693 results about "Matching methods" patented technology

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 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 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 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.

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 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 discloses an enterprise and policy service automatic matching method based on machine learning, and the method comprises the steps: carrying out the processing of enterprise multi-source data, constructing an enterprise portrait graph, and generating an enterprise graph embedding and structuring feature set; performing policy text analysis and condition recognition, constructing a policy condition graph and generating condition graph embedding representation; constructing a condition constraint field based on policy conditions, and generating a cross-graph alignment relationship and fusion features; integrating a multi-source feature input improved model to carry out joint modeling, and outputting a basic matching score; constructing a condition boundary manifold, and generating an anti-fact feature sample and a matching elasticity score; and based on the basic and elastic scores, generating a comprehensive score and outputting matching result information. According to the invention, by introducing graph structure perception alignment, conditional constraint guide interaction and a multi-channel scoring aggregation mechanism, high-precision, high-interpretability and intelligent reachability automatic matching between enterprises and policy services is realized.

The invention relates to a multi-dimensional user grouping matching method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring real-time behavior data, consumption data, interest data, historical conversion data and social media data of a user group, and integrating to form a user feature data set; performing multi-dimensional data fusion and cross-dimensional correlation analysis on the data set to generate a user group feature set; the value weight of the user group is updated based on the feature set, grouping levels are divided in combination with service accuracy requirements, and a dynamic grouping mechanism is constructed; and finally, receiving bidding requests of the service providers, constructing a dynamic bidding model in combination with a dynamic grouping mechanism to calculate bidding scores of the service providers, sorting the service providers according to the scores, and generating an accurate matching result with the target user group. According to the method, the limitation of single-dimensional grouping is overcome, the timeliness and flexibility of grouping are improved, and the accuracy and efficiency of matching between the service provider and the target user group are effectively improved.

The invention relates to the technical field of medical information, in particular to an accurate operation name matching method and system based on a semantic matching model, and the method comprises the steps: S1, constructing a training data set, and carrying out the fine tuning of a pre-training language model through employing a comparative learning strategy, and generating the semantic matching model; s2, performing semantic coding on the standard operation name to generate a standard library semantic vector set; s3, in response to the matching request, reading a to-be-matched original operation name, splitting the to-be-matched original operation name into a basic operation name element set by adopting multi-separator regular matching, and cleaning the basic operation name element set; s4, performing semantic coding on the cleaned elements to generate a query vector set; s5, calculating the cosine similarity between the query semantic vector and the standard library semantic vector, and selecting the first K candidates to form a candidate matching set; and S6, performing optimization sorting on the candidate matching set to generate a final matching report. The method solves the problems that a traditional method is insufficient in semantic understanding, inaccurate in splitting and low in efficiency, and is suitable for medical data standardized management, coding association and statistical analysis scenes.

The invention discloses a knowledge point extraction and question knowledge point matching method based on a large language model, and relates to the technical field of natural language processing. Firstly, a knowledge point extraction test set and a candidate baseline large model are constructed, an average recall score is calculated through matching, and an optimal baseline model is selected; a model adaptive to the teaching field is obtained through supervised fine tuning, then knowledge points are disassembled through the model, classification is carried out according to a Bloom learning model so as to clarify a teaching target, and finally the mastering degree of students is analyzed by associating questions with the knowledge points. According to the method, the course analysis efficiency can be improved, and full-automatic or manual intervention flexible analysis is realized; the accuracy and generalization ability of knowledge point extraction and matching are guaranteed, and subjective errors are reduced through semantic matching and evaluation schemes; teaching objectives are defined, and Bloom type labels are marked for knowledge points; a detailed question and knowledge point corresponding relation is constructed, the importance degree of each knowledge point is represented through a weight vector, and support is provided for student portrait and teaching progress analysis and the like.

The invention provides an automatic material identification and matching method and system for a three-dimensional model and a medium, and relates to the technical field of material management, and the method comprises the steps: constructing a standardized material database, and extracting and standardizing material attribute information; analyzing first material description information in the three-dimensional software model by utilizing an SQL (Structured Query Language), and matching the first material description information with the material attribute information to obtain a first material matching result; extracting second material description information in the project material document by using VBA programming, and matching the second material description information with the material attribute information to obtain a second material matching result; and data integration is carried out through PowerBI, and a project material state visual billboard is constructed. The technical problems that in the prior art, a unified standardized material database is lacked, material description formats in different sources are inconsistent, expression is not standard, a construction material list cannot be accurately generated, and informatization and intellectualization of project material management are limited are solved.

The invention belongs to the technical field of commercial entertainment recommendation, and discloses a matching method for recommending online and offline script playing methods based on an AI algorithm, and the method comprises the steps: collecting user immersion experience data, carrying out the multi-dimensional deconstruction of the immersion experience data, and obtaining a user preference pedigree; performing script emotion tension and interaction density analysis on the script content to form a cross-scene experience map; designing a dual-channel sensing network, and performing cross fusion on the user preference pedigree and the cross-scene experience atlas based on the dual-channel sensing network to generate a personalized matching matrix; establishing an immersion degree evaluation parameter set, and performing scene adaptability calculation on the personalized matching matrix to obtain a scene conversion coordination degree index; generating a script recommendation sequence according to the scene conversion coordination degree index, and realizing real-time recommendation adjustment and experience feedback collection through context awareness; the difference between online and offline script experiences is effectively quantified, and a scientific cross-scene matching basis is provided.

The invention relates to the technical field of multi-modal matching, in particular to a multi-modal input instruction and data analysis matching method and system, and the method comprises the following steps: collecting input multi-modal data and instructions, recognizing texts, images and audios, extracting a time sequence, spatial distribution and statistical parameters, and generating a feature distribution result. According to the method, the modal correlation degree is calculated, information entropy measurement is combined, the correlation features are subjected to differentiation processing, the accuracy and consistency of the high correlation features are ensured through space mapping, the potential deviation of the middle correlation features is reduced through alignment adjustment, and the weights of the low correlation features are re-distributed through normalization; according to the method, the overall feature distribution balance and semantic consistency are enhanced, accurate task matching and classification are realized based on the fitting degree score and the time sequence fitting degree, the association accuracy and the time sequence association recognition capability among complex multi-modal data are effectively improved, and the analysis requirement of diversified data input in a complex scene is met.

The invention provides an engineering drawing equipment label matching method and device, electronic equipment and a storage medium, and belongs to the technical field of computer vision and engineering drawing digital processing. The method comprises the steps that multiple pieces of equipment information and multiple text labels in a target engineering drawing are acquired; for each piece of equipment information and each text label, comprehensive score calculation operation is executed to obtain each comprehensive score, and one comprehensive score is a comprehensive score between one piece of equipment information and one text label and is used for representing the matching degree between the equipment information and the text label; constructing a score matrix according to each comprehensive score; and determining a plurality of target matching results according to the scoring matrix and a Hungary algorithm, wherein one target matching result is used for representing whether one piece of equipment information is successfully matched with one text label or not. According to the engineering drawing equipment tag matching method and device, the electronic equipment and the storage medium provided by the invention, the accuracy of matching between the equipment and the text in a complex scene can be improved.

The embodiment of the invention provides a large-model-based natural language query analysis and database field matching method, which comprises the following steps of: performing intention analysis and entity recognition on natural language query of a user based on a large language model (LLM), and extracting a potential keyword set; for each extracted keyword, binding an entity type through an STAM mechanism, and constructing an entity-type mapping relationship; a column description vector library is constructed, efficient retrieval is achieved in combination with an approximate nearest neighbor ANN algorithm, and results of semantic vector matching, editing distance matching and traceability enhancement matching are combined through a multi-strategy fusion matching mechanism to form a final candidate column set; a database metadata interface is connected, information is analyzed, a basic structure is constructed, semantic enhancement field description is generated, and output is organized in a tetrad structure; and reversely deducing the affiliated table through column matching, judging the table structure value by combining the information density in the table and the connectivity with other tables, eliminating redundant tables, and optimizing database representation.

The invention discloses a method and system for processing unstructured monitoring information of an electric power centralized control system, and belongs to the technical field of electric power system automation, the method for processing the unstructured monitoring information of the electric power centralized control system comprises the following steps: inputting the unstructured monitoring information into a BERT embedding layer and an electric power professional vocabulary embedding module, generating two semantic vectors and splicing and outputting a label result of the entity; standardizing a device name in the monitoring information into a standard name in a centralized control system file; associating equipment in the structured monitoring information with equipment in a centralized control system file; and establishing association between the structured monitoring information and the feature tag of the event rule base by adopting a direct matching mode and an indirect matching mode, and analyzing a formula of the event rule base. The monitoring information of the centralized control system is processed through the artificial intelligence technology, the autonomous recognition rate of the event monitoring information can be remarkably improved, the entity extraction accuracy rate can reach 95% or above, and the event matching accuracy rate can reach 90% or above.

The invention relates to the technical field of semantic understanding, in particular to a special knowledge base fragmented slicing and semantic understanding answer matching method, which comprises the following steps of: obtaining document table images, identifying, segmenting and marking as a fragment set, generating a layered weight sequence by combining question comparison condition obligation and consequence aging reasoning, and obtaining a semantic understanding result; the method comprises the following steps of: identifying and segmenting a text to generate a fragment set, analyzing a connection relationship to form a trajectory link, segmenting candidate answers and performing semantic alignment to supplement missing, calculating a score accumulation result according to a weight sequence, adjusting a parameter sequence and priority, and generating and outputting an optimized answer, and comprises the following steps of: identifying and segmenting the text to generate the fragment set, comparing question contents with conditional obligations and combining consequence aging reasoning; a weighted logic chain is formed, a fragment connection relation dominantly constructs a link, candidate answers are segmented, tracks are aligned, a missing part dependency relation is supplemented, semantic precise alignment is performed on the whole, reasoning is coherent and complete, answer coverage is comprehensive, the integrating degree and precision are improved, and effectiveness and integrity in a complex scene are enhanced.

The embodiment of the invention discloses a business receipt and invoice data matching method and system, and the method comprises the following steps: accessing original business receipt data and invoice data from a plurality of heterogeneous data sources through a uniform interface, and carrying out the data cleaning according to preset mapping, merging, splitting and conversion rules, generating a standardized matching flow order; adopting streaming data loading and a Worker parallel computing mechanism to carry out multi-dimensional matching on business receipt and invoice data in the standardized flow bill, and generating a relation matrix representing a corresponding relation between the receipt and the invoice based on a matching result; and automatically checking the relation matrix by calling a rule engine and a machine learning model, marking the matching result of the business document and the invoice which are checked to be abnormal, pushing the matching result to manual auditing, and feeding back the manual auditing result to a knowledge base to optimize the subsequent matching and checking rule. According to the embodiment of the invention, the matching efficiency of the business document and the invoice data and the accuracy of the matching result can be improved.

The invention discloses a collaborative problem traceability and strategy matching method based on entity recognition, and the method comprises the steps: carrying out the semantic expansion of a negative problem text through a T5 generation type pre-training model, and extracting time, a subject, a problem, a strategy and other entities and position information through a BERT-BiLSTM-CRF model; splicing the subject entity, the question and the strategy entity to generate a text with a prefix, correcting by using a BART generation type pre-training model, and classifying and mapping the question to a subsystem for storage through a BERT-DPCNN model; coarse screening is conducted on texts in the subsystem through a regular rule base, vector representation is generated in combination with BERT and RoBERTa models, cosine similarity is calculated, and matching results are determined by synthesizing sentence positions and keyword overlapping degrees; constructing a question-strategy keyword knowledge graph based on a matching result, and inputting the keywords into a DeepSeek-V3 big language model to generate an optimization strategy scheme; according to the method, multiple deep learning models and large language models are fused, so that semantic understanding, entity extraction and accurate matching of negative problem texts are realized, and a final optimization strategy scheme is generated.

The invention discloses a permission matching method and device, and relates to the technical field of computers. The method comprises the following steps: acquiring a to-be-matched path; performing permission rule matching on the to-be-matched path based on a pre-constructed permission rule tree; when the permission rule in the permission rule tree is matched, determining a corresponding rule control permission based on the permission rule, and obtaining a permission matching result of the to-be-matched path; and when the permission rule in the permission rule tree is not matched, determining a preset default control permission as a permission matching result of the to-be-matched path. According to the method, the accurate child nodes, the single-wildcard child nodes and the multi-wildcard child nodes are integrated through the permission rule tree of the single tree structure, common permission matching scenes such as accurate matching, single-segment wildcard matching and cross-multi-segment wildcard matching can be covered without introducing a regular expression, the integrity of the expression ability is guaranteed, the implementation complexity is reduced, and the implementation efficiency is improved. And the system stability is improved. And meanwhile, the problem of matching logic confusion caused by traditional multi-rule scattered storage is avoided.

The invention relates to the technical field of artificial intelligence and human resource management, and discloses an intelligent talent matching method and device based on a dynamic post portrait, equipment and a storage medium, which are used for improving the accuracy of talent matching and the system adaptability. The intelligent talent matching method based on the dynamic post portraits comprises the steps of obtaining post description and candidate skill information, performing semantic enhancement processing on post texts through local and global attention mechanisms, and constructing the dynamic post portraits; performing similarity comparison on the post portraits and candidate skills, and screening out primary candidate subsets; then, precise matching degree prediction is achieved through deep interactive calculation, and a recommendation list is generated; user feedback data and industry trend information are continuously collected, a closed-loop learning mechanism is established, and dynamic adjustment and continuous optimization of post portraits are achieved.

The invention discloses a differential active temperature control matching method and system based on fluid thermosetting coupling analysis. The method comprises the following steps: determining a multi-physical field action form of a machine tool structure; a fluid-heat-structure multi-physics field coupling simulation model is obtained, specifically, coupling simulation of a cooling fluid field, an endogenous heat field and a machine tool structure field is conducted in sequence, and an effective multi-physics field coupling simulation model is obtained; adopting a response surface design method to generate multiple groups of DOE analysis working conditions; a Kriging algorithm is adopted to construct an agent model and establish a mapping relation: based on the DOE sample points and simulation results thereof, the Kriging agent model is trained, and a mapping relation between machine tool detection parameters and active temperature control parameters is established through an automatic refining process; based on the mapping relation, performing multi-objective optimization on temperature control parameters by taking thermal deformation of the key position as a constraint, and realizing differential control strategy matching of each temperature control loop; the problem of thermal deformation caused by thermal characteristic change of a machine tool structure is solved.

The invention relates to the technical field of computer vision and pattern recognition, and discloses a multi-component object matching method and system based on graph matching. The method comprises the following steps: identifying each discrete component and component type label in a to-be-detected object image; forming a hierarchical set structure corresponding to the hierarchical relationship of the preset template; establishing undirected full-connection edges between adjacent levels, and filtering invalid edges to construct a dynamic graph; searching candidate sub-graphs matched with the topological structure of the preset template in the dynamic graph; calculating an affine transformation matrix between each candidate subgraph and a preset template; calculating a matching degree between the remaining candidate sub-images and a preset template, taking a matching result of which the matching degree exceeds a preset threshold value as effective matching, and outputting an object position coordinate and a geometric transformation parameter corresponding to each effective matching; according to the method, deep learning detection and graph matching technologies are fused, and hierarchical dynamic graph construction and backtracking affine two-stage verification are combined, so that the detection rate and the processing efficiency are greatly improved, and the mismatching rate is effectively reduced.

The invention relates to the crossing field of artificial intelligence and big data, and discloses a data matching method based on an AI model and knowledge graph driving, which comprises the following steps: intelligently capturing policy data, collecting enterprise data, and carrying out document processing and structured storage; constructing a policy knowledge graph and realizing enterprise data enhancement, including knowledge base initialization and AI auxiliary modeling; a mixed matching engine and a rule reasoning mechanism are integrated, dynamic and accurate matching of enterprises and policies is achieved, a causal discovery algorithm module is embedded in the mixed matching engine and used for recognizing the causal relationship between policy terms and enterprise behaviors, mismatching caused by false correlation is avoided, and the purposes of improving the intelligent level of policy matching and improving the policy matching efficiency are achieved. Personalized and high-precision policy recommendation support is provided for enterprises, meanwhile, scientificity and effectiveness of government resource allocation are improved, and good practical value and popularization prospects are achieved.

The invention discloses a printing defect dynamic traceability method based on a knowledge graph and a large language model, and the method comprises the specific steps: S1, building a printing field knowledge graph based on printing field multi-source heterogeneous data; s2, receiving defect phenomena and real-time process parameters, mapping the defect phenomena and the real-time process parameters into a graph standard entity and generating a semantic vector, generating labels by the process parameters according to template structuring, and combining the labels into a composite query object; s3, screening Top-K historical cases and extracting a causal chain as a candidate scheme; s4, when the retrieval is insufficient, extracting a path sub-graph, and generating a suspected defect point list through a large model by combining the parameters in S2; and S5, pushing the scheme of S3 or S4 and receiving feedback, if not, pruning, and if not, returning to S4 until the scheme is accepted. According to the printing defect dynamic traceability method based on the knowledge graph and the large language model disclosed by the invention, the problem that a traceability result is inaccurate due to the fact that only the defect phenomenon surface semantic similarity is concerned in a method based on retrieval enhancement generation or case matching in the prior art is solved.

For a cross-modal target prompt matching task, the invention provides a training-free multi-stage matching method based on local feature points and global spatial transformation so as to realize robust positioning of the same target in different modal images. According to the method, firstly, feature points and descriptors of a target prompt image and a to-be-matched image are extracted by utilizing a pre-trained SuperPoint, then, MINIMAGlue finely adjusted on cross-modal data is preferentially adopted to perform feature point matching, and when matching fails, LightGlue is switched to serve as remediation; in the matching process, an optimal angle is selected through a rotation enumeration strategy to enhance matching robustness; in the coarse positioning stage, calculating a minimum enclosing rectangle according to matching points in the ROI, and if the matching points are insufficient, estimating a homography matrix by adopting RANSAC and carrying out geometric correction on a rectangular frame; in the fine positioning stage, the central point of the coarse positioning result is used as a prompt to input MobileSAM for target segmentation, and a final rectangular frame is corrected through area consistency constraint. Through integration and fusion of the multi-stage process, accurate matching and stable positioning of cross-modal target prompt are realized under a training-free condition, and the generalization ability and practicability of the method are improved.

The invention discloses a multi-source data driven intelligent charging stop resource matching method and system and a medium, and relates to the technical field of charging stop resource matching, and the method comprises the steps: respectively executing the multi-source data collection of a user side, a resource side and an environment side, and building a multi-source data set; mapping the multi-source data set to a multi-dimensional tensor space; weighting each dimension in the multi-dimensional tensor space through a space-time dynamic coding mechanism to form a dynamic tensor representation reflecting a real-time state and a future prediction state; constructing a user intention-resource state coupling tensor; calculating a dynamic matching potential index; and establishing an intelligent matching result. According to the method and the device, the technical problems of insufficient matching accuracy and low efficiency caused by insufficient fusion of multi-source data in charging stop resource matching and lack of dynamic adaptation and space-time scheduling consideration in the prior art are solved, and the technical effect of improving the matching accuracy and efficiency of the charging stop resource and the user demand is achieved.

The invention discloses a translation memory bank retrieval and matching method based on semantic similarity, which relates to the technical field of computer-aided translation, and comprises the following steps: generating an internal edge and a cross-graph edge, calculating a one-time jump success probability, calculating edge and cross-graph edge cost based on cleanliness, correcting by using the one-time jump success probability to obtain an edge weight; generating a weighted graph, constructing an initial priority queue, searching by using a shortest path to obtain a distance matrix, calculating density based on a fuzzy similarity matrix, screening the density to obtain a genealogy center, calculating proximity, and generating candidate translations in combination with the distance matrix; static and dynamic fragment sets are constructed through a dependency syntax and BERT-NER, semantic matching precision and noise robustness are improved, a weighted graph is constructed in combination with cleanliness and transfer factors, shortest path matching is carried out, and the retrieval recall rate and quality of technical translation are improved.

The invention discloses a track matching method and device, equipment and a storage medium, and relates to the technical field of target tracking, and the method comprises the steps: obtaining a target video frame; a trajectory prediction frame is calculated through a trajectory prediction model, the trajectory prediction model is generated by a historical observation value, a velocity quantity, an acceleration quantity and a uniform variable motion model, and the velocity quantity and the acceleration quantity are updated in real time when the trajectory state is updated; inputting the target video frame into a detection model to obtain a detection frame; constructing a cost matrix based on the detection frame and the trajectory prediction frame; and matching the detection frame and the trajectory prediction frame in the cost matrix to obtain a matching result, so that the continuity and consistency of trajectory tracking can be improved, and the robustness of a trajectory algorithm is improved.

The invention provides a real-time feature point extraction and matching method based on star operation and GateMLP, and the method comprises the following steps: S1, carrying out the preprocessing of an input image, and sending the image into a lightweight convolutional network to extract an initial feature; s2, on the basis of the output feature map, deep feature modeling is carried out by fusing the structure of a convolution branch and a star operation module, and a dense descriptor and a feature point thermodynamic diagram are output; s3, non-maximum suppression is performed on the thermodynamic diagram, and key point coordinates are screened in combination with confidence score; s4, performing rough matching on the key points of the left view and the right view by adopting cosine similarity and a mutual nearest neighbor strategy to obtain an initial matching pair; and S5, inputting the paired descriptors obtained by rough matching into the gated multilayer perceptron for fine-grained matching, and outputting accurate matching point pairs. The method provided by the invention realizes end-to-end efficient reasoning while ensuring high extraction and matching precision, has good real-time performance and robustness, and is suitable for unmanned aerial vehicle navigation, SLAM (Simultaneous Localization and Mapping) and other actual scenes needing quick response.