2778 results about "Automatic processing" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a multi-modal event data processing and collaborative circulation method, device, equipment and medium. Generating a stereoscopic scene model based on the event data file and positioning a target area, performing multi-modal fusion by combining a positioning result and non-spatial data of the event data file to generate a fusion analysis result and form a processing report, and constructing an auditing and judging item set containing a conditional strategy and an execution action to execute auditing and generate an auditing and judging result. And generating a collaborative circulation instruction set based on the processing report and the audit judgment result, and triggering execution through distributed message transmission to obtain an execution result. According to the method, through multi-modal data processing, three-dimensional scene modeling, cross-modal fusion analysis, rule-driven auditing and distributed execution, full-process automatic processing is realized, and the processing accuracy and efficiency are improved.

The invention discloses a government affair intelligent interaction and information extraction method and device, equipment and a medium, and relates to the technical field of digital government affair, digital cities, intelligent interaction and the like. The traditional manual item-by-item filling is converted into end-to-end automatic processing, so that the operation steps of a user are remarkably reduced, the filling burden of a hundred-item-level form is solved, and the filling efficiency is improved; and an ID voice corrector is adopted to realize three-layer progressive verification, so that the output ID offset of the multi-modal large model is effectively inhibited, the accurate mapping of field identifiers is ensured, and meanwhile, a lightweight knowledge graph is adopted to automatically capture logic rules among fields, interspecific compliance conflicts of government affair scenes are intercepted in real time, the logic error risk is reduced, and the filling accuracy is improved.

The invention relates to the technical field of surveying and mapping information processing, and discloses a surveying and mapping information intelligent analysis and visualization system based on the Internet of Things. According to the system, a building space data acquisition terminal acquires geometric feature point cloud and environmental parameters of the surface of a building structure in real time through distributed Internet of Things nodes; the multi-source heterogeneous data fusion engine performs space-time alignment processing on the geometric feature point cloud to generate a building total-factor topological skeleton with an absolute coordinate system; the three-dimensional topological relation reconstruction module is used for extracting a spatial constraint relation of building components according to the topological skeleton and establishing a hierarchical building information model capable of being edited in a parameterized manner; and the dynamic visual rendering platform is used for generating a multi-scale spatial index based on the hierarchical building information model, and driving real-time light and shadow effects and interactive sectioning analysis. According to the system, full-process automatic processing and dynamic visualization of building surveying and mapping data are realized.

The invention discloses a multi-modal data automatic processing and information extraction method, and belongs to the technical field of data processing. Comprising the steps of establishing an original knowledge base according to original training data in a business scene; preprocessing the original multi-modal data in the original knowledge base to obtain a preprocessed knowledge base; inputting the preprocessing knowledge base into a knowledge retrieval unit; a text to be queried is converted into a query vector, the distance between the query vector and a knowledge fusion vector representation vector in the knowledge retrieval unit is calculated, and a retrieval result is obtained through an approximate nearest neighbor retrieval algorithm; and performing multi-modal data fusion on a retrieval result through a cross-modal Transform model, and combining fused semantics with user query to generate an answer. According to the method, data of multiple modes such as texts, images, audios and videos can be processed, multi-mode fusion and reasoning are carried out through the visual language model, accurate extraction and structured storage of information are achieved, and the efficiency and quality of data analysis and mining are improved.

The invention discloses a hydraulic engineering dam body crack detection method and system based on machine vision, and relates to the technical field of computer vision. A high-definition camera is used for shooting a dam body image, obtaining a sample data set, extracting feature parameters of different noise types, and constructing a feature database; filtering the current dam body image to obtain a first dam body image, enhancing the crack gray scale difference of the first dam body image, obtaining a second dam body image, segmenting the second dam body image, screening candidate cracks in the crack image, and identifying real cracks according to the edge features of the candidate cracks. According to the method, a feature database is constructed, a targeted dam body image processing flow is combined, a high-definition camera collection and automatic processing flow is used, filtering parameters are dynamically adjusted by constructing the feature database, gray level enhancement and precise segmentation are combined, real cracks are precisely recognized through edge feature analysis and comparison, and quantitative parameters are output; and a reliable basis is provided for dam body safety assessment.

The invention relates to a large model business logic processing method and system based on workflow engine and domain knowledge fusion, and is suitable for automatic processing of complex multi-node and multi-branch business processes. According to the method, natural language input of a user is analyzed through a large language model, a service intention is recognized, and the service intention is converted into an executable task process through a workflow engine. And the workflow engine dynamically adjusts an execution path according to rules and data in the domain knowledge graph to realize efficient parallel processing of tasks. The system integrates a workflow engine, a domain knowledge graph and a large language model, supports real-time data processing, rule matching, conflict resolution and decision optimization, is suitable for the fields of water conservancy, medical treatment, finance and the like, automatically generates and executes a complex business process, and optimizes a decision process. The system improves the accuracy and execution efficiency of business decision through deep fusion of domain knowledge.

The invention relates to the technical field of equipment abnormity control, and discloses an equipment abnormity self-closed-loop control system. An anomaly detection engine of the system collects operation state data of target equipment in real time, and performs preliminary anomaly marking according to a preset anomaly judgment rule; the dynamic analysis unit receives the preliminary anomaly marks, generates anomaly feature vectors in combination with historical operation data, and inputs the anomaly feature vectors into a multi-dimensional evaluation model to divide anomaly levels; a strategy generator is matched with a preset repair strategy library according to the abnormal grade division result, and a corresponding repair instruction sequence is generated; the execution control module analyzes the repair instruction sequence, and dispatches hardware resources to execute repair operation according to a preset execution priority; and the feedback verification unit collects the running state data again after restoration, and compares the running state data with the initial abnormal mark to generate a restoration verification result. The system realizes full-process automatic processing of equipment exceptions, and improves exception processing efficiency and equipment operation stability.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

The invention relates to the technical field of human resource management, and provides a one-stop talent screening method and system. The method comprises the steps of performing text analysis on a job seeker resume, identifying structured information in the job seeker resume, performing weight distribution on the analyzed information through a preset post competency model, and generating a resume analysis result containing a candidate and target post matching degree score; consultation information about enterprise information, position treatment and office environment of the job seeker is obtained, the consultation information is analyzed to obtain a question intention of the user, and response content corresponding to the question intention is generated; performing semantic analysis on the communication record corresponding to the job seeker, and identifying the job hunting willingness level of the job seeker; and dynamically generating personalized interview questions through an interview question generation model in combination with the matching degree score and the job hunting willingness level to complete talent screening. The method is suitable for full-process automatic processing of resume screening, intelligent communication and adaptive interview in the enterprise recruitment process.

The invention relates to the technical field of data integration, in particular to a multi-source data integration system. According to the technical scheme, the system comprises a plurality of heterogeneous data acquisition devices, a data processing module, a data storage module and a data transmission module. Differentiation processing is carried out on data of different devices, accurate time synchronization and data alignment are achieved through an NTP protocol and dynamic calibration, intelligent noise processing and data cleaning are carried out through multiple algorithms, compression encryption is adopted in storage, and a double-queue priority and breakpoint resume mechanism is applied in transmission. And fault monitoring and automatic processing are realized by means of hierarchical log recording and intelligent analysis, so that the problems of data isomerism, time synchronization, noise interference, storage and transmission efficiency, system reliability and the like are efficiently solved, and efficient, stable and safe data support is provided for a real-time monitoring platform.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention relates to the technical field of building structure health monitoring, in particular to an unmanned aerial vehicle laser radar-based building point cloud deformation quantitative extraction method, which comprises the following steps of: based on building corner structure characteristics in point cloud data, carrying out quantitative evaluation on the overall inclination state of a building through point cloud segmentation, straight line fitting and angle calculation; based on an ideal plane hypothesis, performing plane fitting and point-to-plane distance analysis on the building facade point cloud in the point cloud data, identifying a local concave-convex deformation area of the building facade, and performing quantitative evaluation; and roof plane detection and wall surface normal vector analysis are carried out based on the point cloud data, and accurate quantification is carried out on parapet wall inclination based on region growth segmentation and PCA principal direction analysis. Through automatic processing and quantitative analysis of an algorithm, subjective errors and inconsistency possibly occurring in a traditional manual evaluation method are avoided, and objectivity and repeatability of an evaluation result are enhanced.

The invention relates to the field of finance, and discloses an AI-based investment project feasibility intelligent analysis and decision support system, which comprises a multi-modal data fusion engine used for extracting a feature vector of an unstructured text through a BERT variant model, carrying out joint coding on the feature vector and a structured data feature, and outputting fused multi-modal data; the dynamic knowledge graph construction module is connected with the multi-modal data fusion engine; the deep reinforcement learning decision module comprises a feasibility prediction network and a risk assessment network which are cooperatively trained; an interpretable AI module; and a closed loop feedback module. By means of a multi-modal data fusion engine and a deep reinforcement learning decision module, automatic processing and intelligent analysis of data are achieved, and the analysis time of a single project is greatly shortened to 8 minutes. The high-efficiency processing speed enables the investment institution to quickly respond to the market change, timely grasp the investment opportunity, and occupy the decision-making precedence in the financial market which changes instantaneously.

The invention relates to the technical field of intelligent aquaculture and automatic processing, in particular to an appearance characteristic-based hippocampus intelligent grading and pond-dividing culture method and system, and the method comprises the following steps: S1, collecting sparse visual data and multi-source physiological data of hippocampus; s2, generating a three-dimensional digital twinborn model based on the sparse visual data; calculating a growth state grading index based on the three-dimensional digital twinborn model; s3, calculating a physiological stress index based on the multi-source physiological data; s4, comparing the physiological stress index calculated in real time with a preset safety stress threshold value and a critical stress threshold value, and generating a hierarchical adaptive correction strategy; s5, according to a grading self-adaptive correction strategy, controlling a flexible mechanical arm to execute fishing and pond separation operation; according to the method, the appearance and physiological data of the hippocampus are synchronously collected in a non-contact mode, the technical problems that the real condition of the hippocampus cannot be evaluated and damage is possibly caused due to target stress response caused by traditional physical intervention are solved, and the authenticity and effectiveness of original data are guaranteed.

The invention provides a business rule automatic processing method and system based on natural language understanding. The method comprises the following steps: extracting text content from an original document containing business rules and preprocessing the text content; utilizing a BERT-based metadata extraction model to extract structured metadata from the preprocessed text content; confirming and perfecting the structured metadata; on the basis of the confirmed metadata, a rule execution flow chart and / or a test case are / is automatically generated and used for verifying the correctness of rule logic; and according to the target format, converting the verified rule into a corresponding symbolic representation through a template engine to obtain a defined rule. According to the invention, the automatic conversion of the business rule from the natural language description to the symbolized executable form is realized, so that business personnel can directly use the natural language to define and maintain the business rule, the manual conversion cost is reduced, and the accuracy and efficiency of rule processing are improved.

The invention discloses an integrated machining device suitable for a large marine propeller, which comprises a machining adjusting assembly, the machining adjusting assembly is composed of an X-axis moving mechanism, a Y-axis moving mechanism and a Z-axis moving mechanism, one side of the Z-axis moving mechanism is fixedly provided with a rotating mechanism, one end of the rotating mechanism is fixedly connected with a cutting assembly, and the other end of the rotating mechanism is fixedly connected with a cutter; according to the large marine propeller integrated machining device with the automatic machining function and the self-adaptive clamping function, through the integrated multi-axis linkage machining adjusting assembly and the three-dimensional scanner, automatic path planning can be conducted, and automatic operation is achieved; therefore, the dependence on advanced technicians is greatly reduced, the machining efficiency and the product consistency are remarkably improved, and meanwhile, through the collaborative design of the rotatable three-jaw chuck and the center supporting mechanism, simultaneous reliable supporting from the interior and the exterior of the workpiece can be automatically achieved during clamping.

The invention discloses a method for dynamically monitoring and automatically processing abnormity of an entrance and an exit of a parking lot, relates to the technical field of intelligent parking lot management, and aims to solve the problems that existing unattended parking lot entrance and exit management depends on manpower, abnormity processing is passive, and the misjudgment rate is high. The method is based on an information acquisition and execution layer-edge processing layer-cloud platform three-layer architecture, the core realizes high-precision scene understanding through a multi-algorithm fused visual perception module, multi-modal data weighted fusion is combined with a ground induction coil, a radar and the like, and the misjudgment rate is reduced; a sensing result is converted into an automatic processing action through a configurable intelligent rule engine; and meanwhile, the cloud platform stores passing data and an evidence chain, optimizes an AI model through incremental training and returns the AI model to an edge layer, so that closed-loop evolution is realized.

The invention discloses a fault co-seismic sliding surface inversion method and system based on Beidou and artificial intelligence. The method comprises the steps of collecting Beidou data and seismic waveform data of a target area in real time, and performing preprocessing to extract co-seismic displacement and waveform features; fusing the Beidou deformation features and the seismic wave features to form a multi-modal feature vector; a fault prediction model is constructed based on a graph neural network, forward modeling and historical earthquake example data training are utilized, and multi-modal features are input to obtain preliminary fault sliding distribution prediction; rapid optimization under physical constraint is carried out through the elastic dislocation model, and a final fault sliding model conforming to the geophysical law is obtained; finally, uncertainty quantification is carried out, and a visual product of the dynamic process including sliding distribution, seismic moments and fault deformation is generated. According to the system, full-process automatic processing is achieved through cooperation of all the units, the inversion speed, precision and physical credibility are improved, and reliable support is provided for earthquake emergency response and disaster assessment.

The invention relates to a constraint condition and multi-objective optimization fused investment attraction target enterprise screening method. The method comprises the steps of obtaining target industry field information and generating an initial target enterprise pool; obtaining regional resource constraint information and enterprise multi-dimensional information, and inputting the information into a constraint discrimination model to obtain a candidate enterprise set; inputting enterprise multi-dimensional information associated with the candidate enterprise set into the tendency evaluation model to obtain an enterprise comprehensive score set; obtaining optimization target information, and constructing a multi-target optimization function; obtaining optimization condition information, matching a corresponding multi-objective optimization algorithm and adjusting parameters; solving the multi-objective optimization function, generating an enterprise screening strategy scheme set, and generating an enterprise screening decision report; in conclusion, by fusing the regional resource constraint condition and the multi-target optimization mechanism, the automatic processing and objective evaluation of the enterprise screening process are realized, and the method has the advantage of improving the efficiency and objectivity of screening the investment attracting target enterprises.

The invention relates to the technical field of semantic understanding, in particular to an environmental impact report auxiliary auditing system based on semantic understanding, which comprises a text cleaning module, a chapter segmentation module, a rule generation module, a content auditing module and a result output module. According to the method, the paragraph structure and the layout identification are analyzed in a unified mode, key paragraph types in environmental influence chapters are efficiently recognized, the text integration accuracy is improved by combining inter-paragraph similarity calculation and repetition rate screening, chapter boundary accurate positioning and affiliation adjustment are achieved on the basis of format feature comparison of serial numbers and title styles, and the text integration efficiency is improved. A multi-layer parameter alignment rule set is constructed to support comprehensive verification of standard numbers, monitoring frequencies and periodic elements, an exception labeling mechanism is combined to complete difference item logic judgment and consistency evaluation, an auditing basis with rule driving and data comparison capabilities is formed, chapter positioning precision, element extraction comprehensiveness and logic exception recognition efficiency are improved, and the method is suitable for popularization and application. And the pertinence and the automatic processing depth in the auditing process are enhanced.

The invention discloses a power grid project-oriented information matching review system based on a large language model, and belongs to the technical field of intelligent review of electric power engineering. Analyzing the uploaded file through a project structure analysis module, loading a rule template, and generating a first review plan (basic information review) and a second review plan (special information review) which comprise required files; the text preprocessing module performs format conversion and unit standardization processing on the file and updates the file to an examination plan; the basic judgment module generates conclusions of file content matching, timeliness and engineering information matching according to the rule template; a special item extraction module generates a special item information matching conclusion; and the report generation module combines the two conclusions to form a preliminary result, and finally outputs a readable information matching examination report after expert reexamination and correction. The whole system realizes full-chain automatic processing of power grid project file intelligent analysis, rule and semantic dual-drive review, cross-file consistency verification and man-machine collaborative optimization.

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 simulation test key factor screening-oriented method and system and a computer program product. The method comprises the following steps: generating a structured configuration file; constructing a supervised learning data set based on the structured configuration file; based on the supervised learning data set, training a machine learning classification model to obtain a current key factor set; and repeating the steps, judging whether the current key factor set is converged or not, and if the current key factor set is converged, stopping simulation and outputting the current key factor set of the last round as a final key factor. According to the technical scheme of the invention, a high-dimensional, nonlinear and high-coupling complex system can be automatically processed; key factors are objectively recognized through machine learning, a small-batch iteration verification mechanism is adopted, and the screening accuracy and efficiency are improved while the number of times of simulation is remarkably reduced.

The invention discloses a government affair work order clustering method and system based on a large language model and multi-scale embedding, and the method comprises the steps: S1, extracting a work order from a work order database, and generating a work order abstract and work order key features based on the work order and the large language model; s2, generating corresponding semantic representations based on the work order abstract and the work order key features by using a semantic embedding model, splicing the semantic representations of the work orders to obtain a work order representation vector, and writing the work order representation vector into a work order vector database; s3, using a clustering algorithm to cluster vectors of the work single vector database to generate a plurality of event clusters; and S4, generating a matched work order of the extracted work order by using a similarity algorithm based on the step event cluster. According to the method, automatic information extraction, similar work order retrieval and high-precision clustering of the government affair work orders can be realized, the intelligent and automatic processing efficiency and the standardization level of the government affair work orders are remarkably improved, and a solid support is provided for efficient operation of government affair services.

The invention relates to the technical field of graphene, and more particularly discloses an automatic processing system and method for a graphene cooling fin, and the method comprises the steps: obtaining the specification and shape parameters of the cooling fin, a monitoring video, a slicing pressure value and other information, extracting a slicing process change feature vector, a cooling fin parameter correlation feature vector and a pressure feature vector, and obtaining a graphene cooling fin; the feature vectors are classified through a classifier, and whether the pressure value at the current time point needs to be increased or decreased is judged. In this way, automatic machining of the graphene cooling fins is achieved, and the machining applicability and quality are improved.

The invention relates to the field of mines, in particular to a mine ventilation safety intelligent monitoring method. The method comprises the following steps: firstly, constructing a graph structure based on acquired mine information, optimizing spatial distribution of monitoring points in the graph structure through a monitoring point coverage efficiency function, and outputting an optimal monitoring point set; arranging sensors based on the optimal monitoring point position set; then, ventilation parameters are collected based on a sensor, and a state feature matrix is generated; constructing an adjacent matrix based on the state characteristic matrix; based on the state characteristic matrix and the adjacent matrix, calculating the abnormal probability of the monitoring point position; based on the abnormal probability, obtaining an early warning level; based on the abnormal probability, calculating three-dimensional coordinates of the hidden danger position through a gradient-guided air flow propagation positioning algorithm; and finally, based on the early warning level and the three-dimensional coordinate of the hidden danger position, executing a hierarchical automatic processing and response mechanism. The technical problems of incomplete monitoring coverage, poor data acquisition real-time performance, insufficient abnormal recognition capability and untimely early warning response are solved.

The invention discloses an online order full-process automatic processing system and method. The system comprises an order state analysis module, a cross-platform collaborative adaptation module, a resource scheduling planning module, an environment load evaluation module and a task scheduling optimization module. According to the invention, by analyzing the trend of the key time sequence change of the order life cycle, the circulation bottleneck can be identified and the time section exceeding the threshold value can be generated, so that the task sequence is matched with the system load, the scheduling coordination and the response precision are enhanced, the execution capability is dynamically compared to form the optimal distribution instruction, and the cooperation efficiency and the transmission continuity are improved. Based on a utilization rate change identification trend, resource distribution is balanced, scheduling flexibility and controllability are enhanced, resource and energy consumption cooperative adjustment is realized through environment load evaluation, operation stability and environment adaptability are improved, high efficiency and continuous optimization of an order flow are realized, and processing capability and operation toughness of a system under high concurrency are enhanced.

The invention relates to a land site selection recommendation method and system fusing constraint conditions and an optimization algorithm, and the method comprises the steps: obtaining a constraint feature set, dividing the constraint feature set into strong constraint features and weak constraint features, and constructing a site selection knowledge graph; obtaining project demand information, and screening a strong constraint set and a weak constraint set based on the site selection knowledge graph; obtaining a compliance land use information set based on the strong constraint set, extracting fuzzy terms in the weak constraint set, and generating auxiliary decision suggestions based on the site selection knowledge graph; solving the compliance land use information set and the weak constraint set, generating a site selection recommendation solution set, and outputting the site selection recommendation solution set to the user side; according to the method, the planning provisions and the spatial data are automatically analyzed through the multi-channel feature extraction model, the site selection knowledge graph is constructed to achieve intelligent classification of constraint conditions, the recommendation solution set is generated in combination with the multi-target optimization algorithm, and the method has the advantages that the automatic processing degree of the complex planning provisions is improved, and multi-target optimization decision making is achieved.

The invention relates to the technical field of electric digital data processing, and discloses a multi-source data automatic processing and service integration system based on artificial intelligence driving. Comprising the steps that a multi-source data access module achieves concurrent data access and caching, an AI driving data preprocessing module completes field automatic mapping through a data isomerism adaptation algorithm, and an intelligent data fusion module calculates a multi-source data fusion weight through a dynamic weight distribution algorithm; the service integration module realizes standardization of fusion data, and the monitoring scheduling module monitors each module in real time. High-concurrency multi-protocol data access is achieved through multi-source data automatic processing and service integration and a distributed Kafka cluster, automatic data mapping and precise data cleaning are completed through an improved algorithm depending on an AI driving module, the data fusion precision is improved in combination with a dynamic weight algorithm, the service integration flexibility is enhanced through multi-protocol API packaging and load balancing, and the service integration efficiency is improved. And stable system operation and maintenance are guaranteed.

The invention discloses a heterogeneous document structured data extraction system and method based on multi-modal fusion, and the method comprises the following steps: S1, receiving a heterogeneous document, and carrying out the preprocessing; s2, extracting visual and semantic modal information based on the visual backbone network and an OCR module, and fusing spatial coordinates, text content and layout features; s3, receiving a dynamic target mode Schema, executing field-level semantic matching, and calculating semantic similarity; s4, performing consistency verification, verifying and filling the field values, and correcting the associated fields; s5, converting the format into a data file in a specified format, and reserving fields to be mapped with an original document semantic block; s6, automatically switching a cloud mode and a local mode according to a deployment environment; and S7, outputting the data file of the target field. According to the method, high-precision and low-delay structured data extraction of the heterogeneous format document can be realized, and the automatic processing efficiency and the data credibility are improved.