190 results about "Semantic query" patented technology

The invention discloses a digital twin three-dimensional scene modeling method based on a webGPU, and relates to the technical field of three-dimensional scene modeling and graphic computing, multi-source sensing data are asynchronously sampled from sparse point cloud, a video texture sequence, a scene semantic tag graph and a structure boundary tuple, and a six-dimensional structure unit group is generated through a normalization operator; constructing a structural unit atlas with nodes representing component entities and edges representing constraint relations, and introducing a tension balance mechanism and multi-scale constraints to generate a modeling path prior model; constructing a graph calculation and graph rendering dual-channel assembly line in the WebGPU, and executing parallel texture mapping and boundary fitting operation; a dynamic sensing module is used for capturing scene disturbance and driving atlas response, and incremental reconstruction of the model is achieved; and finally, mapping the model to a Web terminal, and supporting microscopic semantic query and multi-layer data linkage. According to the method, the response speed, semantic consistency and structural adaptability of three-dimensional modeling in a complex environment are improved.

The invention discloses a conference memory enhancement method and device for a conference tablet and a medium, and relates to the technical field of intelligent conference equipment, and the method comprises the steps: collecting multi-modal data in real time based on a preset data collection technology, and coding the multi-modal data into a space-time event stream according to the time sequence of the multi-modal data; identifying handwritten characters on the conference tablet, and extracting a key tag in a speech in combination with a pre-trained natural language processing model to construct a conference knowledge graph corresponding to the current conference; comparing the conference knowledge graph with a historical conference knowledge graph, determining conflict content, and superposing a virtual information layer on a physical interface of the conference tablet; and querying query association information corresponding to the semantic query request based on the conference knowledge graph and the time-space time stream to determine semantic contradictions, and displaying a historical conference evidence chain of the semantic contradictions based on the virtual information layer.

The invention discloses a course data intelligent multi-dimensional retrieval system based on deep learning, and relates to the technical field of course retrieval. The course data retrieval platform is in communication connection with a knowledge graph construction module, a user intention analysis module, a user portrait construction module, a personalized recommendation engine module and a retrieval display module, and all the modules are in electric signal connection; the knowledge graph construction module is used for collecting course data from multiple sources and constructing a course domain knowledge graph. According to the natural language processing technology based on deep learning, deep semantics queried by a user can be deeply analyzed, fuzzy or implicit requirements can be understood, limitation of traditional keyword matching is broken through, intent features and entity relationships can be extracted by combining a knowledge graph, specific entities and relationships queried by the user can be accurately mapped, and user experience is improved. Therefore, a more accurate retrieval result is provided, and personalized requirements of users are met.

The invention discloses an information retrieval system and method based on semantic normalization, and relates to the technical field of artificial intelligence information, and the method comprises the steps: collecting a semantic query record input by a user, carrying out the preliminary semantic analysis, and generating structured data; on the basis of the structured data, entity disambiguation is carried out by utilizing a knowledge graph, abstract classes are generated through a neural network, calibration and dynamic weight adjustment are carried out, and high-confidence entity abstract classes and confidence scores are generated; entity abstract classes and confidence scores are combined with user contexts, an action-value function is calculated through a value network, and an optimal action is selected by utilizing a-greedy algorithm; executing semantic normalization mapping according to the optimal action, and obtaining an intermediate expression by using a meta-symbol dynamic generator; and performing index retrieval and multi-dimensional sorting based on the intermediate expression to generate a sorted retrieval result list. According to the method, the semantic fragmentation problem of multi-modal query is solved, and deep semantic alignment and dynamic weight calibration of heterogeneous data are realized.

The invention provides a knowledge graph construction and semantic query control method and system of a BIM (Building Information Modeling). Comprising the following steps of BIM model data preparation and attribute extension, IFC data analysis and knowledge graph modeling rule definition, knowledge graph automatic construction tool development, Neo4j graph database storage and management, natural language processing and large model semantic understanding, Cyber query statement generation and IOT interface calling, and query and control of an interaction process. According to the knowledge graph construction and semantic query control method for the BIM model, the BIM model is converted into the knowledge graph, and unified query and control from building space information to semantic level are realized by utilizing a natural language processing technology and large model capability.

The invention discloses a knowledge base management system and an enhancement method, and belongs to the technical field of data processing, and the method specifically comprises the steps: obtaining user query information containing a plurality of semantic elements, and determining an IT service scene according to the semantic elements and a pre-constructed IT service knowledge graph; dividing query information into a high-priority semantic information area and a low-priority semantic information area according to the dimension of the knowledge base; core knowledge is quickly positioned based on the high-priority semantic information region, and a retrieval result is perfected in combination with the low-priority semantic information region; if the semantic element has multiple semantics, triggering multi-semantic query; according to the method, the accuracy and efficiency of IT service knowledge retrieval and semantic understanding are effectively improved, and the application value of the knowledge base is enhanced.

The invention belongs to the technical field of artificial intelligence and information retrieval, and provides a multi-modal retrieval method and system based on a lightweight knowledge graph and an index table, and the method comprises the steps: obtaining multi-modal source data, and extracting a structured semantic tag set; constructing a lightweight knowledge graph and a metadata index table; analyzing a natural language query input by a user to obtain a semantic query vector and a keyword set, executing semantic retrieval in the knowledge graph to obtain a text candidate result, and executing keyword matching in the metadata index table to obtain a non-text candidate result; for each non-text meta record, fusing the cross-modal similarity between the non-text meta record and the text candidate result, performing index matching on an original score and a graph semantic evidence score, calculating a comprehensive correlation score, and performing reordering; and generating a natural language answer containing a non-text record link according to a reordering result. The method is suitable for efficient cross-modal knowledge retrieval in a high-security and low-resource scene.

The embodiment of the invention discloses a natural language query method and device. According to the embodiment of the invention, after the natural language query statement is received, the natural language query statement is input into the pre-trained large-scale text embedding model capable of better capturing the semantic and context relationship, and the corresponding intention semantic query vector is obtained; the method comprises the steps that firstly, an intention semantic query vector is obtained, then multiple intention semantic vectors are recalled from a preset intention semantic vector database according to the intention semantic query vector, the similarity between the intention semantic query vector and all the intention semantic vectors is determined to determine a target intention type, and finally feedback information is determined according to a natural language query statement and the target intention type. According to the embodiment of the invention, a large-scale text embedding model is used, the context information can be incorporated into the intention recognition process, and the accuracy of the target intention type is improved. The feedback information is determined according to the natural language query statement and the target intention type, and the accuracy of the feedback information can be improved. And meanwhile, the security of internal data can be ensured without interaction with external data.

The invention discloses a water conservancy intelligent question-answering system and method based on knowledge enhancement and data driving, and aims at water conservancy business structured and unstructured data query, through loop optimization driven by positive and negative examples, precise classification of secondary intentions of water conservancy problems is realized, knowledge questions and answers, data query and professional water conservancy subclass questions and answers can be distinguished, and the system and the method can be applied to water conservancy business. And the semantic analysis efficiency is improved. Aiming at the problem of low query accuracy of retrieval enhancement generation in the field of water conservancy, a differential water conservancy knowledge base oriented to professional books, industrial standards and laws and regulations is constructed, local and networking information is processed through a multi-source knowledge fusion and conflict resolution mechanism, and the accuracy, interpretability and traceability of question and answer content are improved. Aiming at the problems of complex operation and low semantic query accuracy in query of massive water conservancy business data and monitoring data, multi-layer constraint Text-to-SQL conversion is performed based on water conservancy business knowledge, high-precision semantic query and automatic visual output of water conservancy structured data are realized, and query efficiency is improved.

The invention discloses a large language model semantic query acceleration method based on a sparse KV Cache index. The method comprises a KV Cache semantic pruning strategy based on an attention mechanism and a set of asynchronous pipeline reasoning architecture with overlapped calculation and I / O. According to the method, the attention sparsity characteristic of the large language model in the reasoning stage and the asynchronous transmission capacity between the Host memory and the GPU video memory are fully utilized, calculation redundancy and video memory occupation in repetitive semantic query are greatly reduced, and high-throughput, low-delay and high-performance batch semantic data processing service is provided. Through a mechanism for mapping a static text into a compressed semantic index and combining a prefix cache technology in a reasoning process to realize state multiplexing, high-performance reasoning acceleration and high-efficiency storage compression are provided for a data-intensive semantic analysis task in a resource-constrained environment.

The invention discloses a detection system for natural land resources, and relates to the technical field of resource detection, the detection system comprises a data preprocessing module, a fusion space-time diagram construction module, a multi-target enhancement module and a feedback module, an entropy law is combined with a semantic space-time diagram index, quantitative modeling of an ecological law is realized, and the real-time performance of the system is improved. Natural-human collaborative intervention simulation under multiple time scales, time-space semantic query response time improvement, prediction precision improvement and interdisciplinary fusion are supported, the entropy law of ecological economics is combined with time-space diagram index, the limitation that a traditional model only pays attention to physical indexes is broken through, ecological-economic balance is achieved through an entropy weight method and Monte Carlo simulation, and the prediction precision is improved. According to the method, self-adaptive decision making is supported, a perception-decision-execution integrated platform is constructed based on the real-time updating capability of space-time diagram indexes, a full-chain solution from data collection to dynamic optimization can be provided for natural resource intelligent management, a prediction model is combined with entropy increase constraint, and the ecological economic cost is quantified.

The invention discloses an operation and maintenance fault processing method and device based on a large model and a knowledge graph, equipment and a medium, and relates to the field of operation and maintenance fault diagnosis processing, and the method comprises the steps: constructing an operation and maintenance fault processing domain ontology, and inputting historical operation and maintenance fault information and the operation and maintenance fault processing domain ontology into a preset large model to obtain a target triple, obtaining an operation and maintenance fault processing knowledge graph based on the target triple; performing dependency syntactic analysis on the historical operation and maintenance fault information to obtain a dependency syntactic tree, processing the dependency syntactic tree based on the operation and maintenance fault processing knowledge graph to obtain a first dependency syntactic tree, and obtaining a fault processing question and answer template library by using the first dependency syntactic tree; determining a second dependency syntax tree based on the operation and maintenance fault information to be processed, determining a question and answer template corresponding to the second dependency syntax tree from a question and answer template library, and instantiating the question and answer template to obtain a semantic query graph; and determining a target sub-graph corresponding to the semantic query graph from the knowledge graph, and obtaining an operation and maintenance fault processing result based on the target sub-graph.

The invention discloses a CAD drawing instant positioning method and device based on spatial index analysis, and relates to the related technical field of CAD drawing positioning, and the method comprises the steps: carrying out the multi-stage splitting identification of a multi-version target CAD drawing; performing associated indexing on the drawing version information, the drawing layer information and the drawing component set; semantic analysis and spatial association are carried out on the drawing text annotations; embedding and associating the drawing component space semantic tag set into a component-layer-drawing three-level association graph; performing matching indexing on the drawing semantic query request based on the drawing spatial index mapping table; and inquiring a topology path based on the component to carry out instant positioning and visual feedback. The technical problems that in the prior art, complex spatial relations and semantic information in the drawings are difficult to effectively process, and multi-version CAD drawings are low in retrieval efficiency, insufficient in instantaneity and inaccurate in positioning are solved, and the technical effect of improving the retrieval efficiency, query instantaneity and positioning accuracy of the multi-version drawings is achieved.

The invention relates to the technical field of mine environment risks, and discloses a mine environment risk multi-modal analysis and early warning decision-making method, which comprises the following steps of: performing time and space reference alignment on multi-source observation data, constructing a unified spatio-temporal data set, and establishing a troposphere disturbance model to realize observation disturbance coupling. And constructing a physical evolution operator and a rainfall-driven external input operator based on a seepage mechanical relationship, and generating a state evolution mechanism. Semantic direction parameters are generated by mapping semantic query information to a target attention dimension, and a lagging propagation operator is constructed by setting a rainstorm ending moment as a starting point. And determining a non-regular spectrum transition index and a peak lag moment through the non-regularity measurement and the dynamic change rate of each lag propagation operator, and setting a reference threshold value based on the spectrum transition index of a historical non-risk time period. Finally, when the early warning intensity meets the condition, early warning is output, and a query result is generated in combination with the lagging propagation operator and the semantic direction parameters.

The invention discloses a meteorological intelligent question-answering system and method based on a domain knowledge graph and dynamic optimization, the system comprises a multi-source fusion training module, a meteorological knowledge graph and other modules, and the term analysis accuracy is improved through multi-source data training, knowledge graph supporting and dynamic optimization; according to the method, questions and answers are realized through multi-modal input analysis, meteorological element analysis and data feedback optimization. Compared with a traditional NLP model, the meteorological term analysis accuracy is improved from 68.2% to 92.7%, the composite query understanding bottleneck is broken through, three-layer nested semantic query is supported, the model new term adaptation period is shortened to be within 2 hours, the timeliness is improved by 10 times, meteorological element correlation analysis is achieved, and powerful support is provided for disaster reduction decision making.

The invention belongs to the crossing field of traffic engineering and information technology, and discloses a highway continuous longitudinal slope section traffic safety knowledge graph construction method. According to the method, road section features, traffic flow, historical accidents and environmental data are integrated, and a multi-source data set is constructed through cleaning and standardization processing; defining a road section, an accident, an environment and a vehicle entity, and constructing a multi-level knowledge graph comprising a static topology layer, a dynamic attribute layer and a probabilistic reasoning layer; knowledge triples are extracted by using a natural language processing technology, and real-time risk assessment and early warning are realized by dynamically fusing association logic of gradient, environment and accident data in combination with a rule engine; a graph database is adopted to store the graph, and semantic query and dynamic updating are supported. The method breaks through the limitation of traditional single data source analysis, solves the problem of coupling risk assessment of dynamic environmental factors and static features under complex terrains through multi-source fusion and probabilistic reasoning mechanisms, and remarkably improves the traffic safety risk prediction precision.

The invention provides a service recommendation decision-making method based on large model retrieval enhancement generation and related equipment. The method comprises the following steps: acquiring an unstructured service demand; calling a large language model to perform semantic analysis and extension on the service demand, and generating an unstructured extended semantic text; encoding the extended semantic text to obtain a semantic query vector; querying a service description of each pre-stored service from a service database, respectively calculating a first similarity score between the semantic query vector and the service description, expanding a second similarity score between each keyword in the semantic text and the service description, and screening out a candidate service set having semantic association with the service demand; all corresponding application programming interfaces in the candidate service set are determined, and a final application programming interface set is screened out and recommended according to the use frequency of the various application programming interfaces, the semantic similarity among the labels and the semantic query vectors. According to the method and the device, the API recommendation accuracy is improved.

The invention discloses an open vocabulary three-dimensional scene semantic query method based on a hierarchical semantic field, and the method comprises the steps: extracting instance masks of a sub-part, a part and an overall hierarchy through a zero sample segmentation model SAM, and generating a pixel-level multi-granularity semantic feature map in combination with a CLIP encoder; a neural radiation field architecture is improved, a multi-resolution hash grid and a multi-head multi-layer perceptron network are designed, efficient mapping from three-dimensional space coordinates to multi-level semantic features is realized, and region contrast loss and feature consistency loss are introduced to constrain cross-view semantic consistency; in the reasoning stage, the optimal hierarchy is adaptively selected by calculating the similarity between the text features and the rendering semantic features, and a high-precision semantic segmentation result is generated. According to the method, the problem of feature blurring caused by the fact that a traditional method depends on image block cutting is solved, the problems of low semantic query precision, insufficient multi-granularity understanding and cross-view conflicts in an open vocabulary scene are solved, and a natural language driven solution with fine granularity and high robustness is provided for three-dimensional scene interaction.

The invention discloses a video semantic question-answering system for a monitoring scene, and the system comprises a video collection module which is composed of a plurality of cameras and forms a monitoring network; the video preprocessing module is used for optimizing original video data through three-stage processing of key frame extraction, noise reduction and time alignment; the multi-modal feature extraction module comprises a video feature extraction channel and a text feature extraction channel; the multi-modal interactive reasoning module adopts a cross-modal neural network model; the answer generation module is used for outputting corresponding structured answer branches and natural language branches based on joint representation prediction answers; and an interaction and visualization interface module. The invention provides a video semantic question-answering system oriented to a monitoring scene, which supports a user to carry out semantic query on monitoring video contents in a natural language form, and the system automatically analyzes problems, analyzes video data and generates structured or natural language answers. And the combined understanding and response capability of open semantics, dynamic behaviors and attribute states is realized.

The invention relates to the technical field of engineering management, and particularly discloses a building engineering management system and a management method thereof, and the method comprises the steps: carrying out the semantic analysis of electronic data of a target labor service worker through employing a data processing technology based on deep learning, so as to extract the identity information feature of the labor service worker, and meanwhile, in combination with pre-formulated construction requirement rules, semantic query response coding is performed on the identity information of the labor service personnel and a large number of construction requirement rules, so that a potential matching relationship between the labor service personnel and the construction project is mined, and intelligent assessment of the adaptability of the labor service personnel is realized. According to the method, whether the labor service personnel meet the specific requirements of the construction project or not can be evaluated more comprehensively and accurately, and the construction problem caused by insufficient skills or lack of experience of the labor service personnel is effectively avoided.

The invention relates to the field of intelligent evaluation, and particularly discloses an urban old city community toughness evaluation method and system, and the method comprises the steps: firstly obtaining the multi-modal data of the toughness of a to-be-evaluated old city community, extracting the multi-modal characterization characteristics of the data of the toughness of the to-be-evaluated old city community based on a deep learning technology, and meanwhile, carrying out the evaluation of the toughness of the to-be-evaluated old city community; the method comprises the following steps: firstly, extracting a preset number of multi-modal characterization characteristics of evaluated old city communities and corresponding toughness level labels from a background toughness database, so as to construct an association representation of the toughness data characteristics of the evaluated old city communities and an evaluation result; through rapid scanning semantic query of toughness data characteristics of the old city community to be evaluated and the evaluated old city community, toughness grade evaluation of the old city community to be evaluated is realized. Thus, through effective utilization of historical evaluation cases, the problem of insufficient similarity and difference analysis among different communities can be effectively solved, confusion among categories is reduced, and the consistency and accuracy of evaluation results are improved.

The invention discloses an intelligent decision data inquiry system and method based on a generative large model, and belongs to the technical field of artificial intelligence. Natural language inquiry input by a user is received, and an inquiry intention and a target field are extracted; extracting structured and unstructured cost data from the real estate project database, and constructing a candidate field set; performing semantic matching by utilizing the generative large model to generate an intention field mapping matrix; constructing a field dependency graph based on a mapping result, and extracting a minimum closed-loop field set; performing credibility evaluation on the fields, and constructing a weighted field set; generating a dynamic query template, outputting a natural language answer, and attaching a field tracing path; if the field is missing or the credibility is insufficient, automatically generating a reverse query until a credibility requirement is met; according to the method, automatic association and accurate question and answer between complex semantic query and heterogeneous cost data are realized, and the method has good interpretability, traceability and intelligent interaction capability.

The invention discloses a recruitment full-process automatic cooperation method and system based on a large language model, and relates to the technical field of artificial intelligence and human resource management, and the method specifically comprises the following steps: a recruitment platform receives recruitment position information issued by an enterprise recruiter, and generates a standardized recruitment position information description; performing text semantic analysis on the standardized recruitment post information description, and extracting a job key field of a recruitment post; extracting job seeker resume key fields from the job seeker resume; converting the job key field of the recruitment post into a high-dimensional semantic query vector, constructing a resume semantic vector library based on the resume key field of the job seeker, and performing query matching to generate a candidate resume list set; generating an interview comprehensive evaluation report based on the interview data; returning the interview comprehensive evaluation report to a training library of the large language model, and updating parameters of the large language model; and the enterprise recruiter determines an intention job seeker according to the interview comprehensive evaluation report, and generates an in-job notification and an in-job guide process.

The invention is suitable for the technical field of data processing, and provides a database query method, system and device based on a natural language and a medium. Based on the natural language text and the field library, calling a root matching strategy, a semantic matching strategy and a fuzzy matching strategy through a hierarchical collaboration mechanism to perform field mapping, and determining a target field; splicing the target fields to obtain an initial semantic query statement; loading and executing the plurality of modifiers in sequence through a service discovery mechanism to obtain a target semantic query statement; converting the target semantic query statement to obtain a target physical query statement; and executing the target physical query statement to obtain a target query result and returning the target query result to the query party. Through hierarchical cooperation of the root matching strategy, the semantic matching strategy and the fuzzy matching strategy, the expandability and adaptability of the matching strategy are improved, the field recognition accuracy is improved, the field matching range is expanded, and the matching efficiency is optimized.

The invention relates to the technical field of visual information pushing, and particularly discloses a visual information pushing method and system for public health science popularization, which creates a detailed user portrait by analyzing online behavior data of a user. And then, semantic search query is carried out by utilizing the user portrait and each science popularization content in a public health science popularization database, so that public health science popularization semantic query response feature representation meeting user interests and requirements is matched, and a public health science popularization visual expression image is generated based on the public health science popularization semantic query response feature representation and is pushed to a target user object. The personalized public health science popularization visual information pushing method can ensure that the pushed content is not only matched with the interest of the user, but also adapts to the acceptance of the user in cognition degree, so that not only are the correlation and pertinence of information pushing improved, but also the user experience is enhanced, the public health science popularization effect is improved, and the user experience is improved. And improvement of health of the whole people is promoted.

The embodiment of the invention provides an index system creation method and device, equipment, a medium and a program product. The method comprises the steps that if a query request requesting for creating a target index system is detected, a large language model is controlled to convert the query request into a semantic query request for a knowledge graph, and the target index system is an index system used for evaluating a target object; the control chart retrieval enhancement model queries a target community hit by the query request in a target knowledge graph; the target knowledge graph is a knowledge graph created according to a triple extracted from target knowledge data, the triple comprises entities, attributes of the entities and relationships among the entities, the target knowledge data is knowledge data containing a target index, and the target index is an index used for evaluating a target object; and controlling the large language model to generate a target index system according to the searched triple in the target community and the searched abstract of the target community.

The invention belongs to the technical field of artificial intelligence, and discloses a semantic enhancement processing system based on a large model agent RAG database. The objective of the invention is to solve the problem of balance between creative thinking and factual basis retrieval in anti-factual reasoning. An anti-fact hypothesis condition of a user is obtained through a semantic query analysis module, semantic conflict intensity is accurately quantified through a knowledge retrieval and conflict recognition module, and a balance proportion of dynamic trade-off factor control creativity and factuality is constructed. A multilayer semantic alignment network is introduced to perform semantic reconstruction on fact knowledge, an enhanced knowledge fragment set compatible with anti-fact hypothesis is generated, and a reliable anti-fact reasoning path is constructed. And meanwhile, a feedback optimization mechanism is also provided, so that continuous learning and improvement can be realized from user interaction, and adaptive adjustment of parameters is realized. According to the method, the reasoning quality of a large model is improved, and high-quality contents which conform to the anti-fact premise and keep basic reasonability are generated.

The invention relates to the technical field of open source intelligence analysis, and particularly discloses an open source intelligence analysis system and method driven by a large language model, and semantic ambiguity and noise are eliminated through collection of public information data, convergence of multi-source heterogeneous original data and data cleaning and preprocessing. Based on credible sources such as an authoritative database and patent literature, a structured domain knowledge graph is constructed as a fact benchmark. The preprocessed data are input into an information encoder based on a large language model, and a preliminary intelligence assertion is generated; then, extracting a preliminary intelligence assertion and an associated knowledge graph, dynamically checking the consistency and factuality of the preliminary intelligence assertion and the associated knowledge graph based on a semantic query type self-attention aggregation mechanism, and determining the confidence coefficient of the preliminary intelligence assertion; therefore, the confidence degree of the intelligence assertion can be effectively analyzed and judged, a multi-path correction strategy can be triggered for the low-confidence-degree assertion, a closed-loop process of generation-verification-iterative optimization is formed, and the illusion penetration risk is remarkably reduced.

The invention discloses an electronic equipment storage scheduling management method and system, and relates to the field of intelligent management, and the method comprises the steps: collecting equipment electric quantity data in real time, combining with storage rules, task requirements, transportation restrictions and other multi-source constraint conditions, converting heterogeneous information into semantic vectors, and then utilizing an integrated decision-making mechanism of single semantic query to carry out the storage scheduling management of the electronic equipment. And excavating potential association between the current electric quantity state of the equipment and the future demand, predicting the electric quantity gap risk and the charging priority, and generating an optimal charging scheduling scheme based on the available resource state. According to the method, differentiated charging requirements of different devices in various scenes can be actively identified, and the charging resource distribution is optimized while the availability of the devices is ensured. Therefore, operation stagnation caused by low electric quantity is avoided, the influence of unnecessary charging times on the equipment turnover efficiency is reduced, and the compliance requirement of complex safety specifications on the battery state is met at the same time.

The invention discloses a domain generalization semantic segmentation method based on semantic consistency and style diversity. The method comprises the following steps: S1, performing visual and text feature extraction based on a CLIP visual encoder and a text encoder; s2, based on a semantic query intensifier, establishing cross-modal semantic association and aggregating related semantic features by using semantic consistency between image-text modalities to enhance initial object query; s3, guiding the transformation of the low-frequency amplitude spectrum of the image features by using the text embedding difference based on a text-driven style transformation module; s4, strengthening the separation of the features between the fields and the aggregation of the features in the fields by cooperatively weighting the style comparison loss and the style aggregation loss; s5, performing mask prediction, category prediction and query refinement layer by layer by using semantic query based on a mask decoder; according to the method, the optimal performance obviously superior to that of an existing method is achieved on each cross-domain data set, meanwhile, the training overhead of the model is kept low, the reasoning speed is high, and the method has obvious practical value and application prospects.