83 results about "Semantic change" patented technology

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

The invention provides a text segmentation method and related equipment. The method comprises the steps of obtaining a to-be-processed text; segmenting the to-be-processed text into ordered statement sequences to obtain an initial statement set of the to-be-processed text; wherein the ordered statement sequence comprises a plurality of statements; the semantic variation, the confusion degree variation and the information entropy variation of a first target text block are calculated when a to-be-decided statement in an initial statement set of the to-be-processed text is added into the first target text block, and the first target text block is a set of multiple statements meeting a merging condition; determining the collaboration degree of the statement to be decided and the first target text block based on the semantic variable quantity, the confusion variable quantity and the information entropy variable quantity; and partitioning the to-be-processed text based on the collaboration degree of the to-be-decided statement and the first target text block to obtain a target partitioned text of the to-be-processed text. The text segmentation quality can be improved.

The invention discloses a remote sensing image semantic change detection method based on semantic enhancement Transform and three-dimensional convolution, and aims to solve the problems of insufficient semantic and change information fusion and difficult narrow and long ground feature feature extraction in the existing method. According to the method, an SET-3DC network is constructed, 'feature extraction-feature interaction-semantic enhancement-change information fusion 'is taken as a core link, multi-scale dual-time-phase features are extracted through a dual-branch feature extractor (DFE), cross-branch feature interaction is realized through a channel feature exchange module (CFE), and multi-scale dual-time-phase features are extracted through a multi-scale dual-time-phase feature extraction module. A semantic branch decoder (SEAA-SD) based on semantic enhancement and axial attention strengthens local details, global semantics and narrow and long ground feature features, a change information extraction module (CIE) based on three-dimensional convolution fuses multi-source change information and aligns time-space correlation, and a network is optimized in combination with a joint loss function. Through multi-module cooperation and innovative structural design, the precision and internal consistency of semantic segmentation and change detection are improved, and the method is suitable for a high-precision remote sensing image semantic change detection scene.

The invention relates to a business data processing method and system, equipment and a medium. The method comprises the following steps: preprocessing multi-source heterogeneous cross-domain economic data to generate a standardized data stream; semantic drift in the standardized data flow is detected in real time, and a detection result is generated; semantic alignment judgment is carried out based on the detection result, and a dynamic alignment signal is generated; performing incremental training of a mapping model by using the dynamic alignment signal to generate an updated cross-domain mapping model; and finally, performing mapping conversion on the data stream based on the updating model, and outputting service data with unified semantics. By adopting the method, the semantic change in the economic data can be responded in real time, the problems of semantic drift detection lag, long model updating period and high maintenance cost in the traditional technology are effectively solved, and the accuracy and timeliness of cross-domain economic data processing are remarkably improved.

The invention relates to the technical field of intelligent questioning and answering, in particular to an intelligent questioning and answering method based on context semantics and dynamic retrieval, which comprises the following steps of: splicing data to form a complete dialogue text, extracting a dialogue core theme by using a large model in combination with a thinking chain and small sample learning, and extracting the dialogue core theme based on the dialogue text and a current question. Generating a dynamic keyword set in combination with BM25 and BERT MLM, finally splicing the current problem, the keyword set and the core topic into a multi-dimensional semantic representation, and vectorizing the multi-dimensional semantic representation by using a BGE-M3 embedding model to generate an embedded vector; by combining context topic extraction and dynamic keyword generation technologies, the system can capture core intentions and semantic changes in multiple rounds of dialogues in real time, so that knowledge base retrieval does not depend on fixed keyword matching any more, but dynamically adjusts a retrieval strategy according to a dialogue context; therefore, the matching precision of the question and the knowledge base content is remarkably improved.

The invention discloses a remote sensing semantic change detection method, and the specific process is as follows: in a dual-temporal VSS-Mama encoder, executing the four-stage feature extraction of a dual-temporal image pair, and outputting a dual-temporal feature map; cascading the double-time-phase characteristic patterns according to a channel of a change decoder, and respectively placing the double-time-phase characteristic patterns into an STSS module at four stages of the change decoder to carry out learning cross-time-sequence interaction and output a binary change pattern; a parallel bilingual decoder is adopted to execute step-by-step feature up-sampling on the dual-time-phase feature maps and output bilingual change maps; repeating the training until convergence and obtaining an optimal weight file; generating a semantic change graph by using the binary change graph, the bilingual change graph and the optimal weight file; according to the remote sensing semantic change detection method, a twinning network architecture of encoder-change decoder-bilingual semantic decoder is constructed, so that global space-time modeling and local detail enhancement are complementary, and the problems that the remote sensing semantic change detection method is low in long-range dependence modeling efficiency, broken in edge continuity, easy to ignore subtle change, high in reasoning overhead and the like are solved.

The invention discloses a remote sensing image semantic change detection method, which belongs to the field of remote sensing image semantic change detection, and comprises the following steps: carrying out double-temporal feature extraction by using a residual network, sending the double-temporal features into a constructed difference enhancement module to model difference information, and obtaining a change weight of the double-temporal features; the change weight and the double-temporal feature are sent to a feature interaction module, interaction among the three branches is completed, the interacted double-temporal feature and a differential feature rich in semantic information are obtained, a binary change graph is obtained from a segmentation head to a semantic segmentation graph and through a change detection head, the semantic segmentation graph is superposed on the obtained binary change graph, and the binary change graph is obtained. And finally generating a semantic change graph. Compared with other existing semantic change detection methods, the method provided by the invention is more excellent, can reduce leak detection conditions under different spectrums similar in semantics and error detection conditions under the same spectrums change semantics, and also has a good effect in detail and edge detection.

The invention provides a sensitive data discovery method based on complex semantic analysis, which innovatively introduces high-dimensional sparse Fourier transform (HSFT), maps semantic characteristics to a frequency domain space, captures semantic change frequency characteristics (such as high-frequency mutation characteristics of sensitive information) which are difficult to recognize by traditional deep learning, and improves the recognition accuracy of the sensitive data. Sensitive information and non-sensitive information in similar contexts are effectively distinguished, and the problems of high misjudgment rate, lack of dynamic adaptability and unreliable risk assessment in complex contexts in the prior art are solved.

The invention belongs to the technical field of remote sensing information extraction, and particularly relates to a remote sensing semantic change detection method based on space-time semantic feature fusion. Aiming at the problems of insufficient semantic extraction and inconsistent change characteristics in the existing semantic change detection, the invention provides a semantic enhancement and change consistency network, the semantic extraction capability under a complex ground feature category is improved by introducing a multi-scale adaptive module, and the high-efficiency zero-sample characteristic of SegmentAnything Model 2 (SAM2) is combined, so that the semantic change detection efficiency is improved. And a semantic alignment module is designed to enhance the consistency of change information. Finally, from the perspective of perception-analysis-extraction, the semantic change detection of the high-resolution remote sensing image is realized, the accuracy and practicability of remote sensing semantic change detection are further improved, and the method has important research value.

The invention discloses an engineering construction site real-time three-dimensional modeling method based on an unmanned aerial vehicle aerial image, and relates to the technical field of computer vision, and the method comprises the steps: collecting inclined image data and high-frequency pose data, obtaining sparse key point features, depth semantic features and high-frequency time sequence features, generating a three-dimensional space point cloud model, and recognizing a low-confidence region. And comparing the three-dimensional space point cloud model with the semantic tag with a dynamic semantic three-dimensional model of a previous period as a reference model to identify a semantic change area and semantic state conversion conforming to preset construction logic, constructing a dynamic semantic three-dimensional model of a current period, and generating an unmanned aerial vehicle flight parameter adjustment instruction. According to the method, the three-dimensional modeling quality is improved through cross-view semantic consistency verification, progress changes are interpreted based on construction logic, a building information model and time sequence prediction are combined, and high-confidence data support is provided for construction site management by solving a multi-objective optimization function and deciding unmanned aerial vehicle collection operation.

The invention relates to the technical field of image processing and computer vision, in particular to a remote sensing image semantic change detection method based on comparative learning of pseudo tag guidance, which comprises the following steps: constructing and training a remote sensing image semantic change detection model, and inputting an image to be processed into the trained remote sensing image semantic change detection model to obtain a detection result; the remote sensing image semantic change detection model comprises a double-branch encoder module, a semantic consistency feature enhancement module, a double-branch semantic decoder module, a binary change classifier, a category condition false label generation module and a cross-time phase comparison learning module. According to the method, the precision of semantic change detection is improved, and the dependence on large-scale manual annotation data is reduced.

The invention provides a semantic communication method and system based on multi-modal perception, and the method comprises the steps: obtaining multi-modal information, carrying out the semantic extraction of the multi-modal information, and generating a corresponding current semantic representation; performing semantic association on the current semantic representation and historical semantic information to obtain context enhanced semantic information; determining a semantic difference degree or a semantic change trend between the current semantic representation and historical semantic information based on the context enhanced semantic information to generate a key frame semantic description; semantic processing and semantic completion are carried out on the semantic description of the key frame; and according to a set context constraint condition, performing conditional semantic restoration on the complemented semantic description of the key frame by using a multi-modal model, and outputting auxiliary decision information or prompt information based on a restoration result. According to the method, the multi-modal data transmission bandwidth requirement is reduced, and meanwhile, the robustness and continuity of semantic communication in a complex channel environment are improved.

The invention relates to the technical field of computer vision, in particular to a processing method and system based on a computer vision large model, and the method comprises the following steps: obtaining the alignment position of a continuous response region in a feature map, screening a consistent change region, analyzing semantic nodes, connecting a mapping path to an original map, extracting coordinate analysis direction grouping nodes, and tracking semantic change to mark a jump position, extracting feature analysis to activate a difference to delimit a mutation fragment, and obtaining a semantic response structure chart. According to the method, continuous regions with consistent directions are screened through position alignment of multilayer channel response regions, spatial focusing of semantic regions is carried out, path clues with continuous semantic relationships are screened through extraction of connection directions and sequence information of nodes in an output sequence, and path segments with a propelling trend are classified through analysis of node coordinate arrangement. And by tracking node semantic value changes, marking jump node positions, and dividing mutation fragments through response differences, the layering and distinguishing capability of the semantic structure is enhanced.

The invention relates to a semantic change detection method based on text assistance and comparative learning. The method comprises the following steps: 1, acquiring remote sensing images acquired in different time phases in the same area, and inputting a twin multi-scale encoder with shared parameters to extract multi-stage features; 2, a pre-training vision-language model and a text encoder are introduced in the training stage, a text describing changes is generated, high-level visual features are injected, migration features are obtained, and reconstruction constraints based on comparative learning are constructed; 3, constructing a context and channel perception fusion module to carry out adaptive fusion on the multi-scale features; 4, designing a multi-scale decoder to recover the spatial resolution and outputting a semantic change graph; and 5, performing end-to-end optimization on the network by adopting a joint training target consisting of spatial mask supervision loss and reconstruction loss. The method is suitable for scenes such as remote sensing monitoring, disaster assessment and urban dynamic analysis.

The present application belongs to the technical field of remote sensing information extraction, and particularly relates to a remote sensing semantic change detection method based on multi-feature fusion. The present application proposes a new type of remote sensing semantic change detection twin convolution network; firstly, in order to enhance the evaluation and perception ability related to the differences between classes and classes, a difference feature enhancement module is introduced. The module comprehensively captures features from the time dimension. Then, in order to solve the mutual relationship between multi-temporal and multi-level features, the feature selection interaction module and the module are studied, realizing the multi-dimensional deep fusion and interaction of the change features. This enhances the information transmission and integration ability between the features in the multi-temporal remote sensing image. Finally, from the perspective of "perception-analysis-extraction", the semantic change detection of high-resolution remote sensing image is realized, which further improves the accuracy and practicability of remote sensing semantic change detection, and has important research value.

The invention discloses a software testing method and system based on a data analysis platform, and particularly relates to the technical field of software testing, and the method comprises the following steps: tracking business semantic change by constructing a semantic evolution perception map, and establishing a semantic-driven data label linkage mechanism to maintain the consistency of training samples of a defect recognition model; semantic consistency drifting is monitored, model output is controlled, a map-enhanced test case generation mechanism is introduced to achieve semantic synchronous generation, meanwhile, a semantic regression verification process is set to ensure alignment of a test case and evolution semantics, and a closed-loop test process covering a full path is formed; according to the method, semantic-driven label linkage and model synchronous updating are realized by constructing the semantic evolution perception map, and a map-enhanced test case generation and regression verification process is introduced, so that the test process is ensured to be consistent with the service semantic dynamic state, the defect identification accuracy and the test coverage integrity are improved, and the test efficiency is improved. The method is suitable for software quality guarantee in a continuous delivery environment.

The application provides a sensitive data discovery method based on complex semantic analysis, which innovatively introduces high-dimensional sparse Fourier transform (HSFT), maps semantic features to a frequency domain space, captures semantic change frequency characteristics (such as high-frequency mutation characteristics of sensitive information) that are difficult to identify by traditional deep learning, effectively distinguishes sensitive and non-sensitive information in similar contexts, and solves the problems of high misjudgment rate, lack of dynamic adaptability and unreliable risk assessment in the prior art in complex contexts.

The application relates to the technical field of semantic analysis, in particular to an AI intelligent agent question and answer text generation method based on semantic analysis, which comprises the following steps: acquiring a question keyword set and a question and answer keyword set of each question pair; calculating a semantic change vector of each keyword, and then acquiring a semantic abnormality deviation of each keyword in the question keyword set of each question pair; combining the differences between the semantic abnormality deviations of different keywords in the question keyword set to obtain the semantic ambiguity of the question text in each question pair, so as to calculate the question and answer matching degree of each question pair; and utilizing the question and answer matching degree to eliminate the question pairs in the historical question and answer records to construct a FQA corpus, and combining the AI intelligent agent to generate corresponding question and answer texts according to the questions of users. The application can improve the accuracy of AI intelligent agent question and answer text generation.

The invention discloses a sequence recommendation method based on attribute semantic evolution modeling, electronic equipment and a storage medium, relates to the technical field of machine learning recommendation systems, and solves the defect of'attribute static hypothesis' caused by neglecting attribute semantic dynamic evolution in existing sequence recommendation. And the problem of semantic drift modeling deficiency caused by lack of a time perception mechanism is solved. According to the technical scheme, the method is characterized by comprising the steps of collecting structured attribute values of articles in a user historical sequence and occurrence timestamps of the structured attribute values, constructing a time sequence of the attribute values and generating semantic embedding of all historical articles; aggregating historical semantic embedding through a mask attention mechanism to form dynamic attribute representation; generating disturbance variants for random masks of the semantic embedding sequence, and enhancing characterization robustness by utilizing contrast learning; and fusing dynamic attribute representation and article original embedding, and inputting a basic sequence model to carry out joint interest modeling and prediction. According to the method, the preference of the user for the attribute semantic change can be accurately captured, and the recommendation accuracy and generalization ability are remarkably improved.

The invention relates to the technical field of remote sensing image change detection, in particular to a remote sensing image semantic change detection method and related equipment, and the core is as follows: firstly, obtaining dual-tense remote sensing image data, and inputting the dual-tense remote sensing image data into a trained semantic change detection model for processing; the model comprises a ResNet34 module, a semantic branch and a change branch. The ResNet34 module is responsible for extracting multi-scale features of the dual-tense image, generating feature maps of different scales and synchronously inputting a semantic branch and a change branch; the semantic branch is in close communication connection with the change branch, and enhanced semantic features are generated by extracting semantic information of the feature map and combining unchanged information of the change branch; and the change branch fuses the double-tense features and performs change region activation processing in combination with semantic information to obtain improved change features, and finally, a semantic change graph is output through the improved change features and semantic features. According to the method, precise detection of the semantic change of the dual-tense remote sensing image is realized through a branch cooperation mechanism.

The present invention belongs to the technical field of financial data processing and discloses a network multi-source financial text big data processing method, comprising collecting multi-source financial text data, performing semantic-level unstructured analysis, separating language critical points and triggering semantic breakpoints in semantic changes, constructing a financial fluctuation language trigger factor matrix, and capturing key expression fragments; receiving the trigger factor matrix, adopting a language expression motivation modeling mechanism, performing structure extraction and configuration mapping on the key expression fragments, constructing a configuration transformation graph with language expression motivation as the axis, and outputting configuration equivalent semantic clusters; performing topological structure analysis based on the configuration equivalent semantic clusters, identifying semantic fission points and mutation connections, determining semantic faults, and performing concept reconstruction on both sides of the fault to generate a concept semantic tree; achieving deep mining and structural modeling of semantic information of financial texts, and having important application value.

The application relates to a semantic change detection method, in particular to a tower base hyperspectral semantic change detection method considering illumination change. The method comprises the following steps: S1, determining a target scene according to the position and light path coverage range of a tower base remote sensing platform, and pre-setting a hyperspectral imager; moving at least three gray scale targets with gradient difference to the target scene; S2, collecting image data of the target scene and all the gray scale targets by using the hyperspectral imager, obtaining the radiance values of all the gray scale targets according to the image data of all the gray scale targets, and performing radiation correction on the target scene by using the radiance values of all the gray scale targets and the known reflectivity values of the gray scale targets, so as to obtain the radiance values of the target scene; and S3, performing semantic change detection analysis by using the radiation-corrected hyperspectral data. The application can realize radiation correction of the target scene and obtain more accurate ground object reflectivity data.

This invention relates to a method for constructing a disaster-bearing entity based on a structure-function-behavior model, comprising the following steps: S1 entity structural semantic modeling; S2 entity functional semantic modeling; S3 behavioral semantic modeling; S4 constructing a semantic fusion module and building urban natural disaster risk factor data and knowledge data, and using the risk factor data and knowledge data to fuse the semantics formed in the models built in steps S1-S3 to realize the construction of the disaster-bearing entity. The method also includes a data and mechanism-driven twin model update method, specifically including: updating the twin model using a change detection model, and update management, wherein the change detection model includes the detection of structural and semantic changes. The method also includes a disaster risk prediction method, which uses a hybrid approach consisting of decision trees and node data formed by urban natural disaster risk factor data and knowledge data. This achieves data analysis and prediction of urban entity disaster situations with visualization of the sum of structure-function-behavior factors.

The invention provides a key frame generation method and device based on a semantic change degree and a semantic communication system, and the method comprises the steps: obtaining multi-modal perception data at a current moment, and carrying out the semantic coding of the multi-modal perception data, so as to generate a current semantic representation; determining a semantic change degree according to the current semantic representation and pre-acquired historical semantic information, wherein the semantic change degree is used for quantifying the change degree of the current semantic state relative to the historical semantic state; and comparing the semantic change degree with a preset change threshold value, judging that the current moment is a key frame according to a comparison result, triggering to generate a key frame semantic description, performing semantic coding based on the generated key frame semantic description, and sending the semantic coding result to a receiving end through a communication channel. According to the method, generation and sending of the semantic description of the key frame can be triggered in a self-adaptive manner, so that the communication overhead is effectively reduced on the premise of ensuring the integrity of the key semantic information, and the stability and the real-time performance of a semantic communication system in a complex environment are improved.

This invention relates to the field of semantic processing technology, specifically to a large-scale adaptive training method and system based on task semantic awareness. The method includes the following steps: acquiring text structure information, extracting batch semantic vectors to determine consistency, tracking semantic differences across steps to form a trajectory, merging stage boundaries, locating abnormal nodes and verifying loss gradients, and mapping parameter tuning instructions into an adaptive training control structure. In this invention, through structured semantic expression and a real-time feedback mechanism, the training process can be dynamically adjusted to adapt to changes in task semantics, avoiding semantic shift and inconsistency issues. Precise labeling of semantic states and monitoring of change trajectories enable the model to adjust training strategies in a timely manner at different semantic stages, thereby improving training efficiency and stability. Accurate location of key nodes allows training to focus on training steps with significant impact, avoiding ineffective training. Through semantic-driven parameter adjustment and training control, the model's adaptability to complex tasks and resource utilization are improved.

The application discloses a medical dialogue understanding and generation method based on a Transformer model, and comprises the following steps: medical text, structured medical record data and historical dialogue content are extracted, and unified semantic representation is generated through embedding mapping; various embedding representations are input into a Perceiver structure containing a cross-attention mechanism to perform cross-source information fusion, and unified latent semantic vectors are obtained; a semantic state evolution path function is constructed based on a neural ordinary differential equation; a dialogue round is introduced as a time variable to model the dynamic evolution process of the semantic change with the round; a context control semantic vector is constructed in combination with the current semantic state and the historical context, and the natural language generation model is guided to generate initial reply text; and finally, the final medical dialogue response text is output through semantic trajectory consistency and medical entity reference consistency verification. The application has strong context modeling capability.

The application discloses a three-dimensional and semantic synchronous change detection method and system, first, a three-dimensional and semantic change detection small sample label data set of multi-modal data is established; then, a positive and negative generator based on a geosciences knowledge graph is designed to generate positive and negative samples for the small sample label data set; then, a semantic detector based on contrast learning is designed, the positive and negative samples generated by the generator are input into a momentum contrast encoder for learning, and then connected to a downstream change detection task (decoder); finally, a loss function of an adversarial network is used to realize three-dimensional change detection in a generative adversarial process of the positive and negative generator and the semantic detector. The self-supervised three-dimensional and semantic synchronous change detection network under the condition of small samples is realized, and the technology and method of multi-modal remote sensing intelligent change interpretation are enriched.

The invention provides a universal remote sensing image change detection method for binary change detection and semantic change detection, and the method comprises the steps: employing a dynamic rare category perception sampling module, evaluating the rare degree of each semantic category, carrying out the weighted sampling of a dual-time-phase image pair, and forming a training set; inputting the double-time-phase image pair into a multi-scale attention enhancement encoder sharing a weight, carrying out feature extraction, and constructing a low-level difference feature and a high-level difference feature; splicing the two to obtain a comprehensive change representation, respectively inputting the comprehensive change representation to a change detection solution head and a contrast learning module, outputting a change detection result by the change detection solution head, and establishing a contrast learning loss by the contrast learning module; based on the semantic change detection loss, the binary change detection loss and the contrast learning loss, establishing an overall loss function for training; and inputting the to-be-detected dual-time-phase image pair into the general change detection model to obtain a corresponding change detection result.

The application provides a lightweight semantic enhancement and change integrated remote sensing image semantic change detection method, comprising collecting double-time-phase remote sensing images and constructing a sample library; a multi-task semantic change detection network model is constructed, and network model training and optimization are performed based on the sample library; a lightweight multi-task weight sharing encoder is used for the encoder part of the multi-task semantic change detection network model, and the multi-task weight sharing encoder simultaneously supports semantic segmentation and binary change detection tasks; the decoder part of the multi-task semantic change detection network model comprises a semantic segmentation decoder corresponding to a first time phase, a semantic segmentation decoder corresponding to a second time phase, and a binary change detection decoder; the images of the first time phase and the second time phase input into the encoder part are respectively processed to generate feature maps with different resolutions, and are transmitted to the decoder part through a jump connection; and the double-time-phase remote sensing images are input into the trained multi-task semantic change detection network model to perform semantic change detection.

The invention discloses a map-image semantic change detection method and system based on multitask-single label learning. According to the method, efficient learning of semantic change detection of a previous land cover map and a newest optical image is realized through semantic change detection and distribution transformation. The method comprises the following steps: firstly, extracting a change region between map data and an optical image and a semantic category of image pixels by using a multi-task model, and then converting a semantic category probability into a binary change probability through a distribution transformation function; through the design, on one hand, the latest and best semantic change detection model can be integrated; and on the other hand, learning of the semantic change detection model can be efficiently driven only by using a binary change tag or a semantic segmentation tag, and finally, high-performance and low-cost semantic change detection is realized. According to the method, modeling of map-image pair change area positioning and pixel category identification can be realized by virtue of an existing optical remote sensing image semantic change detection method based on mature research.