137 results about "Semantic change" patented technology

The invention belongs to the technical field of image processing, and relates to a remote sensing image semantic change detection method and device based on a Mama model, and the method comprises the steps: obtaining paired dual-time remote sensing images, and carrying out the preprocessing of the dual-time remote sensing images; inputting the preprocessed dual-time remote sensing image into a Mama encoder branch for feature extraction to obtain a multi-scale feature; processing the multi-scale features by adopting a difference feature extraction module to obtain multi-scale difference features; inputting the multi-scale features and the multi-scale difference features into a feature fusion enhancement module for fusion to obtain semantic features; respectively decoding the semantic features and the multi-scale difference features by adopting a Mamba decoder branch; inputting the decoded semantic features and the multi-scale difference features into a classifier, and carrying out masking operation to obtain a semantic change graph of the dual-temporal remote sensing image; according to the method, the changed area of the dual-time-phase remote sensing image and the specific change category of the changed area can be accurately detected.

The invention discloses a semi-supervised multi-temporal satellite image time-varying information extraction method, and belongs to the technical field of remote sensing image processing. The semantic change detection performance of the model and the detection precision of complex shape change ground objects are improved. The method comprises the following steps: constructing a semantic change detection model, carrying out full-supervised training on the semantic change detection model by using a binary change detection supervised loss function and a semantic segmentation supervised loss function by using a small amount of labeled dual-temporal remote sensing images to obtain an initial model, and obtaining a semantic change detection prediction result of each pair of images; using a pseudo label optimization strategy to optimize the semantic change detection prediction result of each pair of images; combining the obtained pseudo label data with high confidence and a small amount of labeled dual-temporal remote sensing images into a new training set, using the new training set to perform semi-supervised training on the initial model in a semantic change detection model, and using a consistency regularization combination loss function to perform supervised training to obtain a new model; and until a preset number of iterations is reached.

The invention provides a lightweight semantic enhancement and change integration remote sensing image semantic change detection method. The method comprises the following steps: acquiring a dual-temporal remote sensing image and constructing a sample library; constructing a multi-task semantic change detection network model, and performing network model training optimization based on the sample library; an encoder part of the multi-task semantic change detection network model adopts a lightweight multi-task weight sharing encoder, and supports semantic segmentation and binary change detection tasks at the same time; a 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; an image of a first time phase and an image of a second time phase input into the encoder part are respectively processed to generate feature maps with different resolutions, and the feature maps are transmitted to the decoder part through jump connection; and inputting a dual-temporal remote sensing image to the trained multi-task semantic change detection network model for semantic change detection.

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 Mama-based edge-refined remote sensing image semantic change detection method, and belongs to the technical field of remote sensing image change detection. In order to solve the problem of rough prediction edge caused by insufficient optimization of boundary region details in the feature extraction and fusion process of the existing method, the invention provides the following technical scheme: firstly, extracting multi-level features of a dual-temporal remote sensing image by using a twin Mama encoder backbone network; secondly, cross-time-phase feature interaction and difference feature extraction are carried out through a difference module based on Mamba; then, an edge-refined visual state space decoder is adopted, and expansion and corrosion operation and an attention mechanism are fused to reinforce edge information; meanwhile, the learning ability of the model to edge details is improved by combining a loss function strategy of depth boundary supervision and change region supervision. Experiments are verified based on a SECOND data set, the method is superior to an existing mainstream method in the aspects of precision, intersection-to-union ratio, F1 score and other indexes, the boundary precision and semantic segmentation effect of change detection are remarkably improved, and the method is suitable for urban planning, disaster assessment and other high-precision demand scenes.

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 discloses a multi-task remote sensing semantic change detection method based on a visual state space model. The method mainly solves the problems that in the prior art, semantic segmentation and change detection subtasks lack consistency, and global feature information is not fully utilized. According to the implementation scheme, the method comprises the following steps: introducing a visual state space model into a feature extraction encoder, obtaining multi-level semantic features of image global context information modeling, and enhancing the multi-level semantic features; respectively obtaining a semantic segmentation result and a binary change result of the image from the enhanced multi-level features through a semantic segmentation decoder and a change detection decoder; and in combination with a semantic segmentation result and a binary change result, a semantic change result with higher discrimination is generated, and multi-class semantic change detection of the remote sensing image is realized. According to the method, the specific category information in the change area can be accurately classified while the image change area is efficiently extracted, and the method can be used for land resource utilization management and monitoring of the change of the land type of a specified area.

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 provides a two-time remote sensing image semantic change detection method based on Fourier transform. The method comprises the steps of selecting original remote sensing image data and performing preprocessing; performing feature extraction on the original remote sensing image data to obtain shallow features and deep features; generating binary change features according to the deep features; performing multi-scale feature fusion and decoding on the binary change features to obtain decoded binary change features; performing multi-scale feature fusion and decoding on the deep features and the shallow features to obtain dual-temporal semantic features; semantic change modeling is carried out in the frequency domain and the space domain, and binary change features and dual-temporal semantic features after semantic change modeling are obtained; generating a dual-temporal semantic segmentation graph and a binary change detection graph through classification and up-sampling; obtaining a semantic change graph through mask multiplication; according to the invention, the change position of the dual-time-phase remote sensing image can be accurately detected, and the detail capturing capability of the change area is improved.

The invention relates to the field of artificial intelligence and natural language processing, in particular to a long text semantic classification method based on deep learning. The method comprises the following steps: performing structured cleaning and sentence boundary recognition on an original text to obtain a sentence sequence; based on the sentence sequence, constructing a semantic fragment; performing coding processing on the semantic fragments to generate a segment-level semantic vector sequence; based on the segment-level semantic vector sequence, generating a global semantic representation vector through a semantic tension driven aggregation algorithm; and based on the global semantic representation vector, designing a semantic flow enhanced classifier to complete text classification. The method solves the problems that when a traditional text classification method is used for processing long texts, semantic incoherence and context information loss are prone to occurring, and particularly when structural semantic mutation exists in the texts, a traditional model often cannot accurately capture semantic changes caused by the mutation; a traditional classifier is often prone to over-fitting of majority classes of samples and neglects recognition of minority classes.

The invention discloses a corpus extension method, corpus extension equipment and a storage medium. The corpus extension method comprises the following steps: performing word segmentation on an original corpus to obtain original vocabularies; determining score influence factors matched with the candidate replacement vocabularies based on the word meaning quantity corresponding to the candidate replacement vocabularies and / or the original vocabularies; calculating a replacement recommendation score corresponding to the candidate replacement vocabulary by using the score influence factor, and selecting the candidate replacement vocabulary of which the replacement recommendation score meets a preset condition as a target replacement vocabulary corresponding to the original vocabulary; and replacing the original vocabularies in the original corpus with the target replacement vocabularies corresponding to the original vocabularies to obtain the extended corpus, so that score influence factors matched with different candidate replacement vocabularies can be accurately obtained, the calculation accuracy of replacement recommendation scores is improved, the selection accuracy of the target replacement vocabularies of the original vocabularies is further improved, and the user experience is improved. On the premise of increasing the corpus quantity, the situation that the extended corpus has semantic change relative to the original corpus is reduced, and the quality of the extended corpus is ensured.

The invention discloses a semantic change detection method, system, equipment and medium for a remote sensing image, and relates to the technical field of semantic change detection, and the method comprises the steps: obtaining a plurality of dual-time remote sensing images; constructing a double-path double-branch network structure, and guiding one branch to selectively learn and fuse the feature information of the other branch through the learning of a bidirectional guiding module; the feature information of the double branches in different time periods is kept consistent in channel number and spatial resolution, an interaction strategy is adopted, double-branch network structures with different scales are used for extracting channel attention weights respectively, dynamic feature fusion is carried out based on weight information, and double-time semantic features are obtained; and performing binary change detection on the dual-time semantic features to obtain a binary change detection graph, and obtaining a final result of semantic change detection. According to the method, a two-way guiding strategy is integrated, the network can be effectively guided to be more focused on key change characteristics in a two-time-phase image, and the method also has strong robustness and adaptability under images with different resolutions.

The invention discloses a remote sensing image semantic change detection method based on cross-sample semantic contrast learning. The method comprises the following steps: constructing a twinborn residual network sharing weight; correspondingly cutting an original dual-time remote sensing image into blocks, and respectively inputting the blocks into corresponding branches of the twin residual network; constructing a feature extraction module, and embedding the feature extraction module after the first stage of the twin residual network; constructing a change detection module, and inputting an intermediate feature generated in the third stage of the twin residual network into the change detection module; and constructing a multi-scale feature pyramid fusion module, and inputting the auxiliary fusion feature map, the abstract fusion feature map and the change result feature map into the multi-scale feature pyramid fusion module to obtain a final result of semantic change detection. According to the method, a cross-sample semantic consistency feature extraction module is embedded, so that ground feature consistency information required by semantic change detection can be better learned, and the accuracy and reliability of a semantic change detection result can be 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 relates to the technical field of artificial intelligence and finance, and provides an artificial intelligence-based text wordart method, device, equipment and medium, on one hand, a target text is rasterized based on a text wordart model to obtain a target raster image text, a vector text layer is converted into a pixel layer, and the pixel layer is converted into a pixel layer; the text is not limited by language types, the problem that the Chinese language structure is complex is avoided, and the text-to-graph problem is converted into the graph-to-graph problem; on one hand, a large language model is used for recognizing regional coordinates used for semantization in raster image characters and expanding style cue words, and the controllability degree of text art generation is improved; and on the other hand, the discriminator is utilized to guide the diffusion model to generate a font which can be obviously recognized, and meanwhile, through an adversarial training mechanism, the generated wordart has the advantages of clear font recognition and artistic semantic change, so that the style and the font are highly fused and balanced, and the problems of low efficiency and poor quality of the text wordart are solved.

The invention relates to a remote sensing image semantic change detection method based on difference feature guidance, and the method comprises the steps: obtaining difference features through employing a pixel-level subtraction method, employing a guidance segmentation branch to only focus on the segmentation of a change region, finally generating an accurate land coverage map, and guiding a change branch to generate a clear edge of the change region. According to the method, semantic segmentation is more accurate, and the edge of a change region is clearer.

The invention discloses a remote sensing image semantic change detection method based on high-resolution feature bidirectional fusion, which belongs to the field of remote sensing image semantic change detection, and comprises the following steps: performing multi-scale feature extraction in a high-resolution network, performing multi-scale feature integration by using a bidirectional deep and shallow feature aggregation module, and performing remote sensing image semantic change detection. The method comprises the following steps: acquiring difference characteristics through a high-resolution difference extraction module, refining by using an efficient channel attention module to obtain two semantic segmentation graphs and a binary change graph, and superposing the two semantic segmentation graphs on the binary change graph to finally generate a semantic change graph. Compared with other existing semantic change detection methods, the method provided by the invention is more excellent, especially has better effects in the aspects of identifying the contour of a change area and detecting a subtle change area of a complex scene, can reduce semantic errors caused by visual similarity, and has wider applicability.

A system and method of identifying occurrence of a semantic variation of a phrase in a passage by at least one processor may include calculating a phrase embedding vector, representing a semantic meaning of the phrase; extracting, from a textual representation of the passage, at least one hierarchical set of nested sequences of words; for each sequence, calculating a corresponding sequence embedding vector, representing a semantic meaning of the sequence; for one or more sequence embedding vectors, calculating a corresponding vector similarity value, representing similarity of the sequence embedding vectors to the phrase embedding vector, identifying a sequence corresponding to a maximal vector similarity value of the one or more vector similarity values; and determining the identified sequence as a semantic variation of the phrase, based on the maximal vector similarity value.

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 relates to the technical field of text processing, in particular to an internet-oriented illegal advertisement recognition method, device and system. According to the method, text information data carried by an advertisement is specifically analyzed, the high distribution consistency condition of part-of-speech and word frequency of each segmented word in a local text before and after special symbol processing and the similarity condition of an overall text are compared and analyzed, and the necessary processing degree of a special symbol is obtained in combination with the connection association condition of words before and after the special symbol processing; through the processed text data and the abnormal possibility of the image and the release time, the violation score of the advertisement is evaluated and identified, and a more accurate evaluation result is obtained. According to the method, by comparing semantic change conditions represented by coherent text contents before and after special symbol processing, interference special symbols carried in the advertisement text are processed, the accuracy of advertisement violation information identification is improved, and violation advertisement identification is more comprehensive and reliable.

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 relates to a diversified semantic distribution matching data compression method and system. The method comprises the following steps: pre-training a group of models on an original data set to obtain a pre-trained model; each category in the original data set is modeled as Gaussian distribution by semantic embedding extracted by a pre-training model, a category center represents inherent features of the category, and a covariance matrix represents semantic changes of instances in the category; performing class center alignment and covariance matrix alignment on the synthesized data set and the original data set; optimizing the synthetic data set by using a plurality of pre-training models, and storing a historical class center of the synthetic data set optimized by a previous pre-training model through a memory bank; and aligning the class center of the synthetic data set optimized by the current pre-training model with a historical class center to obtain a semantic diversified synthetic data set. According to the method, a framework based on distribution matching is followed, and the semantic information is fully considered from the perspective of the semantic information, so that the semantic diversity of the compressed synthetic instance is enhanced.

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 present invention relates to the field of natural language processing technology, and in particular to a language translation method, electronic device and system based on big data, including: constructing an initial NMT model and translating a sentence segment to be translated to obtain an initial translation; constructing a network corpus, combining the network corpus to perform semantic analysis on the relationship between the sentence segment to be translated and the word unit in the initial translation in the sentence structure, obtaining the sentence structure parameters of the word unit, and replacing the word unit in the corresponding sentence through the synonyms and near synonyms of the word unit in the network corpus, analyzing the semantic change characteristics before and after the replacement, obtaining the semantic feature value of the word unit, combining the sentence structure parameter of the word unit with the semantic feature value, obtaining the translation degree of the word unit, thereby providing feedback to the NMT algorithm, and iteratively translating the sentence segment to be translated to obtain the final translation. The present invention effectively optimizes the semantic expression of the translation, and improves the accuracy and context adaptability of the translation.

The invention provides a wetland semantic change detection system and method based on frequency domain information and multi-scale feature extraction, the system is a dual-domain adaptive frequency sensing wetland semantic change detection network structure, and the network structure comprises a BSFE module, an MASPP module, an AFSM module and an SCG module which are connected in sequence; the BSFE module adopts an AFT module based on frequency domain information to extract high-level and low-level features of an image, and improves the perception capability of subtle changes through frequency enhancement; the MASPP module enhances the sensing ability of the network under different scales through dense void factor combination and multi-scale pooling operation; the AFSM module enhances a specific frequency component through frequency domain convolution; and the SCG module utilizes time sequence consistency constraint to guide semantic learning, fuses the change features and the spatial-temporal features, and generates a final semantic change graph through mask operation. According to the invention, the precision and efficiency of wetland change detection can be improved, and the calculation overhead is reduced.

The invention relates to the field of text processing, in particular to a text splitting mark generation method and device, a medium and electronic equipment. Comprising the following steps: obtaining summary information of a sub-text corresponding to each sliding window in a to-be-processed text; generating a semantic change amplitude between any two adjacent summary information; and when any semantic change amplitude is greater than a change threshold, generating a splitting mark at the tail position of the sliding window corresponding to the front summary information in two adjacent summary information. According to the method, a complete text is split into a plurality of sub-texts, then the change size between scenes described by two adjacent sub-texts is reflected through the cosine distance between semantic vectors corresponding to the two adjacent sub-texts, and the change size is used as the basis of text splitting, so that the whole to-be-processed text is reasonably split and marked. Therefore, clearer plot arrangement structures of works can be provided for creators, follow-up modification is facilitated, and meanwhile creation efficiency and quality are improved.