76 results about "Character vector" patented technology

The invention relates to the technical field of image recognition, in particular to a license plate recognition system and method based on an image technology and a medium. The method comprises the following steps: acquiring area sensing data and a camera image set, and performing deformation effect compensation to obtain an environment compensation image set; performing image diffusion reverse enhancement on the environment compensation image set to obtain a license plate area enhanced image set; performing character region high-dimensional topological mapping based on the license plate region enhanced image set to obtain a character segmentation matrix; extracting character morphological characteristics according to the character segmentation matrix, and performing character recognition on the character morphological characteristics to obtain a character recognition result; and carrying out cross-character semantic compensation on the character recognition result to obtain a semantic compensation license plate character vector, and carrying out multi-target cross verification on the semantic compensation license plate character vector to obtain a license plate recognition result. According to the invention, the accuracy and robustness of license plate recognition can be improved.

The invention belongs to the technical field of electronic medical records, and particularly discloses a medical text named entity recognition method and system.The method comprises the steps that an original medical text is collected and serves as input of a DLBC, a pre-training language model serves as an embedded layer of the DLBC to conduct word segmentation processing on the original medical text, the text is converted into a character sequence, and the character sequence is stored in a database; sending the character sequence into a pre-training language model to generate a corresponding character vector representation; extracting medical nouns and categories by using a corpus, and constructing a domain dictionary containing multiple categories of medical nouns; using a multi-head attention mechanism to obtain a feature sequence; performing feature extraction and context information modeling on the feature sequence by using a bidirectional long short-term memory network Bi LSTM; and the output of the Bi LSTM is converted into NER label probability distribution corresponding to each character by using a linear layer, and a final recognition result is obtained by applying a joint decoding strategy CRF. By adopting the technical scheme, entity embedding enhancement is carried out by utilizing the external information provided by the domain dictionary, and the medical entity recognition accuracy is improved.

The invention relates to a robot bionic character establishing and cultivating method and an expression robot. The method comprises the steps that a character model, an age model and an environment model are established, and the emotional state of the robot is initialized; external environment input is received and analyzed, and short-term emotional tendency and long-term emotional tendency of a target object are extracted; acquiring current age information of the robot from the age model; updating a short-term emotion vector of the robot according to the short-term emotion tendency and the current age information, and updating a character vector of the robot according to the long-term emotion tendency, the environment vector and the current age information; and circulating the above steps, and after a preset time, outputting a final character to form a report so as to simulate the growth process of the child. The user can establish a character model, an age model and an environment model, and then the expression robot can update the character model in real time through factors such as language communication and behavior actions of parents to the child / robot, the family environment where the child is located and the like.

The invention provides a big language model character shaping method and device, electronic equipment and a storage medium, and belongs to the technical field of data processing.The method comprises the steps that character prompts and dialogue content are input into a big language model, and dialogue vectors are obtained; inputting the dialogue vector into a character vector generator to obtain a character adjustment vector; the character vector generator is a neural network model obtained by training on the basis of a historical dialogue data set with character annotations; inputting the character adjustment vector into a large language model to adjust a first output vector output by the large language model to obtain a second output vector; the first output vector is an output vector obtained by a large language model based on the dialogue vector, and the second output vector is an output vector containing character features. According to the big language model character shaping method and device, the electronic equipment and the storage medium provided by the invention, personalized flexible adjustment of the big language model can be realized.

The invention discloses a multi-modal person wearing state adaptive detection method and device. The method comprises the following steps: acquiring an image of a target object; calling a pre-stored reference object image and a corresponding text description from a database; processing the image of the target object, the reference object image and the corresponding text description by using a multi-modal CLIP model to obtain a target image feature vector, a reference image feature vector and a reference text vector; respectively calculating the similarity between the target image feature vector and the character vector and the similarity between the reference image feature vector and the character vector; comparing the two similarities, and judging whether the user is in a preset wearing state or not according to a comparison result. According to the method, multi-modal information of images and texts is combined, image and text data processing is carried out based on the fine-tuned CLIP model, the understanding ability and accuracy of a recognition task are improved, and the method has high generalization ability and deployment efficiency.

The invention discloses a journal paper traceability method and device based on an RAG technology and computer equipment, relates to the technical field of paper indexing, and aims at enhancing the depth and breadth of retrieval based on the RAG technology while improving the traceability accuracy and efficiency by combining vector and keyword retrieval and capturing association and contextual relationships among literatures. The method comprises the steps of obtaining paragraph characters to be traced, and performing vectorization processing on the paragraph characters to obtain character vectors; performing vectorization retrieval on the character vectors through a vector index database to obtain a plurality of first related document blocks, the vector index database being constructed based on an RAG technology; performing keyword retrieval on the paragraph characters to obtain a plurality of second related document blocks; according to the document relevancy between each first related document block and the paragraph text and between each second related document block and the paragraph text, document tracing operation is executed, and a target original document is obtained and output.

The invention relates to the technical field of knowledge maps, and particularly provides a role perception-based map construction method and system, a terminal and a storage medium, and the method comprises the steps: extracting time, places and figures from event data, taking the extracted time, places and figures as roles, and building a relationship between the roles; entities in the event data are constructed into roles, the roles serve as virtual entity nodes, multivariate relations between the roles serve as edges, and a knowledge graph is constructed by constructing role vectors and a pattern matrix; and constructing a scoring function and a loss function, and training the knowledge graph according to a strategy of minimizing the loss function based on the scoring function and the loss function. The time-space factors can be conveniently added into the model, and the map prediction accuracy is improved.

The present disclosure relates to a mathematical formula recognition method and apparatus, and an electronic device and a readable storage medium. The method comprises: acquiring an original image containing a mathematical formula; inputting the original image into a formula recognition model, so as to obtain a predicted character vector that is output by the formula recognition model; the formula recognition model performing enhancement processing on the basis of the correlation between pixels in a region where the formula in the original image is located, so as to obtain an image encoding vector, and performing recognition processing on the image encoding vector to obtain the predicted character vector; and generating the mathematical formula in the original image on the basis of the predicted character vector. In the present embodiment, by means of a formula recognition model, enhancement processing is performed on pixels in a region where a formula in an original image is located, such that the correlation between characters in the original image can be acquired, thereby facilitating an improvement in the accuracy of predicting a character vector, and further improving the accuracy of reducing a mathematical formula.

The invention relates to the technical field of natural language processing, in particular to a multi-granularity Chinese medical named entity recognition method and system for dynamic knowledge enhancement, and the method comprises the steps: constructing a medical term knowledge base containing a plurality of Chinese medical terms, and generating a term representation vector for each term; the method comprises the following steps: for an input Chinese medical text sequence, extracting a character initial embedding vector, fusing a word-level embedding vector of a word where the character initial embedding vector is located and an adjacent word-level embedding vector to obtain a multi-granularity feature fusion vector, and encoding to obtain a character vector containing context semantics; performing semantic alignment optimization based on the character vector and the term vector to obtain a term information enhanced character vector, and dynamically updating the medical term knowledge base according to the term information enhanced character vector; and taking the updated term vector as knowledge priori, performing multiple rounds of screening on the text candidate segments, and outputting a medical named entity recognition result. The method can effectively improve the recognition accuracy of nested entity, isomorphism and ambiguity terms, reduces boundary division errors and category confusion, and improves the reasoning efficiency.

The invention discloses an implementation method and system for AI and Agent agents to perform autonomous exploration and produce content, and relates to the technical field of AI and Agent agents, and the implementation method comprises the following steps: receiving an Agent identity file, an exploration target keyword, a narrative style parameter and an observation preference parameter configured by a user at a client, and generating Agent metadata containing character vectors and behavior tendencies; generating a virtual city model with a spatial topological structure based on GIS data, a POI map and historical and cultural corpora, and endowing buildings, roads and NPCs in a city with queriable semantic tags and interactive states to form a city-level semantic environment; by constructing a city-level semantic environment and a multi-agent collaborative exploration mechanism, autonomous perception, hierarchical decision and dynamic narrative generation of the AI Agent in a highly structured virtual city are realized, and by means of deep fusion of GIS data, POI atlas and historical and cultural corpora, a real geographic basis and cultural connotation are given to a virtual space, and immersion and logic coherence are improved.

The invention belongs to the technical field of public security management, and relates to an unmanned aerial vehicle public security inspection method, system and device. In public security inspection, a traditional method is limited by the environment and is difficult to accurately extract figure targets and process dynamic distance relations among figures. A target inspection area image is collected through an unmanned aerial vehicle, a grid image is constructed by using a feature extraction method, and a character target is extracted by combining a character target feature image set; establishing a character vector field by means of a capsule neural network, analyzing a distance relationship, tracking the change of the distance relationship, and analyzing and processing through a long-short-term memory network to obtain a constant distance interval; and finally, judging whether the person is normal according to the distance relationship among the persons and prompting. According to the invention, people can be accurately identified, the distance relation is dynamically analyzed, abnormity is judged, and public security inspection efficiency and prevention and control capability are effectively improved.

Metadata for highlights of a video stream is extracted from card images embedded in the video stream. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. Card images embedded in video frames of the video stream are identified and processed to extract text. The text characters may be recognized by applying a machine-learned model trained with a set of characters extracted from card images embedded in sports television programming contents. The training set of character vectors may be pre-processed to maximize metric distance between the training set members. The text may be interpreted to obtain the metadata. The metadata may be stored in association with the portion of the video stream. The metadata may provide information regarding the highlights, and may be presented concurrently with playback of the highlights.

A domain name resolution request sent by a terminal is received. The domain name resolution request includes a domain name to be identified. The domain name includes at least one character. A sequence matrix corresponding to the domain name is determined. The sequence matrix includes at least one character vector. Each of the at least one character vector corresponds to each of the at least one character one to one. Each of the at least one character vector is sequentially input to an input and output gate. The input and output gate includes a logical operation rule between a plurality of activation functions. A feature vector corresponding to the sequence matrix is obtained by performing logical operation processing on each of the at least one character vector based on the logical operation rule between the plurality of activation functions.

The application provides an entity extraction method, device and equipment and a computer readable storage medium. The method comprises: obtaining at least one character vector and at least one extended word vector contained in a text to be extracted; the at least one extended word vector contains at least one preset entity vector; the at least one preset entity vector is the vector information of the entity corresponding to the text to be extracted in a preset entity dictionary; performing encoding and decoding transformation based on the at least one character vector and the at least one extended word vector to obtain at least one target entity corresponding to the text to be extracted; and the at least one target entity is used to realize natural language processing of the text to be extracted. Through the application, the efficiency of entity extraction can be improved on the basis of ensuring the accuracy of entity extraction.

The embodiment of the present disclosure discloses a text entity relationship analysis method, device, electronic equipment and readable storage medium. The text entity relationship analysis method comprises: a vector obtaining step, obtaining a vector combination comprising an entity vector corresponding to a character in a text, wherein the entity represented by the entity vector comprises at least one character; a vector splicing step, splicing the vectors in the vector combination for the character in the text to obtain a character splicing vector; a pooling step, performing pooling calculation on the character splicing vector to obtain an entity pooled vector; and an entity relationship determining step, splicing any two entity pooled vectors as an entity pair vector, and classifying the entity pair vector to determine the entity relationship.

The embodiment of the invention provides a similarity calculation method and device based on an improved BERT model and a storage medium, and the method comprises the steps: obtaining unlabeled text data and labeled text data in a specific field; training a BERT model by taking the unlabeled text data and the labeled text data in the specific field as input to obtain word vectors and word vectors; on the basis of a TF-IDF method, obtaining a new feature vector according to the tagged text data in the specific field, the word vector and the character vector; based on a multivariate distance, obtaining similarity according to the labeled text data in the specific field; and based on the BERT model, according to the new feature vector and the similarity, training to obtain an improved BERT model. According to the technical scheme, the problems of few training samples and low model training result accuracy in some specific fields are solved, and the adaptive capacity of the model and the text similarity accuracy can be effectively improved.

The present invention relates to a Chinese named entity recognition method based on local and global character representation enhancement. Existing Chinese named entity recognition methods model it as a character-based sequence labeling problem, but a single Chinese character vector is difficult to represent independent semantics, which brings about entity boundary and type recognition errors. The glyph structure of Chinese characters and their related domain terms contain information specific to domain entities, and effective use of this information is conducive to solving the above problems. To this end, the present invention uses an autoencoding mechanism to fuse the radical structure embedding, radical sequence embedding and contextual semantic embedding of characters to obtain a local character representation; and uses an interactive gating mechanism to combine the global domain term representation corresponding to the character with the local character representation to obtain an enhanced character representation; finally, the enhanced character representation is sent to the Bi-LSTM and CRF layers to obtain a character sequence label. Experiments on a domain Chinese named entity recognition dataset show that the present invention is effective.

The invention discloses a personalized emotion compensation method for lower limb walking intention perception of a stroke patient and a related device, and relates to the technical field of medical rehabilitation, the method comprises the following steps: synchronously collecting an electroencephalogram before a lower limb walking rehabilitation task of the stroke patient and a lower limb surface electromyogram signal in the task; preprocessing the electroencephalogram physiological signals and calculating dynamic brain function connection characteristics; preprocessing the electromyographic signals; inputting the brain function connection features into a TCN-LSTM model to obtain an initial emotion vector, and determining a comprehensive emotion vector in combination with an individual character vector converted by a psychological scale; time and amplitude scaling factors of the electromyographic signals are determined according to priori knowledge and a predefined optimization algorithm; inputting the preprocessed electromyographic signals into a convolutional network model to extract deep spatio-temporal features, performing scaling factor compensation to obtain compensated electromyographic features, inputting the compensated electromyographic features into a classifier to identify lower limb walking intention category output, and controlling rehabilitation equipment or feedback; the effect and efficiency of lower limb walking rehabilitation training of the stroke patient are effectively improved.

The present application relates to a kind of based on demonstrative word implementation financial field contract element extraction analysis method, including to document is divided into sentence, and text analysis is carried out;Initial vector representation of text character is obtained, aggregation demonstrative word is in the representation information in LSTM network layer, the similarity of demonstrative word and context other character vector is calculated, and weighted summation is carried out;Weighted information is converted into the probability distribution of each entity mark, and the optimal path is obtained by dynamic programming algorithm analysis, obtains the element entity information of text.The present application also relates to a kind of for realizing based on demonstrative word financial field contract element extraction analysis device, processor and its computer readable storage medium.The demonstrative word implementation financial field contract element extraction analysis method, device, processor and its computer readable storage medium based on the present application, compared with traditional method, guarantee element extraction accuracy, the whole time is far less than traditional method, and the efficiency of element extraction in contract can be greatly improved.

The present invention discloses an information parsing method, device and electronic device based on vocabulary enhancement, the method comprising: obtaining bond information to be parsed, constructing an input sequence according to the bond information, the input sequence comprising a character vector in the bond information and a vocabulary vector corresponding to the character; constructing a pre-training model, processing the input sequence through the pre-training model, generating a semantic representation of the input sequence; after passing the semantic representation through a discarding layer and a normalization layer, outputting a target vector, the target vector being the parsed structured bond information. The embodiment of the present invention makes full use of the vocabulary information in the text, so that the device can better identify entity boundaries and improve the accuracy of transaction element extraction in the secondary transaction business of financial bonds.

The invention provides a named entity recognition method for a high-speed railway technical improvement and overhaul project text, which comprises the following steps of: preprocessing unstructured data and semi-structured data in a picture and / or a text, and converting character-level positions of entity labels by adopting BIOES (Basic Input / Output Element Specification) labeling to obtain a technical improvement and overhaul data set; performing data enhancement on the technical improvement and overhaul data set by adopting a data enhancement strategy to obtain a data enhancement result; and inputting the text data subjected to data enhancement into an RBC fusion model, converting the text data into character vectors, carrying out a sequence labeling task to obtain a prediction label result of each character, and finally realizing an entity of a technical renovation and overhaul text through a large model prompt project method. Through preprocessing, data enhancement and an RBC fusion model, non-structured and semi-structured data in the high-speed rail technical improvement and overhaul project are efficiently identified, the problem that key information cannot be dynamically extracted through a traditional method is solved, and the method has the advantage that the information extraction efficiency and accuracy of the high-speed rail technical improvement and overhaul project are improved.

To extract metadata for highlights of a video stream from a card image embedded in the video stream.SOLUTION: Highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. Card images embedded in video frames of the video stream are identified and processed to extract text. The text characters may be recognized by applying a machine-learned model trained with a set of characters extracted from card images embedded in sports television programming contents. A training set of character vectors may be pre-processed to maximize metric distance between the training set members. The text may be interpreted to be able to obtain the metadata. The metadata may be stored in association with a portion of the video stream. The metadata may provide information regarding the highlights, and may be presented concurrently with playback of the highlights.SELECTED DRAWING: Figure 4

The invention discloses a scene character recognition method based on single vector query decoding and related equipment. The method comprises the following steps: performing visual feature extraction on an image to obtain visual features; coding the character to be coded to obtain a character vector; performing global information fusion on the visual features and the character vectors to obtain context vectors; inputting the context vector and the visual feature into a single vector decoder for decoding to obtain a target decoded character; and if the characters in the image are not completely decoded, taking the target decoded character as a to-be-coded character, and returning to the step of coding the to-be-coded character by adopting the position sensing hash coding method to obtain the character vector until the characters in the image are completely decoded, thereby obtaining a scene character recognition result. According to the method, the recognition accuracy of the model on shielded and fuzzy samples can be improved, the problems of attention drift and calculation amount increase faced by a traditional autoregression decoding method can be relieved, and the method can be widely applied to the technical field of computer vision.

The invention discloses a feature extraction and pseudo tag comparison-based clustering analysis method, which comprises the following steps of: after acquiring an English text sequence, converting the text sequence into an input sentence character embedding E meeting the requirements of a BERT encoder according to a BERT word segmentation device and a BERT embedding module; inputting the character embedding E into a BERT encoder module containing a shallow character representation fusion mechanism for character vector modeling to obtain an initial implicit vector H; inputting the character embedding E and the initial implicit vector H into an Electra and BERT module for local semantic extraction, and modeling the local semantic vector of the character to obtain a local semantic vector space; implicit vectors in a vector space are introduced into pseudo-label contrast clustering analysis, a pseudo label is used to evaluate and score an overall model, and contrast learning is used to perform clustering. According to the method, a better effect is achieved in an original intention recognition task. The method can be widely applied to the field of intention recognition.

The application provides a term extraction method and device, electronic equipment and storage medium, the method comprises: performing word segmentation processing on input corpus of a to-be-extracted term to obtain a plurality of candidate words; for any candidate word, based on a word embedding language model, a word vector corresponding to the target candidate word is obtained; based on a character embedding language model, a character vector corresponding to each character in the target candidate word is obtained, and a sum vector of each character vector is further obtained; according to the similarity comparison result between the word vector and the sum vector, it is determined whether the target candidate word is extracted as a term; wherein the word embedding language model and the character embedding language model are both obtained by training a pre-trained language model based on sample corpus in the vertical industry to which the input corpus belongs. Thus, manual annotation of training data is not required, the term extraction cost is reduced, and accurate extraction of terms in vertical industry corpus can be realized.

The invention discloses an automatic abstract generation method, device, electronic device and storage medium. The method calculates the first character vector and the second character vector of each single word based on two trained embedding vector models respectively from the original text and the named entities in the original text, and obtains the word vector of each single word by splicing; the word vector of each single word is encoded and decoded by the trained Transformer encoding and decoding model to obtain the word vector of multiple generated words, which can enhance the feature representation ability of the word vector of the multiple generated words, and each generated word is divided into a first-class generated word or a second-class generated word. The first-class generated words and the second-class generated words are respectively calculated using a trained pointer network and a memory network to obtain first-class output words and second-class output words respectively. The target abstract is composed of multiple first-class output words and / or multiple second-class output words, which can effectively solve the problem that uncommon named entities cannot be generated.

The application provides a knowledge extraction method and system based on prompt learning, embedding obtained unstructured text data to obtain a cache vector value; embedding a homogeneous prompt string to obtain a homogeneous prompt symbol vector value, and embedding a heterogeneous prompt character string to obtain a heterogeneous prompt character vector value; splicing the cache vector value, the homogeneous prompt symbol vector value and the heterogeneous prompt character vector value to obtain a spliced vector, taking the spliced vector as a cache vector value of a pre-training language model; adopting a regular matching method to obtain structured data from text data generated by the pre-training language model; the application uses automatically encoded prompt characters, automatically learns a potential semantic representation of a label, and solves a knowledge extraction problem in a general way through a generative large-scale pre-training language model, thereby improving the precision and efficiency of knowledge extraction.

The disclosure generally relates to a computer-implemented method for training a machine learning model for text generation, the method comprising inputting text into the machine learning model; preprocessing the input text to obtain a plurality of character vector representations; encoding, using an encoder, each of the plurality of character vector representations to obtain a plurality of word vector representations; generating, using a backbone model, a plurality of predictive word vector representations based on the plurality of word vector representations; decoding, using a decoder, the plurality of predictive word vector representations to obtain a plurality of character-probabilities; and updating the machine learning model based the plurality of character-probabilities. The disclosure also relates to a computer implemented method for generating text, a corresponding device, system and computer program.

The application provides a network request risk detection method, system and server, and relates to the field of network request risk detection. The method performs feature fusion on an injection keyword matching result, a network request parameter statistical result and a network request character vectorization result corresponding to to-be-detected data, dynamically optimizes continuous features in the fused feature data, and realizes accurate detection of SQL injection risks in a network request process through a lightweight classifier.

This invention discloses a work order classification method, system, device, and medium based on a multi-classification model, relating to the field of work order classification technology. The method includes: acquiring original work order text and forming a labeled sample set through expert labeling; statistically analyzing the label distribution and merging or data augmenting categories below a threshold to obtain a balanced labeled dataset; cleaning to remove non-semantic information to obtain a clean text dataset; training a static character vector model and a pre-trained dynamic character vector model to obtain static and dynamic vector weights; extracting and concatenating static and dynamic features to generate a fused feature matrix; hierarchically sampling the fused feature matrix into training and testing sets, inputting it into a Bi-LSTM-Attention-CNN multi-classification network for training to obtain a work order classification model for classifying new work orders. This invention can replace manual work order automatic labeling for tens of thousands of work orders, saving 2000 hours annually and directly providing data support for product iteration.