1213 results about "Matching methods" patented technology

The invention belongs to the technical field of artificial intelligence and big data, and discloses a customer data processing and insight system based on a big language model. The system is composed of a multi-source data access module, a data preprocessing and label fusion module, a large language model semantic understanding module, a knowledge enhancement and semantic linkage module, an insight generation and visualization module, an intelligent strategy output module and a feedback learning and self-optimization module. According to the method, multi-source heterogeneous data such as texts, voices and structured behaviors are integrated, and the deep semantic analysis capability of a large language model is combined, so that global modeling of customer behaviors and intentions is realized; a multi-modal synchronous acquisition and standardization mechanism eliminates data format barriers, and a dynamic label mechanism adapts to context changes, so that the system can capture deep semantic association in customer expression, and compared with a traditional keyword matching method, the semantic understanding accuracy is improved by more than 40%, and a more complete data base is provided for insight generation.

The invention discloses a person and post matching method, system and equipment based on Transform and a medium, belongs to the technical field of cross application of natural language processing and human resource management, and aims to solve the technical problem of how to reduce subjective errors in a person and post matching process, improve matching accuracy, fairness and objectivity and improve manual matching efficiency at the same time. According to the technical scheme, the method comprises the following steps: data collection: collecting post information data and job seeker resume data from a plurality of data sources, and cleaning and sorting the collected data to obtain a post description text and a resume text; text preprocessing: preprocessing the acquired and sorted post description text and resume text to obtain the preprocessed post description text and resume text; the method comprises the following steps of: fusing a Transform model and a BERT model; constructing an encoder-decoder structure based on the Transform model; optimizing the model; multi-label text classification: constructing a multi-label classification system; and outputting and evaluating a matching result.

The invention discloses a talent matching method and system based on artificial intelligence, and aims to improve human resource configuration efficiency and decision intelligence. The method comprises the following steps: collecting talent and demand data in multiple channels, especially unstructured communication data including interview records and work communication records; processing data through technologies such as multi-mode resume analysis, extracting features and fusing the features; an enterprise talent knowledge base which is used for continuous learning and dynamic maintenance based on a system operation result and multi-source feedback and integrates structured and unstructured data is constructed; based on the natural language query of the user, providing intelligent question answering and decision support by using a retrieval enhancement generation model connected with the specific enterprise talent knowledge base; an advanced deep learning algorithm is adopted, a knowledge base is combined to carry out man-post matching and generate recommendation of context perception, and self-optimization of a matching strategy is realized through mechanisms such as reinforcement learning and the like. According to the invention, accurate and dynamic talent matching and intelligent decision making can be realized.

The invention provides an intelligent retrieval matching method and system for multidimensional data of image-text features, and relates to the technical field of icon image retrieval. Comprising the following steps: extracting image features, text content and semantic features of an icon image by using a convolutional neural network, an image segmentation attention mechanism network, a converter optical character recognition model and a bidirectional semantic understanding model, constructing the extracted features into heterogeneous feature tensors, and performing singular value decomposition to obtain icon feature fingerprint vectors; and constructing a multi-level index based on locality sensitive hashing, realizing rapid retrieval, calculating visual, text and semantic similarities in combination with a deep metric learning model, weighting according to variances and discrimination coefficients of similarity features to obtain a comprehensive similarity score, and outputting a retrieval result with the highest similarity.

The invention discloses an intelligent information screening method and system for a human resource system, and relates to the technical field of human resource systems. The problem that a traditional matching method is difficult to deeply mine semantic association and cannot accurately measure the matching degree of candidates and posts is solved. According to the method, through multi-step intelligent data processing and optimization, the candidate screening efficiency and accuracy are remarkably improved, a semantic analysis technology is adopted to construct a multi-layer topic tree and an emotion analysis model, and deep semantic matching of candidate resumes and position requirements is achieved; meanwhile, candidates are intelligently screened by using a bidirectional semantic matching network, the screening effect is continuously optimized by mining potential high-matching candidates and historical data feedback, bidirectional recommendation is realized through a block chain communication link, the security and efficiency of data transmission are guaranteed, complex enterprise recruitment requirements can be effectively adapted, the recruitment period is shortened, and the recruitment efficiency is improved. The man-post matching accuracy is improved, and the human resource management cost is greatly reduced.

The invention discloses a BIM-based two-dimensional data and three-dimensional model verification matching method and system. The system comprises a 2D drawing processing module, a 3D model processing module, a difference detection module and a report generation module. In the 2D drawing processing module, a user imports a process pipeline plan and a profile map, and through preprocessing and image semantic analysis, pipeline elevation labeling information is extracted and numbered. And the 3D model processing module loads a target BIM model, locates a process pipeline area, analyzes pipeline parameters and a space state, analyzes a pipeline topological relation and performs conflict verification. And the difference detection module binds the 2D drawing with a corresponding pipeline object in the 3D model, establishes a mapping relation, compares differences, and performs labeling and classification. And the report generation module is used for carrying out visual annotation in the 2D drawing and the 3D model based on the annotation difference, automatically generating a difference comparison table and outputting a report, so that efficient and accurate verification and matching of the two-dimensional data and the three-dimensional data are realized, the examination period is shortened, and the rapid collaborative correction efficiency of the construction team is improved.

The invention discloses a computing power analysis method used in a cloud computing environment, a matching method and a computer device. The computing power analysis method used in the cloud computing environment comprises the steps that historical task operation data of different types of computing power nodes in a cloud computing platform are acquired, associated computing power portraits corresponding to the computing power nodes are generated through preprocessing, and then a target model is trained in combination with the historical task operation data to obtain a task-computing power mapping model; performing multi-dimensional dynamic and static analysis on a to-be-executed user calculation task to obtain task feature information; and analyzing the task feature information based on the task-computing power mapping model to obtain analysis information of each type of computing power nodes. According to the method, the required computing power resources and the analysis information of each type of computing power nodes can be accurately predicted according to the dynamic and static analysis feature information of the task before the task is executed, so that accurate computing power identification and intelligent matching are realized, the reasonability and efficiency of task allocation are improved, the resource waste rate is effectively reduced, and the adaptation accuracy in a multi-tenant environment is improved.

The invention discloses an employment information matching method and system based on data analysis, and particularly relates to the field of employment matching, and the method comprises the steps: collecting structured and unstructured data, and carrying out the semantic feature extraction through employing a BERT model and BiLSTM-CRF; in the preprocessing stage, entity standardization is realized through a knowledge graph, and a job seeker portrait and post model including a skill matrix and an occupational development trajectory is constructed; in the feature engineering stage, extracting four core features of skill matching degree, salary expectation integrating degree, commuting tolerance and occupational development goodness of fit; the salary integrating degree is quantitatively evaluated through a bidirectional tolerance model, a random forest model with time decay is used for dynamic weight, feature weight is generated based on historical successful cases, and a real-time feedback mechanism is introduced to adjust a weight coefficient; finally, the matching degree function fuses the weighted features and the industry trend factors, and the model is continuously optimized through a three-level updating mechanism.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a segmentation and matching method, device, equipment and medium for an image background region. Multi-scale features are obtained through cavity convolution branch, fusion features are generated through weighted fusion, the fusion features are input into a decoder network to obtain a background segmentation mask, a background area image is obtained by combining the preprocessed image, and a visual converter model is input to extract background feature vectors; and performing similarity matching with a target feature vector in a preset feature database to retrieve a target image. According to the method, the segmentation accuracy and robustness are improved through combination of multi-scale feature fusion and the visual converter, meanwhile, the retrieval discrimination and reliability are enhanced through feature matching, the problems that an existing method is insufficient in segmentation precision and limited in retrieval capacity are solved, and precise segmentation and efficient retrieval of the background in a complex scene are achieved.

The invention provides a cross-hardware platform AI algorithm model automatic matching migration method and system based on low-code development, and the method comprises the steps: firstly receiving an AI algorithm model uploaded by a user, obtaining the information of a target hardware platform needing to be migrated, carrying out the optimization adjustment of the AI algorithm model according to the obtained information of the target hardware platform, and carrying out the automatic matching migration of the AI algorithm model. The optimization and adjustment process comprises the following steps: analyzing the AI algorithm model, selecting a corresponding model type according to the function of the algorithm model, carrying out parameter quantification, model conversion, model deployment and evaluation on the AI algorithm model according to the model type, classifying and downloading the converted model, and carrying out optimization and adjustment on the model. An optimized AI algorithm model is obtained; and finally, deploying the obtained AI algorithm model to a target hardware platform. According to the method, the defects of a traditional matching method can be overcome, the model performance and universality are improved, and a unified adaptive solution is provided for different hardware platforms.

The invention provides a self-adaptive semantic-driven data set field matching method and system, and the system comprises a data preprocessing module which is used for carrying out the cleaning, standardization and preliminary analysis of an input data set, and extracting a field name, a data type, a field description and a data sample; the deep semantic representation modeling module is used for constructing a field-level semantic representation vector; the multi-level similarity calculation module is used for comprehensively calculating the grammatical similarity, the semantic similarity and the statistical similarity among the fields, dynamically adjusting the weight of the similarity of each level by adopting a weighted fusion algorithm, and generating a comprehensive similarity matrix; and the matching result management and application module is used for generating a field matching mapping table and a fusion suggestion according to the comprehensive similarity matrix. According to the method, high-precision automatic matching of data set fields is realized by fusing deep semantic understanding, multi-dimensional similarity calculation and incremental adaptive learning, and the efficiency and accuracy of data set fusion are remarkably improved.

The invention discloses a person and post matching method and device, equipment, a medium and a product, and relates to the technical field of information processing, and the method comprises the steps: receiving recruitment post data and resume data; and performing data matching on the recruitment post data and the resume data through a pre-constructed person-post matching model to obtain a matching result, the person-post matching model being constructed based on a recruitment vector model, an information extraction model, a weight evaluation model and a matching degree scoring model. Therefore, data matching is carried out on the recruitment post data and the resume data through the human-post matching model constructed by the recruitment vector model, the information extraction model, the weight evaluation model and the matching degree scoring model to obtain the matching result, and the problem that post requirements and resume advantages are not aligned in the prior art is solved. Therefore, the recruitment quality of the enterprise is not high and the talents are lost can be solved, and the accuracy of man-post matching can be improved.

The invention relates to the technical field of industry optimization, and discloses an enterprise value-added service intelligent matching method and system based on multi-modal learning, and the method comprises the following steps: dynamically evaluating the strong and weak chain relationship between enterprises, constructing a time-varying adjacency matrix, and updating the chain strength weight through a propagation correction mechanism; time sequence features of enterprise operation efficiency and innovation ability are fused, and a dual-channel fusion feature is generated through a cross-modal attention mechanism; and constructing a three-dimensional tensor based on the strong and weak chain scores, efficiency and innovation features, and generating final fusion features through CP decomposition and multi-head attention enhancement. According to the method, by extracting service features and modeling enterprise demands, combining similarity calculation, recommending the most suitable service and adopting a clustering algorithm to identify an enterprise demand mode, a recommendation strategy can be dynamically adjusted according to real-time data, the demand of manual intervention is reduced, the decision-making speed and efficiency are improved, and the method is suitable for popularization and application. And enterprises can quickly respond to market changes, and resource allocation is optimized.

The invention relates to the technical field of natural language processing and deep learning matching recommendation, and discloses a supply and demand matching method for digital science and technology personalized services, which comprises the following steps: carrying out semantic understanding and analysis on a submitted technical long text by adopting a natural language large model, effectively extracting a core text of technical contents, and carrying out semantic analysis on the core text; and based on the text classification model, accurately delimiting industry field labels, thereby solving matching obstacles caused by cross-industry term differences. Precise extraction of clear numerical parameters and performance indexes in technical texts is realized by using a natural language large model, a refined demand text set is established, and deep semantic vectorization representation is performed through a word embedding model; through cosine similarity calculation and a screening rule based on industry labels, the matching accuracy and reliability of cross-industry technical services are greatly improved; based on an association weight mechanism of performance indexes, semantic similarity and performance index association degree are comprehensively considered, and the accuracy of matching recommendation results is optimized.

The invention relates to the field of power question answering, in particular to a power knowledge question-answer matching method and system based on an improved retrieval enhancement generation technology, and the method comprises the steps: employing a training set to carry out the decomposition of a pre-constructed Embedding model, and carrying out the training of the Embedding model, and obtaining an Embedding fine tuning model; the method comprises the following steps: vectorizing a power field file by adopting an Embedding fine tuning model to obtain a power knowledge base, and constructing a regular expression rule base; the user consultation content is converted into a query vector, the query vector is matched with the regular expression rule base to obtain an accurate matching result, and meanwhile the query vector is matched with the power knowledge base to obtain a fuzzy matching result; semantic correlation sorting is carried out on the accurate matching result and the fuzzy matching result, and then resorting is carried out by adopting a resorter in combination with a service rule; and inputting the reordering result into a large language model to obtain a result corresponding to the user consultation content. The method can effectively solve the problem of inaccuracy during retrieval of a specific ID, and improves the retrieval precision of a retrieval enhancement generation system.

The invention discloses a similar law judgment document matching method based on prompt engineering and judgment prediction, belongs to the field of text matching, aims to solve the problem of inaccurate case matching in case retrieval of law case handling personnel, and is characterized by comprising the following steps: S10, acquiring a training data set; wherein the data set comprises case serial numbers, judgment texts and legal elements; s20, training a class case matching model by using the training data set, wherein the class case matching model comprises a plurality of Bert models sharing weights; wherein the class case matching model is trained by adopting a joint learning method; and S30, performing similarity prediction on the judgment text contents of a plurality of input judgment documents by using the trained class case matching model.

The invention discloses a Token level cache matching method and system of a large language model and a storage medium. The method comprises the following steps: constructing a local context fragment; generating a context embedding vector of the current Token, and calculating a context entropy value, a semantic consistency index and a semantic change gradient; for the target Token, determining a semantic category of the target Token; inputting the semantic category to which the target Token belongs into a Hash decision maker to obtain a Hash granularity level; if the Hash is the first-level Hash, executing fixed-length Hash and mapping the Hash to a semantic cache bucket based on a semantic theme or a context range of the first-level Hash; if the first-level hash is the second-level hash, dynamically adjusting the hash length according to the semantic similarity with the adjacent Token; if the Hash granularity level of the target Token is a third-level Hash, constructing a high-dimension context representation and executing a fine Hash operation; and matching the hash result with the KV in the pre-stored cache, and executing corresponding operation according to the matching result.

The invention discloses a component identifier matching method and system based on NLP semantic segmentation and a multi-level word library, and relates to the field of constructional engineering informatization, and the method comprises the steps: building a standard main word library and a preset compensation word library, carrying out the semantic segmentation of an input component identifier, and obtaining a segmented lexical element sequence, calculating semantic similarity and morphological similarity between the segmented lexical element sequence and entries in a standard main word bank, and performing standard term mapping based on the semantic similarity and the morphological similarity to obtain a standard term mapping result; carrying out structured data conversion on the segmented lexical element sequence which does not complete the standard term mapping by utilizing a regular expression mode to obtain structured data, and carrying out AI extension library matching on the segmented lexical element sequence which does not complete the structured data conversion to obtain a synonym matching result; and combining the standard term mapping result, the structured data and the synonym matching result to obtain component identification description. By implementing the method, the matching accuracy of the component identifier names in the engineering project can be improved.

The invention relates to the technical field of ship manufacturing, and discloses a ship block assembly man-hour matching method based on multi-modal process feature recognition. Multi-modal process data including structured process parameters, unstructured process documents and the like are collected, each modal process feature vector is obtained through feature extraction, and then a fusion feature vector is generated through a multi-modal feature fusion algorithm. And constructing a man-hour matching knowledge base according to historical assembly order data, and determining an assembly process sequence and a man-hour allocation scheme by using a dynamic matching algorithm. According to the method, a process set can be optimized through a graph traversal algorithm, real-time sensor data are analyzed to deal with abnormal fluctuation, and process compliance is verified by means of a process rule knowledge graph. According to the method, multi-modal data can be effectively fused, the man-hour matching precision is improved, the assembly process is optimized, the ship block assembly efficiency and quality are improved, and the cost is reduced.

The invention discloses a fusion semantic vector space mapping-based translation ambiguity term accurate matching method, which comprises the following steps of: S1, obtaining source language ambiguity terms, context texts and a target language candidate translation list, and extracting domain tags and term matching features to form a multi-modal data set; s2, using improved XLM-R model coding to generate term-level, sentence-level and translation-level semantic vectors; s3, training a dynamic mapping matrix based on a bilingual parallel corpus, and aligning source side vectors to a shared semantic space; s4, fusing the source-side basic vector and the multi-dimensional features through a double-channel attention fusion network, and generating source-side and translation-side comprehensive semantic vectors; s5, introducing term-context attention weight to correct cosine similarity; and S6, outputting an optimal translation through normalized sorting and part-of-speech secondary judgment. According to the method, multi-field ambiguous term accurate matching is realized, the term translation precision and efficiency in professional fields are improved, and the requirements of high reliability of term translation in the fields of medicine, machinery, computers and the like are met.

Embodiments of this application provide a model matching method and a communication apparatus. The method includes: receiving an identifier of a first artificial intelligence AI model from a first communication apparatus; and determining, based on the identifier of the first AI model, whether a second AI model is deployed on a second communication apparatus, where the second AI model is an AI model that matches the first AI model. In this way, an identifier of an AI model can be exchanged to determine, based on the identifier of the AI model, an AI model that matches the AI model.

According to the carbon emission factor dynamic matching method based on the large language model, efficient and accurate matching is achieved through multi-step optimization. The method comprises the following steps: firstly, collecting carbon emission factors and activity data from multiple sources, and cleaning and normalizing the data; then, performing deep semantic analysis on the data by utilizing a pre-trained large language model subjected to field fine adjustment to generate a high-quality semantic vector; the system unifies data formats and completes physical quantity conversion through a unit identification and conversion mechanism; in the matching process, the system performs intelligent matching by calculating cosine similarity of semantic vectors, and under the condition that the similarity is close, a weighted average mechanism is applied to optimize a result, so that the matching precision is further improved; meanwhile, the system analyzes the relevance of newly submitted data by utilizing a factor library dynamic updating mechanism, dynamically expands the factor library and continuously optimizes the matching performance; the whole method improves the matching precision and efficiency, reduces manual intervention and errors, and reduces the calculation cost of carbon emission.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a policy and enterprise intelligent matching method and system based on deep semantic understanding. The method comprises the following steps: acquiring multi-source policy information data and enterprise information; generating an enterprise multi-dimensional portrait according to the enterprise information; performing first matching and second matching on the multi-source policy information data and the enterprise multi-dimensional portrait respectively; and constructing a comprehensive matching model, and based on the first matching result and the second matching result, using the comprehensive matching model to complete policy and enterprise intelligent matching. According to the policy and enterprise matching method, the problems of low efficiency, inaccurate matching and important information omission in a traditional policy and enterprise matching method are solved through matching policies and enterprise portraits. Through automatic data collection, preprocessing and matching calculation, manual intervention is greatly reduced, the policy and enterprise matching efficiency is improved, and an enterprise can quickly find a policy suitable for the enterprise.

The invention discloses a person and post matching method and system based on talent evaluation. The method comprises the following steps: step 1, setting a talent evaluation standard; 2, establishing an occupational quality model of the personnel; 3, creating a post model; 4, developing a post matching algorithm; 5, internal employee behavior analysis; step 6, a post recommendation system; step 7, quantitative evaluation and demand analysis; 8, a data processing technology; compared with the prior art, the method has the advantages that the scientificity, objectivity, efficiency and accuracy of personnel and post matching are improved, personalized recommendation of employees is realized, the efficiency of talent recognition is improved, and the method has remarkable superiority.

The invention discloses an intelligent task matching method based on multi-modal data and an RAG technology, and relates to the technical field of intelligent management, and the method comprises the steps: collecting multi-modal data, building modal data feature representation, and carrying out feature fusion based on a cross-modal collaborative attention mechanism; reading an intelligent task, performing data evaluation of a fusion result, establishing an RAG retrieval requirement, executing retrieval matching of an external database, and establishing a generation database; and constructing and optimizing a multi-learning framework for executing the intelligent task, and executing task processing of the corresponding intelligent task according to the converged multi-learning framework. The technical problems that in the prior art, a traditional single-modal processing method cannot meet the multi-modal data requirement in a complex scene, and the task processing adaptability and accuracy are low are solved, and the multi-modal data fusion, RAG retrieval enhancement and cross-task cooperation mechanism are achieved. And the adaptability and accuracy of intelligent task matching are improved.

The invention relates to the technical field of policy matching, and discloses an intelligent matching system based on big data and a matching method thereof. The system comprises a data acquisition module, a condition analysis module and an intelligent matching module. The data acquisition module is used for acquiring policy text, enterprise qualification and historical case data and constructing a matching basic database; the condition analysis module is used for extracting policy dominant conditions through a regular expression, mining implicit conditions from failure cases by utilizing an association rule algorithm, and calculating condition weights by adopting a random forest algorithm; the intelligent matching module integrates dominant and implicit conditions to calculate a matching coefficient, dynamically divides risk levels and generates an early warning, the matching comprehensiveness and the early warning sensitivity are improved by mining the implicit conditions, dynamically adjusting the matching model and reinforcing a learning optimization mechanism, upgrading from text matching to risk prejudgment is achieved, and the method has the advantages of being high in practicability and easy to popularize. The enterprise declaration success rate can be improved, and the repeated declaration rate is reduced.

The invention belongs to the technical field of large model application, and particularly relates to a semantic enhancement text matching method and system based on an achievement transformation large model. The semantic enhancement text matching method based on the achievement transformation large model comprises five parts, namely preparation of data and knowledge, enhancement of multi-level semantic knowledge, learning of semantic difference, intelligent weight distribution of semantic information and prediction of text similarity. The preparation of data and knowledge is used for constructing basic data and knowledge resources required by a text matching task through text preprocessing and knowledge base construction. And enhancement of multi-level semantic knowledge: enriching semantic representation of the text by injecting external knowledge at three levels of word level, phrase level and sentence pattern level. And semantic difference learning: learning fine-grained semantic features by comparing a learning framework, and improving the recognition capability of the model on the text semantic difference. Intelligent weight distribution of semantic information is carried out, and dynamic balance and optimal combination of different source information are realized by evaluating feature importance and dynamically adjusting weights. And predicting the text similarity, calculating the similarity score of the text pair by integrating the feature information of each stage, and providing an interpretable prediction result. The complete text matching method and system are designed based on the achievement transformation large model, the problems of insufficient knowledge utilization, incomplete semantic representation, fixed feature weight and the like of the large model in a text matching task are effectively solved, the text matching effect of the large model in an actual application scene is improved, and the text matching efficiency is improved. And a feasible solution is provided for achievement transformation of a large model technology.

The invention discloses a BIM (Building Information Modeling)-based building component attribute matching method and a BIM-based building component attribute matching system, contour line segments of a target and a reference component are segmented, curvature change values are recorded, and the local feature expression capability is enhanced. Then, dynamic adjustment parameters are introduced to optimize eigenvector weighting processing, so that the matching process adapts to different shape complexity, and mismatching is avoided; and then, identifying a local similar region by comparing the corrected matching parameter set, and generating a registration mapping relation, thereby improving the accuracy of space consistency judgment. A matching priority matrix is constructed based on the mapping relation and the shape distribution difference, high-matching-degree candidate components are quickly screened, and the matching efficiency is improved. Finally, a feedback adjustment mechanism is adopted in the attribute verification stage, it is ensured that the matching result meets the preset precision requirement, and self-adaptive optimization is achieved. The method effectively solves the problems of low precision and poor stability in non-standard geometric component matching in the prior art, and has important practical value.

The invention provides a multi-target tracking method and device based on a large model. The method provided by the invention comprises the following steps: acquiring a current frame image of a video, detecting a target in the current frame image through a detector, and generating a target detection frame and a corresponding confidence score; based on a dynamic equation of a Kalman filter, predicting the position of a track fragment in the current frame of image according to the track fragment of the previous frame of image; when the target detection frame is an effective detection frame, associating the target detection frame with the predicted trajectory fragment based on a feature coordinate matching method, and updating the position of the trajectory fragment through an observation equation after successful association; when the target detection frame is not the effective detection frame, generating a mask fragment of the target based on a mask fragment updating method, associating the mask fragment with the predicted trajectory, and updating the position of the trajectory fragment; and outputting the tracking result of the current frame, repeatedly executing the step of outputting the tracking result of each frame, integrating the tracking results of all single frames, and generating a complete tracking trajectory of all targets in the video.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology, medical treatment and health, and discloses a data matching method based on security approval, which comprises the following steps: creating a data table and configuring access authority, inserting data in the table into an acquisition table and then synchronizing to an intermediate database and adding a field identifier; synchronizing the data of the intermediate database to a distributed storage structure partitioned according to dates through a timed task; receiving a data table name, a data type and identification information, determining a matching field based on the data type, matching the data value in the distributed storage with a target field, and generating a result set; and submitting a matching result as a security signature, triggering an approval process, and sending result data to the terminal according to permission mapping after approval is passed. According to the method, the data processing efficiency is improved and the sensitive data circulation risk is reduced by constructing the structured acquisition process, setting the automatic synchronization mechanism, executing data matching based on the field type and integrating the approval control logic.