task executor, calling a large model to carry out feature analysis on a data table, and identifying a table type, a service attribute and an acquisition parameter; executing data acquisition according to an analysis result, including identifying a temporary table, determining an acquisition period, selecting an increment field and the like; performing intelligent exploration on the collected data, and identifying field features and data quality; performing data conversion based on the probing result, wherein the data conversion comprises automatically binding a business rule, associating a data standard and generating a conversion rule; and finally, executing data fusion, intelligently matching the target table and the field mapping relation through a large model, generating a fusion rule, and executing fusion.

The invention discloses an intelligent data acquisition and conversion method based on a visual rule model, which comprises the following steps: S1, converting a configured rule chain into a data body in a JSON format by a visual rule modeling layer, analyzing the data body into an AST abstract syntax tree, and performing field mapping on the AST abstract syntax tree, the mapping fields are converted into executable codes through an intelligent conversion engine, and an execution plan is generated; s2, the intelligent acquisition layer pulls data from the heterogeneous data source, cleans the data and then allocates the data to an intelligent conversion engine for parallel processing; and S3, the intelligent conversion engine monitors the data consanguinity map and the field-level conversion path in real time, and sends a signal to the visual rule modeling layer or the intelligent acquisition layer according to the abnormal condition type so as to realize rule chain reconfiguration or data reacquisition. The method has the advantages that non-coding docking and real-time supervision of heterogeneous system data are realized, and the problems of dynamic adaptation, semantic understanding and data blood relationship interpretability in financial fund management are solved.

The invention relates to a BOM analysis method, system and device based on multi-modal semantic fusion and a medium, the method comprises the following steps: receiving BOM data, the BOM data comprising text input data and image input data; performing semantic feature extraction on the text input data by adopting a BERT model to generate a semantic embedding vector of the text; performing row and column structure identification on the image input data by adopting a Table Transform model so as to obtain a corresponding table image, and extracting table content information from the table image; based on the semantic embedding vector and the table content information, analyzing the relationship between the material and the process, and adopting a graph neural network to construct a knowledge graph of the material and the process; dynamically optimizing a mapping rule in the knowledge graph through a reinforcement learning algorithm, adjusting a corresponding field mapping rule, and generating an optimized knowledge graph; and based on the optimized knowledge graph, performing standardized conversion on the corresponding BOM data, and outputting BOM structured data. The BOM analysis method and device have the effect of improving the BOM analysis efficiency.

The invention discloses a method, system and equipment for monitoring the running state of a solid state disk and a storage medium, and belongs to the technical field of monitoring the running state of the solid state disk, and the method comprises the following steps: S1, monitoring core indexes during running of different solid state disks, including a health degree index, a performance index and an environment and hardware index; field mapping tables are established for different models of solid state disks, and SMART attributes of different manufacturers are uniformly converted into self-defined indexes; the main control temperature, the writing speed, the reading error rate and the environment temperature are combined, and the temperature values of different areas of the NAND are predicted; and the temperature, the voltage and the write-in magnification are combined to correct the residual life of the solid state disk. According to the method, the SMART attribute indexes are unified, the NAND temperature is accurately predicted, and the service life is predicted in combination with multiple factors, so that the operation and maintenance efficiency of the solid state disk can be effectively improved, and a safety guarantee is provided for stable operation of the solid state disk.

The invention discloses an enterprise data link management and value management method and system, and the method comprises the steps: carrying out the real-time capturing of multi-source heterogeneous data of all business systems in an enterprise through a distributed data collection engine, and recognizing the types of original data in different formats through a preset data source adapter; if structured data is detected, a relational database connector is adopted for extraction, and if the structured data is recognized as unstructured data, a document analysis module is started for content extraction, and an initial data set containing metadata tags is obtained; performing format conversion and field mapping on the initial data set according to a pre-established data standardization rule base, eliminating duplicate records and abnormal values through a data cleaning algorithm, performing automatic evaluation on a data quality grade by adopting a naive Bayes classifier, if a data quality score is lower than a preset threshold value, triggering a data recovery process, and if the data quality score is lower than the preset threshold value, performing data recovery. And standardized data meeting a unified standard is obtained. The normativity and value utilization efficiency of data management are effectively improved.

The invention provides an intelligent laboratory full-process collaborative management system and method based on multi-dimensional data fusion, and the method comprises the steps: obtaining an original data stream from laboratory instrument equipment, carrying out the format recognition and conversion of heterogeneous instrument data through a standard protocol adapter, and generating a first data set in a unified format; aiming at the first data set, executing automatic analysis by adopting data middleware, and integrating into a structured second data set based on a metadata rule of field mapping and unit conversion; according to the second data set, a multi-source data fusion model is constructed, data are classified according to instrument types through batch processing and aggregated according to timestamps, and a fused third data set is generated; aiming at the initial process configuration, adjusting a node sequence and a parameter threshold value by using a visual design tool, and generating an optimized process configuration adaptive to the diversified scene; according to the optimized process configuration, process scheduling is executed through a dynamic process engine, execution time and resource occupation are monitored in real time, and a process execution log is generated.

The invention discloses an unmanned aerial vehicle outdoor inspection method based on multi-modal fusion, and the method comprises the following steps: obtaining the inspection data of an unmanned aerial vehicle, and carrying out the feature extraction and space-time alignment; calling a GNPDE algorithm, and constructing a multi-modal field mapping layer; constructing a structure-thermal field joint graph structure, and establishing a double-branch coupling solution structure; constructing a topological adaptive edge weight adjustment module, and dynamically modulating the edge weight by adopting a physical modulation function; introducing an energy conservation constraint layer, and executing constraint solution; a multi-scale PDE evolution algorithm subset is quoted and combined, and a scale weight sharing mechanism is adopted to complete cross-scale joint optimization; constructing an abnormal residual reasoning module, and generating an abnormal significance map; and performing spatial registration and superposition on the abnormal saliency map and unmanned aerial vehicle inspection data, and outputting an equipment-level inspection report and a risk level conclusion. According to the invention, the inspection abnormity identification precision and the structure-thermal field reasoning stability are improved.

The invention provides a data analysis mapping method and system based on context awareness, and the method comprises the steps: carrying out the directional crawling of a target website through a crawler instance, so as to obtain multi-format webpage source data, then extracting the structural features, semantic features and source features of the multi-format webpage source data, and constructing a source data context feature set; dynamically matching an adaptive analysis strategy based on the source data context feature set to accurately extract target field data, performing semantic similarity matching on the target field data and a preset service scene standard field library, and performing data format unified conversion in combination with a context format rule to obtain standardized field data; and finally, performing multi-dimensional verification on the standardized field data, and generating an SQL statement after the verification is passed, so as to solve the problems that an existing data analysis mapping scheme depends on a fixed template and cannot dynamically adapt to page structure change of a target website, manual auxiliary field mapping consumes time, and errors are easy to introduce.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing and analysis method, device, equipment and medium, and the method comprises the steps: obtaining a data source set, and registering a source label and field mapping relation; executing extraction, cleaning, conversion and loading in the data processing assembly line based on the data source set, the source label and the field mapping relation to generate a processing table set; performing quality verification and exception handling on the processing table set to generate a verified processing table set; generating an index cloud based on the verified processing table set and the configuration strategy; the index cloud is pushed to the analysis tool set through the interface transmission channel, and the analysis tool set generates analysis output according to the index cloud. According to the method, through unified data registration, standardized processing and quality verification, the index cloud is generated in combination with the configuration strategy and pushed to the analysis tool set, the analysis result is generated and transmitted, the data consistency is ensured, and the real-time performance is improved.

The present invention provides a method, equipment and electronic device for parsing and processing structured data based on configuration. By reading a predefined configuration file, configuration information is obtained and parsed and loaded; the configuration information is configured data processing information; the data processing information includes field mapping relationships and conversion rules; receiving and reading a data source, and parsing the data source according to the field mapping relationship; according to the conversion rules, converting the parsed data to obtain converted target format data; outputting the target format data. Compared with the prior art, by reading the configuration file, the structure and field information of the data source can be automatically identified, and data can be extracted, converted and processed according to the configuration rules, which can adapt to various complex data sources and data formats, improve the flexibility and scalability of data parsing, and improve development efficiency.

The invention relates to the technical field of natural language processing, in particular to an archive structured information extraction method and system based on a multi-modal large model and a medium, and the method comprises the following steps: S1, establishing a mapping table of fields to be extracted; s2, data annotation; s3, constructing a layout analysis model, an archive structured extraction model and an archive structured integration model; s4, screening key information pages based on the layout analysis model; s5, extracting single-page structured information based on an archive structured extraction model; and S6, based on the archive structured integration model, integrating single-page structured information results. Through application of the multi-modal large model, accurate layout analysis, strong structured extraction capability and efficient information integration are realized, automatic extraction and integration from archive image data to structured information are realized, manual intervention is reduced, and processing efficiency is improved.

The invention relates to the technical field of computer programming, and discloses a Go language structural body automatic conversion method and system. The method comprises the following steps: firstly, receiving definitions of a source structure body and a target structure body as input, automatically matching fields of the two structure bodies through a field mapping rule engine according to a preset rule, and generating a field mapping relationship; then, based on the field mapping relation, a code generator is used for automatically generating a conversion code containing field assignment and type conversion logic; finally, the generated conversion codes are compiled into an executable conversion function, and the function can convert data in the source structure instance according to the field mapping relation during program running and fill the data into the target structure instance. According to the method, automation of structural body conversion is achieved, the problem that errors are prone to occurring when conversion codes are written manually is solved, and the maintainability of the codes is improved; the mode of reflection during operation is replaced by code generation, so that the conversion efficiency is remarkably improved; meanwhile, the method supports batch conversion and concurrent processing, and can meet the performance requirements of large-scale data processing.

The invention provides a heterogeneous system data sharing method and device based on a self-built gateway and a medium, and the method comprises the steps: receiving interface requests of a plurality of service parties through a flow gateway, and carrying out the authentication, routing forwarding and flow control of the requests; in the self-built gateway, field mapping, cutting and customized processing are carried out on request parameters according to target interface requirements, the field mapping comprises field name conversion, data type conversion and logical operation, the cutting comprises screening, redundant field deletion and structure reorganization, and the customized processing comprises condition judgment, calculation and format adjustment; and then, sending the processed request to a target service interface, receiving response data, further performing reverse field mapping and field cutting on the response data through the self-established gateway, and returning the response data to a service party. The method supports automatic generation of the field mapping rule, adapts to different interface requirements, and guarantees data security and interface performance through data encryption and decryption and flow control.

The invention relates to the technical field of data import, in particular to an intelligent Excel data import method and system based on dynamic mapping, and the method comprises the steps that the system traverses all Excel fields for each database field, calculates the feature similarity and field matching degree of the Excel fields, and generates a field mapping template set according to all field matching results; reading the Excel content according to rows, cleaning each row of content into a standard value, performing quality scoring, and outputting a target format data structure set; screening a structure alignment data row and a field structure exception record set which conform to a threshold value; the system reads the structure alignment data rows line by line, processes the extraction value of each field and outputs a legal data set; and extracting a field sequence set, searching whether fields marked as primary keys or unique keys exist in the field sequence set, and combining the fields into a uniqueness judgment set to judge whether the currently written Excel data exists in the target database table.

The invention discloses an enterprise data asset entry system based on artificial intelligence, and the system comprises an original document receiving module which is used for generating an original document set; the multi-modal data construction module is used for constructing a multi-modal input data set; the artificial intelligence identification module is used for identifying a field set with data asset attributes; the structured asset modeling module is used for converting the identification field set into a structured data asset object; the rule matching and template selection module is used for generating a field mapping rule and table entry trigger configuration; the field standardization mapping module is used for generating a structured data entry record set; the table entry metadata supplementing module is used for adding a template number, a mapping timestamp and an operator identifier for each data table entry record; the ledger writing and auditing module is used for writing the data entry record into the enterprise asset ledger system; and the table entering state management module is used for generating a state mapping table. According to the invention, automatic data asset entry is realized.

The invention relates to a gateway-based multi-protocol environmental data fusion acquisition method, which comprises the following steps of: acquiring a source protocol and a target protocol of an environmental sensor gateway, and constructing a field mapping relation library between the source protocol and the target protocol; according to the target protocol, identifying pollution data which does not meet the data quality requirement of the target protocol; obtaining a source protocol type identifier and a protocol version number of the polluted data; determining key nodes of the pollution data in the protocol conversion process by analyzing a protocol conversion log of each piece of pollution data in the pollution data isolation area; optimizing a numerical length limiting condition and a data format conversion logic in the protocol conversion function; extracting features of the pollution data according to a protocol exception processing mode; and redeploying the optimized protocol into the environment sensor gateway. According to the method, the data pollution problem in the multi-protocol environment can be effectively identified and processed, the data quality is improved, and reliable data support is provided for environment monitoring.

The invention discloses a business filling scene data acquisition method and system, a storage medium and electronic equipment, and the method comprises the steps: firstly storing filling historical data and domain knowledge into a vector database, and according to the current input of a user, searching related information, and combining the current input to generate a filling suggestion through a generation type large model; multi-modal information of the multi-end filling system is obtained, semantic understanding and information extraction are completed through a large model, and the multi-modal information is converted into structured data; mapping the structured data to a specified field by means of a field mapping rule; a data verification rule is designed, data verification is corrected through a rule engine or large model dynamic logic verification, and a verification result and a prompt suggestion are output; and finally, storing the processed and verified data, and providing data support for the user. According to the invention, the data acquisition efficiency is improved; the intention of the user can be predicted, corresponding suggestions can be provided, and the legality and accuracy of data are ensured.

The invention provides a business modeling method, medium and system based on an enterprise data asset management platform, and belongs to the technical field of modeling methods.The business modeling method based on the enterprise data asset management platform comprises the following steps that an enterprise data asset catalog is read to generate a data asset list, the method comprises the steps of extracting a foreign key association relationship between data tables to construct an initial entity relationship table, calculating reference frequencies between the data tables to generate an entity association degree matrix, dividing a service domain by adopting a K-means clustering algorithm, counting data table fields in the service domain to generate a data table field mapping table, recording a data table operation sequence to construct a data table operation link diagram, and establishing a data table operation link diagram. The method comprises the following steps: generating a data table node evaluation matrix, dividing sub-domains and determining a core entity, and constructing and training a multilayer neural network model for constructing business affiliation, data table incidence relation and business interface definition of a new data table, so as to solve the defects in the aspects of data asset relation discovery, business modeling automation, service interface generation and the like in the prior art.

The present disclosure provides various devices, systems, and methods for iterative shimming in magnetic resonance imaging (MRI). In one aspect, a method of iterative shimming can include generating a field map of a magnetic field B0 from an MRI system. The MRI system can include a shim tray and an array of permanent magnets to generate the magnetic field B0. The shim tray can include slots to receive shim magnets. The method can further include shimming the MRI system by iteratively: applying the field map to a genetic algorithm to determine a set of slots to receive a set of shim magnets, installing the set of shim magnets in the set of slots, generating a next field map of the magnetic field B0, and either performing a next iteration based on the next field map or determining to not perform the next iteration based on the next field map.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health, voice navigation and the like, and discloses a multi-source data processing and form generation method, device, equipment and medium. Generating a preprocessing data set; creating a field mapping relationship through a configuration interface, associating fields of different business attribute types in the preprocessed data set to a target subject, and executing subject clustering processing on the associated preprocessed data set according to the target subject to generate intermediate data; and generating a standard form based on the intermediate data. By automatically collecting the original data, analyzing the fields, establishing the field mapping relation and generating the standard form, automatic processing of the multi-source data is achieved, the workload of manual data processing and verification is reduced, the data processing efficiency and accuracy are remarkably improved, and meanwhile the traceability and consistency of the data are ensured.

The invention provides a paper file information input method, system and device and a medium, and belongs to the technical field of file information processing. The method comprises the steps of collecting a paper file image, performing preprocessing in sequence to obtain a preprocessed image, and performing region segmentation on the preprocessed image by using a target detection algorithm to generate a region coordinate mapping table; generating a file template by using a predefined file template library according to the region coordinate mapping table; performing printing form recognition and handwritten form recognition on characters in the image by using a sequence generation model to generate a text sequence, and recognizing a table structure by using a two-dimensional convolutional network; according to the text sequence and the table structure data, a named entity recognition model is used for labeling field types, data formats are checked and corrected according to template rules, errors are corrected in combination with context semantic similarity, and structured data are generated; and mapping the structured data to an information system database table according to a template field mapping rule, and inputting the structured data into an information system.