36 results about "Word model" patented technology

The invention discloses an intelligent test paper composition and scoring method and system based on a knowledge graph. The method comprises the following steps: S1, constructing a student knowledge graph state sub-graph; s2, constructing a question bank knowledge graph; s3, on the basis of a node embedding modeling method of an improved DeepWalk algorithm, performing joint training and low-dimensional representation learning on knowledge point nodes by utilizing a continuous bag-of-words model of multi-head context aggregation, and constructing a topic adaptation degree scoring index; s4, outputting an optimal test paper scheme set; s5, collecting a current answer record when the student completes the test paper; and S6, extracting a semantic feature vector and a behavior feature vector, constructing a fusion score feature vector, and generating scores of test paper subjective question answers. According to the invention, accurate test paper composition and multi-dimensional subjective question intelligent scoring for individual knowledge states of students are realized, the method is suitable for personalized learning evaluation and teaching feedback scenes in an education evaluation platform, and the method has the advantages of high personalization, fine feedback and automatic scoring.

The invention provides a satellite denial high-altitude hovering positioning method based on visual perception and inertial measurement, which is applied to an unmanned aerial vehicle control system, and the unmanned aerial vehicle control system comprises a front-end processing module and a rear-end processing module, the front-end processing module executes the following steps that ORB feature detection and extraction are carried out on a ground environment image shot by the downward-looking binocular camera, and space coordinate information of feature points is obtained; constructing a key frame dictionary database based on a bag-of-words model, and completing loopback detection through similarity calculation of feature points; initial pose estimation is realized based on a PNP algorithm and inertial measurement unit IMU pre-integration, and an initial estimation pose sequence is generated; wherein the rear-end optimization module executes the following steps: performing real-time optimization on an initial estimation pose sequence by adopting a sliding window optimization algorithm; and solving the maximum posterior probability estimation through an L-M optimization algorithm, and determining the high-altitude hovering positioning pose of the target. And reliable guarantee is provided for stable operation of the unmanned aerial vehicle in a high-altitude complex environment.

Embodiments of the present disclosure disclose a method, device and medium for optimizing data query based on quantum technology. In the method, query text data of a user at a current time and in a historical time window is collected, and a bag-of-words model is constructed after preprocessing the query text data. Feature vectors in the bag-of-words model are quantum state encoded, and a dominant feature vector of the query text data is extracted through a quantum principal component analysis algorithm. Based on the dominant feature vector, a plurality of data items that satisfy a similarity condition with the dominant feature vector are retrieved from a target database to generate a preloaded data set. The preloaded data set is loaded into a head position of an LRU cache linked list in order of similarity with the dominant feature vector. Embodiments of the present disclosure enable faster extraction of data features and identification of data features when facing large amounts of data and high-dimensional data, and faster finding of data with similar features.

The application relates to the field of artificial intelligence and discloses a text connotation quality evaluation method for improving the accuracy of text connotation quality evaluation and improving model training efficiency. The text connotation quality evaluation method comprises the following steps: obtaining an initial text from a preset medical record text; processing the initial text through a natural language processing algorithm to obtain a target text; performing text coding on the target text through a preset bag-of-words model and a preset automatic coding model to obtain first text features; performing feature extraction on the target text to obtain second text features, wherein the second text features comprise text complexity features, text grammar style features and medical semantic features; performing evaluation processing on the first text features and the second text features through a trained logistic regression model to obtain an evaluation result, wherein the evaluation result is used for identifying the connotation quality grade of the preset medical record text. The application also relates to the blockchain technology, and the target text is stored in the blockchain.

The invention discloses a power grid operation violation dictionary construction and automatic classification method and system, and relates to the technical field of operation violation classification, and the method comprises the steps: carrying out the text preprocessing of power grid operation data; constructing an initial word vector space based on Jieba word segmentation and a word bag model; considering the problem that an initial violation dictionary obtained by initial word segmentation has many semantic similar or repeated word segmentation redundancy, introducing skewness and kurtosis based on a relative frequency matrix to construct a unique index, selecting key segmented words, further constructing a classical violation dictionary, and forming a keyword vector space; and establishing a violation classifier according to the obtained keyword vector space and the corresponding violation type label, and intelligently realizing automatic classification of violation codes, violation types and problem types. Intelligent classification of violation codes, violation types and problem types is completed according to unstructured text data formed by power grid field operation violation description.

The application provides a network security event processing method and device, electronic equipment and storage medium, which belong to the technical field of network security. The method comprises the following steps: in response to receiving a monitoring instruction for a network security event, instructing a large language model to monitor the network security event based on the monitoring instruction; when receiving a monitoring result of the network security event monitored by the large language model, outputting alarm information for the monitoring result; in response to receiving user interaction information input for the alarm information, instructing the large language model to output a response strategy for the network security event based on the user interaction information; inputting the response strategy into a prompt word model to obtain a security response instruction for the network security event; and instructing the large language model to process the network security event based on the security response instruction and output a processing result for the network security event. The processing efficiency of the network security event is improved.

The application discloses a kind of unmanned aerial vehicle monocular vision inertial SLAM loop detection methods based on undergrowth trunk geometry, the method includes the following steps: step 1, trunk region segmentation and three-dimensional point cloud generation;Step 2, trunk clustering detection and position positioning;Step 3, triangular geometric feature construction and screening;Step 4, multidimensional similarity calculation;Step 5, geometric and visual joint decision and loop confirmation.This method extracts stable geometric information of trunk by driving depth data, constructs triangular features with invariance with trunk position as vertex to break through the dependence of visual texture, realizes double-path independent detection and joint decision by combining traditional visual bag-of-words model, balances detection accuracy and real-time through adaptive matching strategy, thereby effectively improves the accuracy, robustness and environmental adaptability of loop detection in undergrowth GNSS-free environment.

The application provides a task allocation model construction method and system for a heterogeneous intelligent agent, and the method comprises the following steps: determining a target heterogeneous intelligent agent in real time, and matching a target performance parameter corresponding to the target heterogeneous intelligent agent in a preset database in real time; creating a corresponding professional term vocabulary according to the target performance parameter based on a preset rule in real time, and converting the professional term vocabulary into a plurality of distributed vectors corresponding thereto through a preset continuous bag-of-words model in real time; and training a task allocation model for the target heterogeneous intelligent agent according to a preset bidirectional long short-term memory network and the plurality of distributed vectors in real time, wherein each distributed vector has uniqueness. The task allocation model can objectively and accurately complete the task allocation of the heterogeneous intelligent agent, and the work efficiency is greatly improved.

The invention relates to a text auxiliary analysis and evaluation method. The method comprises the following steps: S1, data preparation: preprocessing a text, including stop word removal, punctuation mark processing, special character processing and case and small case conversion; s2, establishing a bag-of-words model, converting a text into a word frequency vector, and weighting by using TF-IDF; s3, word embedding: converting each word into a high-dimensional vector by respectively using Word2Vec and GloVe, and then performing result comparison; s4, performing feature selection, and performing screening according to importance or correlation of features to reduce noise and overfitting; s5, selecting a learning model, and importing the features for training; and S6, predicting new text data by using the trained model. According to the method, preprocessing, feature extraction, model training and evaluation are carried out on text data through a series of steps, and finally a model capable of being used for text auxiliary analysis and evaluation is obtained.

The invention relates to a visual repositioning method and electronic equipment. The method comprises the following steps: acquiring a current frame image of a vehicle for repositioning; determining at least one candidate image from each mapping image of a visual map according to the global bag-of-word vector of the current frame image and the global bag-of-word vector of each mapping image of the visual map; determining a distribution word bag vector and a depth word bag vector of the current frame image; according to the distribution word bag vector and the depth word bag vector of the current frame image and the distribution word bag vector and the depth word bag vector of the candidate image, determining the real-time pose of the vehicle; wherein the global bag-of-words vectors represent matching conditions of all feature points in the corresponding image and feature points in the bag-of-words model; the distribution word bag vectors represent matching conditions of feature points of different areas in the corresponding image and feature points in the word bag model, and the depth word bag vectors represent depth information of the feature points of different areas in the corresponding image. According to the invention, the repositioning precision is improved.

This invention provides a method for constructing a hierarchical classification model for government procurement items. The method first constructs and encodes a hierarchical structure for the procurement items. Then, it performs text cleaning and word segmentation on the procurement project names, using a continuous bag-of-words model to train the corpus to obtain word vectors, or directly obtaining word vectors using a BERT Chinese pre-trained model, thus obtaining an initial text representation. Finally, the text representation and label representation are fed into the model for training. The three-layer network contained in the HA-BiGRU model can effectively solve the above problems. The text encoding layer with a BiGRU network as the encoder can further extract semantic information from the context; the hierarchical attention recurrent network layer can model the dependencies between layers and enhance the association between text and labels through a text-label attention module; the hybrid prediction layer integrates the local and global losses of hierarchical labels, optimizing the model by reducing the total loss.

The application is suitable for the field of automatic driving technology, and provides a dynamic scene autonomous positioning and mapping method fusing a bag-of-words model, comprising the following steps: recording environment data; constructing a point cloud map and aligning the map to a geodetic coordinate system, selecting a visual key frame and saving the corresponding pose; removing dynamic features in the visual key frame to generate a bag-of-words vector, constructing a mapping relationship between the bag-of-words vector and the pose, and saving the bag-of-words model; in the positioning process, preprocessing the constructed point cloud map, generating a bag-of-words vector by using static features of the camera collected image, searching in the constructed bag-of-words model, and mapping out the initial position of the vehicle; after completing the positioning initialization, performing registration, providing a registration initial value by fusing the bag-of-words model and GPS / IMU, and outputting the pose information of the vehicle in real time. The application solves the problem that the SLAM framework cannot obtain absolute pose and the problem that the autonomous parking positioning information is missing due to the shielding of signals such as GPS.

This invention relates to a fine-grained webpage identification method for complex network environments. First, webpage access traffic is segmented into five-tuples, and the segmented traffic is grouped using the SNI information contained in the data packets. Then, the data packets in specific groups are reassembled into TLS fragments, and their length information is extracted. During multiple accesses to the same webpage, ADUs representing webpage characteristics are repeatedly requested. By statistically analyzing the frequent items in the TLS fragment length information, feature vectors characterizing the webpage are constructed; these feature vectors are considered webpage fingerprints. This invention utilizes clustering to correct numerical fluctuations in feature vectors and designs a bag-of-words model to correct sequential fluctuations in feature vectors. Finally, the corrected feature vectors are input into a machine learning model for training, resulting in a classifier capable of accurately identifying webpages. This method can obtain stable fine-grained webpage fingerprints in complex network environments, enabling accurate identification of harmful webpage access behavior.

The invention discloses a subway construction risk classification method based on LDA topic modeling, and relates to the technical field of engineering risk management. Comprising the steps of exporting a hidden danger text record from a construction unit safety patrol system, obtaining initial text content, carrying out regularization processing to obtain cleaned text content, then constructing a dictionary, inputting the dictionary into a word segmentation tool, obtaining a word segmentation result, constructing a word bag model, calculating TF-IDF weight in combination with the word segmentation result, forming a corpus vector set, and determining LDA hyper-parameters. Inputting the corpus vector set and the LDA hyper-parameters into an LDA model for training to obtain a trained LDA model, inputting a to-be-detected text into the LDA model to obtain topic keywords and text topic distribution, and performing risk classification to form a structured risk text database. Structured and standardized classification of hidden danger information can be realized, and the accuracy and comprehensiveness of risk identification are improved.

The invention provides an analysis-free log anomaly detection method and device based on space-time semantic features, and the method comprises the steps: converting labeled log data into structured vector representation based on a preset bag-of-words model, and carrying out the clustering and grouping of the vector representation through a clustering algorithm, and calculating the feature value of each word in each group by adopting word frequency-inverse document frequency to form semantic feature representation of each log data, so that the extracted features can pay attention to local features. To-be-analyzed log data is matched to a target cluster based on the clustering algorithm, and semantic feature representation of the to-be-analyzed log data is calculated; and finally, calculating a normalized compression distance between the to-be-analyzed semantic feature representation and the labeled semantic feature representation, carrying out classification based on a KNN model, and determining a label of the to-be-analyzed log data. The method does not depend on a complex model structure and a training process, does not need additional training parameters, and has better generalization and applicability.

Embodiments of the present application relate to a positioning method and device of a mobile device, an electronic device and a storage medium. A key frame is acquired by a vision camera installed on the mobile device; mapping information and a feature descriptor of the key frame are determined; a bag-of-words model and a key frame image database are obtained according to the feature descriptor of the key frame; a current image frame is acquired, and the current image frame is converted into a current frame bag-of-words vector based on the bag-of-words model; a candidate key frame with the highest matching score with the current image frame is determined from the key frame image database according to the current frame bag-of-words vector, and a target key frame is determined according to the candidate key frame; a target node index corresponding to the target key frame is determined according to the mapping information, and pose information of the mobile device is calculated according to the target node index, so as to position the mobile device; that is, the prior pose of the mobile device is provided by the bag-of-words model, and the positioning accuracy is improved.

The invention discloses a saliency-point-line-based back-end optimization and loopback detection method in automatic driving, and the method comprises the steps: S1, obtaining an rBRIEF descriptor of a point feature and an LBD descriptor of a line feature from an image, and carrying out the key frame screening of the features from a time constraint or a key frame number constraint; s2, constructing a bag-of-words model based on saliency point lines, then determining the number and depth of branches constructed by a bag-of-words tree, and performing clustering by using K-means + + to generate a dictionary file; s3, constructing a vocabulary vector packet representing each key frame according to a point-line feature weight adjusting method of the saliency region, and performing similarity calculation according to a similarity calculation method of weighting point-line features by the saliency region; and S4, when the similarity of the two image frames is greater than a preset threshold value, carrying out loopback detection. According to the method, the line features are fused in the word bag model based on the point features, a similarity calculation method for weighting the point and line features by the saliency region is provided, the calculation efficiency is improved, and wrong matching of similar scenes is reduced.

Embodiments of the present application provide a relationship extraction method and device, the method comprising obtaining a first entity set and a first text feature from a corpus corresponding to a first target field, and mapping the first entity set and the first text feature into first vector data through a bag-of-words model, then inputting the first vector data into M classification models respectively to obtain M prediction results, and determining a final relationship of each first entity pair in the first entity set from N candidate relationships according to the M prediction results, each prediction result comprising a prediction probability of an inter-entity relationship of each first entity pair in the first entity set being respectively one of the N candidate relationships, and each classification model being obtained by training an initial model according to a second entity vector with a second relationship vector label and a second text vector of a corresponding source knowledge base. In this way, using transfer learning as a relationship extraction method, the knowledge of the source field is migrated to the target field, and the required professional annotation manpower can be reduced.

The invention belongs to the technical field of voice recognition, and particularly relates to a voice wake-up word user-defined setting method, system and device and voice wake-up device.The method comprises the steps that wake-up word text information input by a user is received, the user is prompted to record and store voice information corresponding to the text information, and the voice information is stored; data analysis and optimization are carried out on the recorded voice information, then multi-dimensional acoustic feature extraction is carried out, a user specific voiceprint model is generated, the voiceprint model is verified, and the voiceprint model is stored after being verified to be qualified; and prompting the user to perform wake-up inspection on the setting result, and outputting corresponding prompt content according to the inspection result. According to the method, rapid modeling and efficient recognition of the user-defined wake-up word can be realized; the voiceprint binding is realized by generating the specific voiceprint model of the user, so that the awakening accuracy is improved, and false triggering is reduced; meanwhile, the user-defined wake-up word model can be quickly generated without collecting a large number of real person voice samples, the research and development cost is remarkably reduced, and the development period is shortened.

This invention relates to a heuristic-based method for generating fault patterns in the aviation field, comprising: S1, extracting features from aviation fault text data using a bag-of-words model and the term frequency-inverse text frequency index method; S2, performing clustering category analysis of aviation fault text data based on k-means heuristics; S3, detecting outliers, extracting faults, and concatenating faults in the aviation fault text data to obtain fault pattern names; and S4, processing the obtained fault description text in real time to generate aviation fault patterns. This invention completes text data clustering category analysis using a bag-of-words model, the term frequency-inverse text frequency index method, and the k-means heuristic method. Furthermore, it obtains fault pattern names through outlier detection, fault extraction, and concatenation, enabling real-time detection of fault text data. By periodically re-clustering, it can discover new fault patterns by utilizing existing prior knowledge, thereby improving the accuracy and effectiveness of fault pattern detection.

The application discloses a kind of associated analysis systems of feedback data, to improve feedback data processing efficiency and quality;System covers multiple functional modules: subject classification module uses bag-of-words model and clustering algorithm to classify feedback theme;Data acquisition and processing module collects and processes the feedback data in target area within a year, generates standard data set;Association rule module analyzes data by mining algorithm, mines association relationship, and filters out value association rules;Department analysis module evaluates department service, and carries out training for low-performance department;Seasonal analysis module identifies season high incidence feedback and early warning, and forms feedback team for region;Age salary analysis module analyzes the relationship between age, income and feedback category, and forms a study group for high-frequency feedback category, thereby providing data support and decision basis for feedback work in all directions and depth.

The application discloses a visual inertial synchronous positioning and mapping method based on adaptive deep feature enhancement, comprising the following steps: ①extracting image feature points and original descriptors by using a SuperPoint network; ②dynamically determining the number of target features of a current frame according to the feature tracking efficiency of a previous frame, and performing spatial screening by using a hierarchical square covering algorithm; ③inputting the screened feature points and descriptors into a FeatureBooster module, fusing geometric attributes by self-promotion, aggregating image context information by cross-promotion, and obtaining enhanced descriptors; ④constructing a floating-point bag-of-words model by using K-means clustering, retrieving a loop-closing candidate frame, calculating a relative pose based on a Sim(3) model, and performing global optimization. According to the application, the feature tracking stability in a weak-texture environment can be improved and the trajectory drift error can be reduced without significantly increasing the calculation overhead.

The embodiment of the invention provides a cue word optimization method, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a question set, employing a processing large model, calculating a first optimization answer set, employing a scoring large model to score the first optimization answer set, and obtaining a first optimization score set; determining a first optimized cue word by adopting a cue word model; according to the first optimization prompt word and the question set, determining a second optimization answer set corresponding to the question set by adopting a large processing model, and scoring the second optimization answer set by adopting a large scoring model to obtain a second optimization score set; comparing the first optimization score set with the second optimization score set through a cue word large model, and determining a second optimization cue word; according to the first optimization score set and the second optimization score set, whether the second optimization cue word is the target cue word or not is judged, the complexity of the cue word optimization process is reduced, and the cue word optimization efficiency is improved.

This application relates to the field of artificial intelligence technology and discloses a method, apparatus, device, storage medium, and computer program product for enhancing prompt words. The method includes: analyzing the code of the coding development framework on which the multi-agent system depends, constructing the code flow of the multi-agent system based on the analysis results, constructing a business prompt word model based on the code flow, performing multi-dimensional detection on prompt words through the business prompt word model, and enhancing the prompt words based on the detection results. This application achieves full-process tracking of prompt words by analyzing the code of the coding development framework on which the multi-agent system depends, and performs multi-dimensional detection on prompt words by constructing a business prompt word model, thereby identifying complex security issues generated by the interaction of multiple prompt words, providing a systematic prompt word security guarantee mechanism for the multi-agent system, and thus improving the security and reliability of the multi-agent system.

The application discloses a kind of identification classification method and system to the speech data involving contradictory dispute, it is related to data identification field.The steps of the method include: after the feature extraction of received speech data, the basic features capable of expressing the speech content of current speech data are obtained, the mel frequency cepstrum coefficient information in all basic features is determined;By the sentiment analysis model obtained by training according to mel frequency cepstrum coefficient information, the mel frequency cepstrum coefficient information currently determined is classified into contradictory dispute event;Sentiment analysis model includes simple sentiment model based on bag-of-words model and complex sentiment model based on deep learning model.The contradictory dispute event in the present application can be more accurately identified in speech, and intelligent classification is carried out;Through sentiment analysis model based on deep learning model, semantic information can be effectively extracted, so as to improve the classification accuracy.

The application relates to the technical field of visual positioning, and discloses a semantic visual positioning method based on character identification, which comprises the following steps: a preparation step, a three-dimensional map construction step, an image retrieval step, based on the three-dimensional character map constructed in the previous step, inputting a query frame to perform image retrieval and pose estimation, after obtaining the matching frame of the current frame, the relative pose between the two frames needs to be calculated, and experiment comparison is performed; a data set proposed by TextSLAM is used in the experiment; and multiple classic and best methods in the related fields of image retrieval, position recognition and visual positioning are compared. The semantic visual positioning method based on character identification uses character identification information in a scene as a feature to perform visual positioning, so that a real bag-of-words model is realized instead of the visual "bag-of-words" model used in the foregoing work; visual positioning is performed by using semantic information based on character identification, so that the positioning accuracy and efficiency can be greatly improved.

This invention belongs to the field of smart home, specifically an offline voice command word update method, comprising the following steps: S1: hierarchical model construction; S2: dynamic model compression and distribution; S3: hot patch writing in idle frames; S4: hierarchical collaborative recognition. This invention dynamically loads new command word models by allocating a dedicated extended model area, utilizes system idle frames for data writing, achieving "zero downtime" updates. It avoids redundant calculations through a dual-model parallelism and feature sharing mechanism, and only activates the extended model for secondary matching when the confidence of the base model is insufficient. Furthermore, it employs "8-bit vector quantization + cepstral residual coding technology" to compress the extended command words, reducing the size of a single command to an extremely small volume.

The application discloses a stable control system control strategy text vectorization method for strategy quick search, and the method is based on the text information of the stable control system control strategy, specifically including condition information and result information; based on an improved bag-of-words model and specific coding rules, a high-dimensional vector corresponding to the stable control system control strategy text information is obtained, and vectorization of the stable control system control strategy is realized. The method provided by the application gives a new idea for stable control strategy search, changes the control strategy text matching into vector matching, and realizes stable control strategy search with faster speed and higher efficiency. The embodiment shows that the method has significant performance advantages, and the strategy search time is greatly reduced compared with the text matching method, which is of great significance for improving the complex working condition adaptability of the stable control system and guaranteeing the safe and stable operation of the power grid.

The application relates to the field of artificial intelligence and smart home technology, and specifically provides a home algorithm network host application method and system. After the home algorithm network host is powered on, a voice module, a semantic understanding module, an AI monitoring module, a system control module and a communication module are started in sequence; the voice module loads a wake-up word model and VAD parameters, and is ready to receive user voice input; the semantic understanding module realizes semantic analysis and context understanding; the AI monitoring module starts a camera and an environment sensor, realizes real-time collection of video streams and environment data; the system control module initializes a local device interface, and provides interface support for home appliance control; the communication module establishes an internal message queue and an external network interface, and provides guarantee for data interaction among modules and external device communication. Compared with the prior art, the application can construct a high-performance, low-delay, safe and reliable home smart algorithm network host, and provides an integrated solution for home environment control and home security.

The application provides a satellite denial high-altitude hovering positioning method based on visual perception and inertial measurement, which is applied to a UAV control system, and the UAV control system comprises a front-end processing module and a back-end processing module, wherein the front-end processing module performs the following steps: performing ORB feature detection and extraction on a ground environment image shot by a downward-looking binocular camera to obtain spatial coordinate information of feature points; constructing a key frame dictionary library based on a bag-of-words model, and completing loop detection through similarity calculation of the feature points; realizing initial pose estimation based on a PNP algorithm and inertial measurement unit (IMU) pre-integration to generate an initial estimated pose sequence; wherein the back-end optimization module performs the following steps: performing real-time optimization on the initial estimated pose sequence by using a sliding window optimization algorithm; solving maximum a posteriori probability estimation by using an L-M optimization algorithm to determine a target high-altitude hovering positioning pose.