546 results about "Keyword extraction" patented technology

The invention provides a prospecting prediction method based on a multi-agent technology. The method specifically comprises the steps that S11, keyword extraction is conducted on mineral problems input by a user; s12, establishing a vector database of the mineral knowledge text and a graph database of the mineral knowledge graph, and optimizing the extracted keywords to generate cue words; s13, constructing a large prospecting model to think the cue words, expanding the cue words, and obtaining a complete statement for professional expression of the user question again; s14, performing multi-task decomposition on the complete statement to obtain a plurality of sub-tasks, and scheduling the plurality of sub-tasks to corresponding agents based on a graph database of the mineral knowledge graph to perform step-by-step prediction; and S15, integrating prediction results of the intelligent agents and feeding back the integrated prediction results to a user. According to the method, prospecting prediction is promoted to be converted from manual driving to intelligent driving, and emerging technical support is provided for prospecting breakthrough.

The invention discloses a traditional Chinese medicine intelligent inquiry method and system based on a knowledge graph and medical case enhancement RAG, and the method comprises the following steps: collecting traditional Chinese medicine related data from a plurality of sources, and carrying out the data cleaning, formatting and standardization processing, and constructing a knowledge graph containing traditional Chinese medicine symptoms, disease causes, prescriptions, and the mutual relation of the symptoms, the disease causes, the prescriptions; expressing a knowledge structure in the graph in a triple form; searching a local sub-graph related to query based on query keyword extraction and generalization by utilizing a Leiden community detection algorithm; a mixed retrieval strategy is adopted, global search and local search are combined, recalled contents are sorted and scored, global and local retrieval results are fused, and a large language model is uniformly output. The system aims at improving the intelligent level and individuation ability of traditional Chinese medicine diagnosis, firstly, a knowledge graph covering entities such as traditional Chinese medicine theories, symptoms, pathogenesis and prescriptions and relationships of the entities is constructed, mass traditional Chinese medicine data are systematically integrated, deep understanding and semantic association of traditional Chinese medicine complex diagnosis and treatment logic are ensured, and the traditional Chinese medicine diagnosis and treatment efficiency is improved. Through deep combination of the structured knowledge base and the generated model, the diagnosis and treatment accuracy is improved, and the model fine tuning and updating cost is reduced.

The invention relates to the technical field of medical image report error correction, in particular to a medical image report automatic quality control error correction system and method. The method comprises the steps of importing medical image data, analyzing the medical image data by using an open source tool, performing standardization processing and local contrast enhancement on an image, performing automatic segmentation of a target area through a UNet model, and calculating density features of a key area to obtain feature vectors; meanwhile, the report text data is subjected to word segmentation, standardization processing and keyword extraction steps, the word importance is evaluated by utilizing a BERT model, a semantic vector of the report is generated through a text model, finally, the consistency of the image and the report is detected by calculating the similarity between a feature vector and the semantic vector, and an automatic error correction program is started when the image and the report are inconsistent. And error types are analyzed and corrected. According to the method, medical image processing, natural language processing and deep learning technologies are combined, so that human errors are reduced, and the diagnosis efficiency and accuracy are improved.

The invention belongs to the field of question and answer generation, and provides an interactive retrieval enhancement question and answer generation method and system based on a knowledge graph, and the method comprises the steps: carrying out the document partitioning based on an original document set, generating a global block set, carrying out the entity extraction of each text block in the global block set, and obtaining an entity set; performing relation extraction on entity subsets in each text block in the entity set to obtain a global relation set; generating a plurality of sub-knowledge maps based on the global block set, the entity set and the global relationship set, and performing entity fusion and relationship fusion on the sub-knowledge maps to obtain a knowledge map; performing keyword extraction and semantic embedding on the original problem to obtain a dense vector, performing semantic embedding based on the knowledge graph to obtain an embedded vector, and generating a candidate entity set according to the dense vector and the embedded vector; and based on the candidate entity set, utilizing a large language model calling tool to carry out extended search to generate a candidate information set, and utilizing a large language model to obtain an answer to the original question based on the candidate information set.

The invention relates to the technical field of computer vision and human-computer interaction, and discloses an AI digital human intelligent interaction method, system and equipment based on multiple modalities, and the method comprises the steps: pre-awakening a digital human when a human face is detected, and further thoroughly awakening the digital human based on recognized preset voice information or preset gesture information; voice and video information of a user in the interaction process is obtained, a keyword extraction result, a gesture recognition result and an emotional state tag are generated, a pre-constructed knowledge base is utilized to retrieve related information, a big language generation model module is combined to generate an answer text, and the answer text is input into a preset voice synthesis model to generate emotional voice output. And based on the current emotional state label of the user, driving the digital human animation to be output in an emotional manner. According to the method and the system, the digital human for understanding the emotion of the user, generating personalized answers, providing voices with rich emotions and displaying natural expressions and actions can be created, better interaction with the user can be realized, and more humanized and effective services can be provided.

The invention discloses a science and technology public text intelligent classification and service method and device based on deep learning. The method comprises the steps that multi-source science and technology public text data are acquired and preprocessed; extracting keywords by adopting a keyword extraction algorithm, and splicing the keywords with the public text title to form enhanced text features; constructing a multi-dimensional public text classification system and performing data annotation; performing feature extraction and fine adjustment by adopting a BERT pre-training model to obtain a classification model; automatically classifying the newly-added public texts and visually presenting the newly-added public texts; and generating a personalized recommendation result based on the user portrait and the public text feature index. The invention further relates to a technical scheme of multi-objective quality diversity optimization, heterogeneous resource allocation and fusion of the LPLC2 neural network and the BERT. The technical problems that a traditional method is limited in complex semantic understanding ability, single in classification dimension and lack of an integrated solution are solved, and the accuracy of science and technology public text classification and the intelligent level of service are improved.

The invention discloses an information co-processing platform based on a knowledge graph and a potential relationship prediction method. The information co-processing platform comprises a data acquisition processing layer, a knowledge extraction intelligent analysis layer and a knowledge application decision-making layer, the data acquisition processing layer comprises a data acquisition module, a data format conversion module and a knowledge base management module; the knowledge extraction intelligent analysis layer comprises a keyword extraction module, a keyword layering module, a semantic fusion module and a knowledge ontology dynamic evolution module; and the knowledge application decision-making layer comprises an evolution state judgment module, a military strategy graph generation module, an evaluation result acquisition module and a military strategy guidance information updating module. According to the method, a large amount of dispersed and isolated intelligence is integrated from actual military field business data by taking a real-time military strategy as a guide, association of military intelligence information is realized from a semantic level, powerful support is provided for comprehensive analysis, research, judgment and display of the intelligence information, analysis efficiency is improved, and effective cooperation of military strategy and intelligence data processing is realized.

The invention relates to the technical field of video detection, in particular to a monitoring video detection and identification method and system based on deep learning, and the method comprises the steps: obtaining a target monitoring video of a monitoring region based on monitoring camera equipment, obtaining a video frame sequence of the target monitoring video, and carrying out the target screening of the video frame sequence, so as to obtain a video frame set; and performing target detection on each video frame in the video frame set based on a trained target detection model to obtain a target detection result set of each video frame in the video frame set. According to the method, through combination of keyword extraction and a large language model, complex monitoring data can be converted into structured event description, the textualized information not only facilitates management personnel to rapidly know the field condition, but also can provide reference for subsequent event processing, and the event description generated in real time is combined, so that the management personnel can conveniently and rapidly know the field condition. And a manager can make a coping strategy more efficiently.

The invention relates to the technical field of human resource management and information, and discloses an employment post recommendation method based on a human resource database, and the method comprises the following steps: S1, data collection; s2, data processing; s3, keyword extraction; s4, performing similarity calculation; s5, post filtering and sorting; s6, post recommendation; and S7, effect evaluation: through automatic data processing and similarity calculation, resumes most matched with post demands can be quickly screened out, the workload of recruiters is reduced, accurate matching degree evaluation is beneficial to realizing optimal configuration of talents and posts, employee loss and recruitment cost waste caused by mismatching are reduced, and the efficiency of recruitment is improved. The recruitment will and the matching degree of the job seekers can be improved through accurate recommendation, so that the recruitment success rate and efficiency are improved, more high-quality job seekers are attracted through efficient post recommendation and accurate matching, and the talent reserve and competitiveness of enterprises are enhanced.

The invention provides an enterprise order purchase management method and system, and relates to the technical field of purchase management. Performance state feedback information corresponding to a purchase order is acquired; performing multi-dimensional alignment analysis on the performance state feedback information, generating performance efficiency boundaries of different purchase material types, and constructing purchase constraint rules of order purchase in each execution stage according to the performance efficiency boundaries and the user sign-in data; performing anomaly detection on a keyword extraction strategy during purchase demand identification to obtain a matching deviation situation of the purchase demand in supply chain matching, performing hierarchical verification on resources of suppliers based on the matching deviation situation and delivery sign-in data, and generating supply recommendation rules of hierarchical suppliers; and generating a target order corresponding to the purchase demand in the supply chain according to the purchase constraint rule and the purchase recommendation rule. According to the application, the performance state can be dynamically monitored and suppliers can be recommended in a graded manner under the supply chain collaboration platform, so that the precision of supply chain matching in the purchasing process is improved.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for providing conversation intelligence services may include pre-processing, transcribing, and post-processing. A conversation recording may be pre-processed generating a conversation record (e.g., conversation object). The pre-processed conversation recording may be transcribed into a transcript. The transcript may be post-processed which may include keyword extraction, topic extraction, feature extraction, event generation, trigger action, and / or search indexing. Conversation information may be presented based on the pre-processing, the transcribing, and the post-processing.

The invention relates to the technical field of red tide anomaly detection, in particular to a red tide anomaly detection method and system based on an improved multi-mode Transform. The method comprises the following steps: acquiring a remote sensing image and text data; respectively carrying out data preprocessing according to the obtained remote sensing image and text data; performing visual positioning and text selection based on the preprocessed data; performing cross-modal feature learning on the basis of a hierarchical Transform of a multi-modal capsule mechanism; guiding an attention mechanism based on a semantic path to carry out image-semantic feature alignment optimization; and carrying out multi-modal knowledge distillation on the optimized features. According to the method, an image and text preprocessing module, a visual positioning module, a keyword extraction module and other modules are combined, multi-angle accurate perception of a complex red tide scene is achieved, and the bottleneck that a red tide area is difficult to accurately recognize under the condition that data are single and information dimensions are limited in a traditional method is broken through.

The invention relates to the technical field of artificial intelligence, and particularly provides a knowledge graph-based interview simulation method, system and device and a storage medium, and the method comprises the steps: obtaining a resume document, and extracting key information from the resume document through a keyword extraction technology; retrieving a local graph from a pre-constructed knowledge graph based on the key information; generating a problem based on entities and relationships in the local atlas; receiving answer data for the question, and extracting entities and relationships from the answer data; and matching the extracted entities and relationships with the entities and relationships corresponding to the questions, and generating evaluation data based on a matching result. According to the method, the personalized resume of the interviewer can be deeply analyzed, the specific demand of a target post can be accurately met, and a series of interview problems which are in accordance with reality and are rich in pertinence can be intelligently generated. And the answer quality of the interviewer can be evaluated instantly and objectively.

The invention discloses a question and answer method, device and equipment based on a graph database and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a target question of a user, and performing question similarity verification on the target question and a question knowledge base; under the condition that it is recognized that the problem similarity verification is not passed, keyword extraction is conducted on the target problem, and a target keyword of the target problem is obtained; based on the natural language large model, performing semantic analysis on the target problem to obtain target semantic information; and based on the general language large model, according to the target keyword and the target semantic information, determining a graph database query statement, and according to the graph database query statement, determining a target answer of the target question from the target graph database. According to the technical scheme, after similarity verification, keyword extraction and semantic analysis are carried out on the problems, the optimized graph database query statement is generated through the large model, and the efficiency and accuracy of graph database query can be effectively improved.

The invention discloses an LLM-based document structuring automatic processing method and system, and relates to the technical field of document processing, and the method comprises the following steps: S1, document input and preprocessing: removing special characters in a disordered text, unifying a coding format, and adjusting the semantic density; s2, redundancy removal and keyword extraction: removing repeated information in the disordered text, and extracting a first keyword; s3, semantic extraction and structuring: parsing the disordered text sentence by sentence according to the first keyword, and extracting main meaning and related semantic information corresponding to each sentence to obtain an extraction result which comprises a second keyword and sentence meaning of the same sentence; s4, constructing and updating a semantic stack: constructing and updating the semantic stack according to an extraction result, and storing a semantic unit; one semantic unit corresponds to the same extraction result; and S5, structured output: outputting the data in the semantic stack in a preset format. The method is suitable for structured processing of complex disordered documents in multiple fields and multiple types.

The invention relates to the technical field of financial risk control, and discloses a financial risk control system based on a block chain and public opinions, and the system comprises a public opinion data collection module which is used for collecting financial-related public opinion data from multiple channels; the public opinion analysis and processing module is used for performing sentiment analysis, keyword extraction and trend prediction on the collected public opinion data; the block chain storage module is used for storing public opinion analysis results and risk related data, and is responsible for the non-tampering and transparency of the data; the intelligent contract execution module is used for dynamically triggering risk control measures according to the public opinion analysis result; the risk assessment and credit scoring module is used for performing dynamic risk assessment on the enterprise and generating a credit score; and the risk early warning and control module is used for generating risk early warning information and automatically executing a risk control strategy. According to the invention, through the distributed account book and the consensus mechanism of the block chain, the system ensures that the whole-process records of the public opinion analysis result, the risk score and the risk control strategy cannot be tampered.

A chatbot system detects commercial intent during conversations with users and provides targeted advertisements. The chatbot system extracts keyword candidates from a conversation and assigns relevance scores based on the meaning of the conversation and commercial scores based on a machine learning model trained to detect commercially-related keywords. The chatbot system selects keywords based on a combination of the scores and transmits the selected keywords to advertising content servers that provide advertising content including advertisements selected using the keywords. The chatbot system displays the advertisements to the user during the conversation. The chatbot system may integrate the advertisements into the chatbot's responses, select advertisements based on a predicted click-through rate for the user, train the machine learning model using translations of labeled examples, access a long-term memory of the user's conversations and user profile data to provide context for the advertisements, and analyze messages from multiple users in group chats.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for providing conversation intelligence services may include pre-processing, transcribing, and post-processing. A conversation recording may be pre-processed generating a conversation record (e.g., conversation object). The pre-processed conversation recording may be transcribed into a transcript. The transcript may be post-processed which may include keyword extraction, topic extraction, feature extraction, event generation, trigger action, and / or search indexing. Conversation information may be presented based on the pre-processing, the transcribing, and the post-processing.

The embodiment of the invention provides a data retrieval method, device and system, and relates to the technical field of data processing.The method comprises the steps that an original retrieval statement and an associated retrieval statement are obtained, and a target retrieval statement is obtained; and performing keyword extraction on the target retrieval statement to obtain a retrieval keyword. Retrieving in a knowledge graph database based on the retrieval keyword to obtain a first retrieval result including the first data file; performing retrieval in a basic database based on the retrieval keyword and the target retrieval statement to obtain a second retrieval result including a second data file; performing retrieval in the vectorization database based on the statement feature vector of the target retrieval statement to obtain a third retrieval result including a third data file; and determining a target retrieval result comprising the target data file from the first retrieval result, the second retrieval result and the third retrieval result based on the interaction information of each data file, so as to improve the accuracy of data retrieval and improve the user experience.

The invention discloses a college policy question and answer large model combined with a retrieval enhancement generation technology and a construction method. The method comprises the following steps: inputting a user question large language model and question-answer text vector data about college policies; multi-level indexes are adopted to retrieve question-answer text vector data, and policy content positioning related to the user question is achieved according to a retrieval result; semantic sorting is conducted on the retrieval results according to the scores, secondary evaluation of the policy content is achieved, and the policy content with the high semantic relevancy is preferentially provided; strategy content reasoning is carried out in a large and small model combination mode, including problem rewriting and keyword extraction by using the small model and strategy content generation by using the large model, so that computing resources are optimized and time delay is reduced; and generating answers to the user questions according to content reasoning, and outputting accurate college policy-related answers. The invention aims to improve the accuracy and efficiency of college policy information retrieval.

The invention discloses a public opinion report generation method based on a large language model. The public opinion report generation method comprises the following steps: S1, extracting description information of a public opinion event from user input by adopting a pattern matching method; s2, performing keyword extraction and semantic expansion on the description information by utilizing a large language model (LLM); s3, using the processed keywords to retrieve information related to the public opinion event, and sorting the retrieved information; s4, performing multi-dimensional feature quantification processing on the public opinion information, reordering the public opinion information, and screening a specified number of information from the reordered public opinion information by adopting a down-sampling function to serve as background data of the public opinion report; s5, inputting the screened background data into a large language model, and generating a final public opinion report through iterative analysis; according to the method, the public opinion report is automatically generated, and a multi-dimensional feature quantification processing and information reordering mechanism is adopted, so that the selected public opinion information is highly related to the event and is important, and the public opinion report is more accurate and comprehensive.

The invention discloses an electric power customer service work order abstract generation method based on voice dialogue, and relates to the technical field of voice recognition, and the method comprises the steps: receiving voice dialogue content; key entities related to electric power are extracted; performing deep understanding on the voice text based on the semantic analysis model; based on the extracted key information, automatically generating an abstract of the electric power customer service work order; submitting the automatically generated work order abstract for manual auditing; the invention discloses an electric power customer service work order abstract generation system based on voice conversation. The system comprises a voice receiving module, a voice text processing module, a keyword extraction module, a semantic analysis module, a work order generation module, a work order auditing module and a work order publishing module. Through advanced voice recognition, natural language processing, semantic analysis and emotion recognition technologies, automation of the work order generation process can be achieved, the work order processing quality and efficiency can be optimized, and therefore remarkable benefits and improvement are brought to electric customer service work.

The invention relates to the technical field of information retrieval, and provides a privacy information retrieval system and method based on a block chain and function secret sharing, and the method comprises the steps: carrying out the encryption and keyword extraction of a data document; generating a Bloom filter index table between the keyword and the document based on the keyword trap door; uploading the encrypted file and the Bloom filter index table to a DSSP end, and transmitting the hash value of the encrypted file and the Bloom filter index table to a block chain for on-chain storage; storing the encrypted file in a distributed storage node under the chain according to the divided share; and the block chain calculates data of each column of the Bloom filter index table through consensus to obtain a message verification code, and the message verification code is stored on the block chain. According to the method, the index is constructed through symmetric encryption and the Bloom filter, and block chain evidence storage and FSS distributed storage are combined, so that data privacy protection and index credibility are realized. Through verification and tampering prevention, the problem that the existing FSS lacks verification and auditing mechanisms is solved.

The present invention proposes a wind power equipment accident diagnosis method and system based on knowledge graph, and the method comprises the following steps: obtaining wind power equipment operation and maintenance data and standard specification data, and preprocessing the data to construct a wind power equipment safety data set; performing keyword extraction and text data annotation on text data in the wind power equipment safety data set to construct a keyword corpus, and performing word vector training on the annotated text to obtain a wind power equipment safety data word vector set; constructing an entity recognition model based on a BiLSTM-CRF algorithm combined with a similarity algorithm, and outputting an entity recognition prediction result; and performing relationship extraction and attribute extraction on wind power equipment safety data to construct a wind power equipment safety knowledge graph; by integrating the operation and maintenance data of wind power equipment and standard specification data in the safety field, constructing and optimizing the knowledge graph, accurate diagnosis and cause analysis of wind power equipment faults are achieved, thereby improving the diagnosis efficiency of wind power equipment.

The invention discloses a document retrieval method and device, equipment, a medium and a program product, and relates to the technical field of document processing. The method comprises the steps of obtaining a query statement of a document retrieval party, at least two candidate documents and candidate abstract vectors and candidate full-text vectors of the candidate documents; performing keyword extraction on the query statement to obtain a query keyword, and performing vectorization on the query statement to obtain a query statement vector; according to the query keyword, the query statement vector, each candidate document and a candidate abstract vector of each candidate document, performing coarse screening on each candidate document to obtain at least two coarse screening documents; and according to the query statement vector and the candidate full-text vector of each coarse screening document, performing fine arrangement on each coarse screening document to obtain a target retrieval document. According to the technical scheme provided by the embodiment of the invention, the accuracy of document retrieval is improved.

The invention provides an electric power document keyword extraction method based on Prompt and a knowledge graph, relates to the technical field of electric power document processing, and constructs a lightweight multi-level index knowledge graph in the electric power field by combining entity type and relation type division based on an electric power industry standard document and an electric power field corpus. The method comprises the following steps: performing vector modeling on a power document, constructing a multi-level index from an entity to a vector, realizing standardized semantic modeling and efficient hybrid retrieval of a power document field background, and obtaining a topic vector and a core paragraph of the power document in combination with power key information; according to the method, entity types are indexed in a knowledge graph by using subject vectors, similar entities are obtained to form knowledge sub-graphs, so that multilayer Prompt is obtained to guide a large language model to extract keywords, then knowledge graph similarity constraints are introduced to decode the output of the large language model, the keyword recognition capability in the power field is improved, and the keyword recognition efficiency is improved. And the accuracy of keyword type identification and the normalization of term naming are both considered.

The embodiment of the invention provides a Surreal DB-based cross-modal data processing method and device, and the method comprises the steps: carrying out textualization processing and vectorization processing on data of various different modalities to obtain a text and a vector, and carrying out word segmentation and keyword extraction on the text to obtain a text keyword; constructing a full-text index based on text keywords, constructing a vector index based on vectors, and storing data of different modalities in a data model based on Surreal DB in a mixed storage mode of the full-text index and the vector index; receiving query data of a user, performing textualization processing and vectorization processing on the query data to obtain a query text and a query vector, and performing word segmentation and keyword extraction on the query text to obtain query text keywords; and on the basis of the query text keyword and the query vector, performing vector retrieval and full-text retrieval in the Surreal DB-based data model, and outputting a matched multi-modal data retrieval result.

The invention relates to the technical field of information management, and discloses a project cloud platform data management method based on a process market, and the method comprises the steps: obtaining process demand information inputted by an enterprise user, and obtaining an enterprise name of the enterprise user; crawling enterprise process information through a web crawler tool, and performing multi-level classification to construct a process market platform; obtaining the business scope and the enterprise scale of the enterprise user according to the enterprise name, and determining the enterprise type of the enterprise user according to the business scope; performing keyword extraction on the process demand information input by the enterprise user to determine a target keyword of the process demand information; and matching a demand flow list in a flow market platform according to the enterprise type, the enterprise scale and the target keyword, and pushing the demand flow list to the enterprise user. According to the method, an intelligent process market platform is constructed, keyword extraction and analysis are carried out on demands of enterprise users, automatic aggregation and accurate matching of process resources are realized, and the process adaptation accuracy is remarkably improved.

The invention discloses a chef machine intelligent recommendation system based on machine learning, and relates to the technical field of machine learning. The system comprises a user command input and conversion unit used for acquiring a voice cooking demand instruction input by a user and converting the voice cooking demand instruction into a cooking demand text; the keyword extraction unit is used for extracting four types of cooking keywords from the cooking demand text to obtain a keyword candidate set; the categories of the cooking keywords comprise food materials, processes, nutrition and scenes; and the cooking recommendation unit is used for performing similarity retrieval and matching from a pre-established cooking keyword database according to the extracted keyword candidate set, and presenting results to the user according to a sequence of similarity from high to low. According to the method, complex and diversified natural language input can be efficiently processed, and personalized and precise menu recommendation is realized.

The invention discloses an AI large model-based arrival person screening method and device, a medium and equipment, and belongs to the field of screening, and the method comprises the steps of firstly obtaining screening conditions input by a user, including text content, image data and qualification information requirements, and to-be-screened arrival person data; next, performing word segmentation, keyword extraction and semantic matching on the text content of the person arrival data by utilizing a preset semantic analysis model in combination with BiLSTM, CRF and LDA technologies, and outputting a semantic score; meanwhile, note styles are recognized through the text classification model, logic judgment is conducted, and styles and logic verification scores are obtained. In addition, element detection and style verification are carried out on the image data through the multi-modal recognition model, and image matching scores are output; the qualification scoring module calculates qualification scores according to a preset weight formula. And finally, fusing the multi-dimensional scores to generate a comprehensive score, and carrying out accurate screening on the arriving persons according to the comprehensive score.