300 results about "Nearest neighbor search" patented technology

The invention provides an enhanced generation method based on question matching retrieval, and belongs to the field of matching generation, and the method comprises the following steps: S1, a semantic feature coding stage: carrying out real-time feature extraction and vector space mapping on a natural language query input by a user by adopting a deep neural network model, generating high-dimensional distributed representation with semantic representation capability; s2, a knowledge base intelligent retrieval stage: executing multi-dimensional semantic matching in the vectorized knowledge base based on an approximate nearest neighbor search algorithm, and screening out a candidate knowledge set highly related to query semantics through a similarity measurement function; s3, retrieval matching results are automatically associated to the structured knowledge base through the established semantic-knowledge mapping relation, the preprocessed standardized response content is directly obtained, and the response content adopts a multi-modal data organization form and comprises a structured data entity and retains a rich text expression form.

The invention discloses a literature semantic search method and system based on elastic search, and relates to data retrieval. The literature semantic search method comprises the steps that vectorization processing is conducted on a noun phrase list by means of a text2vec-based-multilingual model trained based on a CoSENT method; according to the semantic vector, performing approximate nearest neighbor search in a second retrieval module to obtain first candidate data; inputting the query text data into a first retrieval module, and performing keyword matching through a BM25 algorithm to obtain second candidate data; fusing the first candidate data and the second candidate data to obtain third candidate data; a Sequence Matcher algorithm is adopted to calculate character string similarity between expansion words in the third candidate data, a similarity threshold value is set based on the length of the longest common subsequence, duplicate removal is carried out, and fourth candidate data is obtained; and performing weight distribution based on positions and similarity scores on the fourth candidate data, and enhancing the distinction degree of the extension words by expanding a score interval to obtain extension word recommendation list data. According to the method, the accuracy of document retrieval is remarkably improved.

The invention discloses a document analysis method based on an RAG model and a dynamic knowledge graph, and relates to the technical field of artificial intelligence. The method is combined with an RAG model and a dynamic knowledge graph technology, and is realized by the following steps of: performing entity relationship joint extraction on an input document, generating a structural triple, and constructing a dynamically updatable knowledge graph; based on the knowledge graph, mapping entities and relationships into low-dimensional vectors by adopting a graph embedding model, and constructing a local vector knowledge base with a topological structure; receiving user questions in real time, encoding the user questions into query vectors, executing approximate nearest neighbor search based on the vector knowledge base, and matching related map fragments; and combining the retrieved graph fragments with the large language model, and generating a structured answer through path constraint of the injection knowledge graph. The method is used for solving the problem that in the prior art, a model cannot capture document deep semantics and dynamic relations insufficiently.

The invention discloses an automatic planning method and system for a curved surface path of a grinding robot, and the method comprises the following steps: S1, constructing a to-be-machined three-dimensional curved surface model and a triangular mesh model, and relates to the technical field of automatic control of robots for surface machining in the manufacturing industry. According to the method, a complex three-dimensional surface coverage path planning problem of a workpiece is converted into two low-complexity sub-problems of key point sampling of a two-dimensional path planning domain and three-dimensional shortest path searching on a triangular mesh model, a small number of sampling points are sampled only on the two-dimensional path planning domain, and the sampling process depends on a spatial index framework; according to the method, global traversal of disordered point clouds is avoided, three-dimensional coordinates are positioned through nearest neighbor search of a KD tree algorithm, adjacent three-dimensional ordered key points are sequentially connected through a graph search algorithm, and a continuous machining path is formed, so that the operand is greatly reduced, real-time response of path planning is facilitated, and the problems that traditional online planning is long in time consumption and difficult in real-time response are solved.

The invention discloses a sparse view angle-oriented three-dimensional Gaussian visual positioning method, which belongs to the technical field of positioning, is used for visual positioning, and comprises the following steps of: extracting global image features and local image features of a sparse image, and constructing a feature database; performing nearest neighbor search on the feature database and the RGB image to obtain a sparseness index, calculating a camera pose of a pseudo view angle, rendering a color image and a depth image under the pseudo view angle by using a training main model, extracting global features and local features and storing the global features and the local features into the feature database, constructing a 2D-3D corresponding relation in combination with a target color image, and performing dynamic inner point screening and pose solving. And obtaining a positioning result. According to the method, visual angle deficiency of a key area is intelligently completed, the balance and coverage of feature expression in a three-dimensional scene are effectively improved, and dynamic updating and mismatching elimination of a 2D-3D matching relation are realized, so that the positioning precision and the system stability are remarkably improved, and the method has higher robustness and actual availability under a sparse training condition.

The invention discloses a few-sample visual defect detection method based on approximate proximity search, and belongs to the technical field of industrial intellectualization. The system mainly comprises an image acquisition module, a feature extraction module, a memory bank construction module, a feature compression module, an approximate nearest neighbor search module and an anomaly detection and result output module. The feature extraction module is used for performing feature extraction on the collected image; the feature compression module is used for reducing the feature data volume and reducing the internal memory and calculation burden; the memory bank construction module is mainly operated during system initialization or model training and is used for constructing a feature memory bank of normal samples; the approximate nearest neighbor search module is responsible for quickly finding normal sample features closest to the input features in the memory bank; and the anomaly detection and result output module is used for converting distance information obtained by neighbor search into anomaly. According to the few-sample visual defect detection method based on the approximate proximity search, the effect of few-sample anomaly detection is achieved.

The invention provides a retrieval enhancement generation method and system for multi-modal semantic alignment in the technical field of computer vision and natural language processing. The method comprises the steps that S1, a large amount of text data, image data and 3D model data are acquired to construct a multi-modal vector knowledge base; s2, acquiring an input query image and a natural language question, and performing joint semantic coding on the query image and the natural language question to obtain a multi-modal query feature vector; s3, evaluating complexity, dynamically adjusting retrieval parameters based on the complexity, and retrieving Top-K candidate knowledge from the multi-modal vector knowledge base through an approximate nearest neighbor search method based on the retrieval parameters and the multi-modal query feature vectors; and S4, in combination with multi-dimensional similarity and token-level interaction alignment, screening each candidate knowledge, and outputting a retrieval result. The method has the advantages that the reasoning precision, the answer credibility and the response efficiency in a complex question and answer scene are greatly improved.

To accelerate search speeds for approximate nearest neighbor searches of vector databases, compute-in-memory techniques using NAND memory structures are introduced. For each element of the database, a kernel of its M nearest neighbors is determined. For each vector of the database, both the vector and its kernel are programmed in the arrays of a NAND memory based accelerator card, so that the vectors will be written into the memory arrays both as themselves and also in kernels of vectors for which they are a nearest neighbor. Metadata, associating the locations of the kernel members with the correspond vector is also stored in the memory system. After determining the input's nearest neighbor at one level of search, the metadata is then used to locate that nearest neighbor's nearest neighbors and their distances to the input vector are then computed in parallel in a compute-in-memory vector-vector dot product multiplication.

The invention discloses an obstacle avoidance motion planning method and system for redundant drive of an intelligent process of a welding robot and computer equipment. According to the method, firstly, a motion path of a robot is planned based on welding process redundancy, then a sampling strategy that the position and posture of a welding gun are separated is adopted, then the sampled posture is mapped to a planar two-dimensional space, and the relevance between an obstacle collision boundary and the obstacle avoidance posture of the welding gun is constructed; an incremental extreme gradient lifting model is adopted to carry out probability modeling on the collision boundary and obstacle avoidance attitude relevance, nearest neighbor search and new planning node expansion are carried out based on target cost to obtain new planning nodes, collision detection is carried out on the new planning nodes, and the new planning nodes passing the collision detection are used for incremental learning of an obstacle avoidance model; and finally, an optimization strategy of spherical linear interpolation and cosine slow motion time mapping is adopted to ensure continuity and smoothness of a motion planning path trace. The problem that in the prior art, it is difficult to quickly calculate and generate a collision-free welding track is solved.

The invention relates to the technical field of robot path planning, and discloses an improved robot path planning method based on an RRT algorithm, and the method comprises the steps: carrying out the global path searching through an improved fast exploration random tree (RRT) algorithm, and employing a special random sampling strategy and a KD-Tree to accelerate the nearest neighbor searching, and improving the searching efficiency; a greedy strategy is utilized to optimize a path, and redundant nodes are reduced; a smooth trajectory is generated by means of cubic spline interpolation, and control points are dynamically adjusted through collision detection. The method effectively solves the problems of path redundancy and poor smoothness of a traditional RRT algorithm, is suitable for the fields of robot navigation, logistics transportation and the like, and can remarkably improve the path planning efficiency and quality.

The invention discloses an electric power multi-modal corpus construction query method and system based on a sliding window, which is applied to the field of electric power data query, and comprises the following steps: obtaining a structured document according to electric power multi-modal data, segmenting the structured document to obtain a plurality of segmented text blocks, and storing the segmented text blocks into a database; inputting each segmented text block into a large language model to generate a to-be-stored text vector and construct an electric power multi-mode corpus, when a user query request is received, generating a plurality of query variants according to query data, performing nearest neighbor search on each query variant in the electric power multi-mode corpus to obtain a corresponding nearest neighbor search result, and storing the nearest neighbor search result in the electric power multi-mode corpus. And fusing each nearest neighbor search result to generate an electric power related document set comprising a multi-modal association mark. According to the method, the semantic units can be accurately captured, semantic breakage is avoided, the retrieval continuity and coverage rate are improved, the comprehensiveness and context adaptability of retrieval results are improved, and the data retrieval requirement under the complex scene of the power industry is met.

The invention relates to the cross technical field of artificial intelligence and data retrieval, and particularly provides an RAG question and answer optimization method and device.Firstly, original knowledge documents of various formats and natural language query of a user are received on an input layer, then a knowledge base construction module and a knowledge retrieval module are arranged on a processing layer, and a knowledge retrieval module is arranged on the processing layer; the middleware layer is provided with an MCP Server module and a vector database, the MCP Server module is a standard interface agent, and the vector database is a distributed storage engine for efficient approximate nearest neighbor search and mixed retrieval; and finally, deploying an LLM generation module at an output layer for generating a final answer based on the retrieved enhanced context. Compared with the prior art, the method has the advantages that irrelevant or low-signal-to-noise-ratio knowledge contacted by a large language model (LLM) can be effectively reduced, and the accuracy, the reliability and the practicability of knowledge base questions and answers are comprehensively improved.

An online system applies a visual language model and an optical character recognition model to a received image to generate descriptive information about unknown items in the image. The online system prompts a generative model with the descriptive information about unknown items in the image to separate the descriptive information into different bins each corresponding to a different unknown item in the image. For each unknown item detected in the image, the online system generates a target embedding from its descriptive information and performs a nearest neighbor search on an item catalog including embeddings for various items to find a set of candidate embeddings matching the target embedding. The online system retrieves item attributes of candidate items each corresponding to a candidate embedding of the set and prompts the generative model with this information to rank candidate items for the unknown item in the image.

The invention discloses an automatic SCADE test case generation method based on a large language model, which comprises the following steps of: analyzing various SCADE related documents to obtain an analysis result; the analysis result is converted into JSON data; performing key information extraction on the JSON data, and performing block processing on the extracted key information to obtain text blocks; vectorizing the text blocks, storing the vectorized text blocks into a vector database, converting a natural language query requirement of a user into a query vector, and performing approximate nearest neighbor search with the query vector in the vector database by utilizing RAG retrieval to obtain a preliminary retrieval result; based on metadata in the preliminary retrieval result, filtering, timeliness sorting and deduplication fusion processing are carried out, and a retrieved context information set is obtained; adopting a dynamic cue word template to construct an enhanced cue word based on the retrieved context information set and the natural language query of the user; and calling a large language model API, inputting an enhanced cue word, and generating a structured test case.

The invention discloses an intelligent medical big data information management system, and the system comprises a data collection layer which serves as a sensing neural network, employs an Internet of Things gateway to be compatible with a medical equipment protocol, and deploys an edge calculation node for physiological signal preprocessing; the intelligent preprocessing layer is used for cleaning and standardizing the original data transmitted by the data acquisition layer; the informatization service layer is used for realizing fine-grained authority control based on an RBAC (Role Based Access Control) model, realizing inspection process automation by integrating an LIS (Line Information System) interface through a Hyperdger Fabric block chain evidence storage key operation hash, and constructing a zero-footprint DICOM (Digital Imaging and Communications in Medicine) viewer; the intelligent analysis layer is used for carrying out data life cycle management by adopting a mixed storage architecture, carrying out feature engineering on data, carrying out label matching and classified storage by using efficient neighbor search and similarity / distance measurement, quantifying contribution of each feature or feature combination to a single prediction result through an SHAP value, generating an interpretation result, and outputting the interpretation result; and presenting an interpretation result to a clinician or related personnel through a query interface.

The invention discloses a data fusion method, system and device for power transmission and transformation equipment and a medium, and the method comprises the steps: obtaining the data of the power transmission and transformation equipment through a sensor, and extracting a multi-scale visual feature set based on a Gaussian filter; fitting the laser point cloud data through a principal component analysis method to generate a multi-scale point cloud feature set, and performing feature matching through a nearest neighbor search algorithm; and converting the visual image and the laser point cloud data into a common coordinate system, and fusing the laser points and the pixel points according to a preset weight to generate multi-source fusion data. According to the invention, by fusing the visual image and the laser point cloud data, the multi-dimensional information of the power transmission and transformation equipment can be obtained, and a more comprehensive and more accurate digital twinborn model is constructed; the subtle change of the power transmission and transformation equipment can be more accurately captured through multi-source fusion data, and the dynamic sensing of the operation state of the equipment is realized; through the multi-scale fusion method, complementary information in multi-source data can be effectively utilized, and the precision of the digital twin model is improved.

The invention discloses a decision control system and method based on temperature field and atmosphere analysis, and relates to the technical field of intelligent control. A monitoring module in the system obtains a temperature set and a gas concentration set; the trend analysis module constructs a three-dimensional model and establishes a coordinate system, a variation function is combined with a Kriging equation to supplement a temperature set to generate an expanded temperature set, the three-dimensional model is discretized into a plurality of nodes and is matched with points in the expanded temperature set through nearest neighbor search, a heat conduction equation and a convection boundary are set, and a finite element matrix equation set is constructed; solving to obtain a temperature field model and carrying out statistics to generate a temperature trend curve; a decision module compares the temperature trend curve, the gas concentration set and a process standard to obtain an abnormal section number, and analyzes a section temperature difference and a concentration error vector of the abnormal section number by adopting a fuzzy PID control algorithm to generate an adjustment vector; and the execution module performs automatic adjustment based on the adjustment vector, so that intelligent decision adjustment based on temperature field and atmosphere conjoint analysis is realized.

This application discloses a digital media content recommendation system based on AIGC. First, by leveraging a large language model (LLM) fine-tuned with instructions to interpret a series of behavioral events demonstrated by the user in the current session in real time, it can intelligently generate text that can accurately describe the user's current dynamic intention, enabling the system to keenly capture the user's subtle and immediate interest focus. Then, a text embedding model is used to convert the dynamic intention description into a vectorized representation to obtain an intention vector. Further, the intention vector is used to perform an efficient approximate nearest neighbor search in a pre-constructed content library vector index, thereby quickly locating and screening out candidate content that highly matches the user's current intention. Finally, several of the most relevant items are selected from the candidate list to obtain the final recommendation list. In this way, the system can significantly optimize the content recommendation effect in the session scenario and enhance the user experience by deeply leveraging the LLM's understanding of the user's dynamic intention and the generation ability of AIGC.

The invention relates to the technical field of natural language processing, in particular to a retrieval system and method based on retrieval enhancement generation, and the method comprises the steps: obtaining a user query text through a user interaction interface, and carrying out the embedding processing of the user query text to obtain a query vector; inputting the query vector into a vector knowledge base, performing approximate nearest neighbor search on the query vector and a storage vector in the vector knowledge base, and inputting the storage vector meeting a preset semantic distance into a candidate text block sequence; analyzing the semantic association strength between each text block in the candidate text block sequence and the query vector; performing descending sorting on each text block in the candidate text block sequence according to the similarity score vector; and inputting the enhanced prompt text into the large language model for reasoning analysis to obtain a retrieval result. According to the method, the text semantic similarity and the context correlation can be considered at the same time in the retrieval process, and the accuracy of the retrieval result is improved through a dynamic weighting mechanism.

The invention provides a laser radar and camera joint calibration method based on pixel point alignment, and the method comprises the steps: carrying out the preprocessing of point cloud data and an RGB image, and obtaining an optimized point cloud and a corrected image; carrying out edge feature recognition and extraction to obtain an edge pixel point set; projecting the optimized point cloud to a plane of the corrected image, and extracting an intensity gradient salient point set of the virtual intensity image; establishing a mapping relation of the two point sets through bidirectional nearest neighbor search, calculating similarity of mapping points, and performing data screening through the similarity to obtain an optimized data set; and constructing a joint loss function of the optimized data set according to a conversion relation among the calibration plate, the laser and the camera coordinate system, and solving through a least square method to obtain an external parameter matrix. According to the method, through intensity correction, image distortion compensation, cross-modal feature manifold mapping, bidirectional nearest neighbor matching and multi-constraint joint optimization, high-precision external parameter calibration is realized, and the robustness and real-time performance of calibration in a dynamic scene are improved.

The invention discloses a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation System) / map tightly integrated navigation positioning method and system based on road marking matching, and belongs to the technical field of navigation positioning, and the method comprises the following steps: acquiring priori road map data, foresight road image data, a GNSS original observation value and an INS original observation value; a semantic segmentation network is adopted to extract road marking semantic features in the foresight road image data, and a road marking feature instance is obtained; performing nearest neighbor search on the road marking feature instance and map elements in the prior road map data, and obtaining corresponding map matching observation based on a nearest neighbor search result; and coupling the GNSS original observation value, the INS original observation value and map matching observation to obtain a navigation positioning result. According to the method, a tight combination model based on GNSS / INS and map matching original observation values is provided, the positioning performance of a navigation system in a complex city scene is improved, and omni-directional pose constraint is provided for a vehicle.

The invention discloses an irregular roadway colored point cloud hole filling method, and belongs to the field of underground mine three-dimensional modeling and point cloud data processing. Firstly, dense slicing is carried out on roadway point clouds, slices are projected to a two-dimensional plane for polar coordinate sorting, disordered point clouds are ordered, then the slices serve as type value points, a control point solving matrix is solved through a chasing method, initial control points are rapidly obtained, the control points are optimized through a progressive iterative approximation algorithm (PIA), and the optimal control points are obtained. The method comprises the steps of effectively reducing errors between a fitting curve and an original point cloud, performing non-uniform cubic B-spline adaptive fitting interpolation according to a point spacing to realize uniform filling of holes, and finally performing K-nearest neighbor search on interpolation points, calculating an RGB weighted average value of neighborhood points and assigning the RGB weighted average value to realize color repair of a hole region. The method has a good repairing effect on various types of holes of the irregular roadway of the mine, uniform color assignment can be carried out on point clouds at the same time, and the modeling quality and efficiency are effectively improved.

The invention provides a three-dimensional part retrieval method based on graph similarity search, relates to the field of computer graphics, and solves the technical problems that in the prior art, geometric and design semantic information of CAD cannot be fully utilized, and a topological relation is difficult to capture and display, so that the retrieval efficiency is low. The method comprises the following steps: acquiring part three-dimensional data of a computer-aided design (CAD) model, and constructing a training data set; constructing an edge-surface connection diagram based on the three-dimensional data of the part; calculating a graph editing distance (GED) matrix of all edge surface connection graphs in the training data set as a supervision signal; based on the GED matrix, training a sorting model, mapping an edge-surface connection graph to a hidden space, and constructing a part vector database according to an output graph-level embedding vector; the sorting model is constructed based on a graph attention network; and inputting a to-be-queried CAD part into the trained sorting model to obtain a feature vector, carrying out nearest neighbor search in the part vector database, and returning a similar part result.

The invention belongs to the technical field of building three-dimensional visualization, and particularly relates to an urban building three-dimensional visual lightweight system based on data fusion, which comprises a real image acquisition module, a bridging module and a building object updating module. According to the scheme, through point cloud data comparison and vector analysis, elimination of disappearance building point clouds and addition of newly-added buildings, it is ensured that a non-change part model is unchanged, meanwhile, the large-scale point cloud processing calculation amount is reduced, octree block optimization and nearest neighbor search optimization are adopted, the point cloud matching calculation amount is further reduced, and a verification and model correction mechanism is utilized, so that the accuracy of point cloud matching is improved. Compared with a traditional static city model, the method is more suitable for dynamic scenes such as city planning and disaster monitoring; the height is reduced layer by layer from the view angle of the unmanned aerial vehicle, a middle view angle virtual image is rendered through an initial rough model, the feature difference with the ground view angle is gradually reduced, and the cross-view angle three-dimensional reconstruction quality is improved.

The invention belongs to the field of visual SLAM, and discloses a visual SLAM optimization method and system based on lightweight depth features, and the method comprises the steps: calling an acceleration feature module to extract feature key points, a high-dimensional feature descriptor and a confidence map from an image, screening high-confidence feature points through a feature matching module, and carrying out the recognition of the feature points, and then matching is carried out by combining nearest neighbor search and a multi-layer perceptron to generate a matching pair, then pose estimation and local map tracking are carried out based on the matching pair, and finally a local map area is updated. The method effectively deals with a complex environment, reduces tracking failures, enhances feature extraction capability, improves pose estimation precision, reduces error accumulation, reduces calculation burden, improves real-time performance, reduces error accumulation in an unknown scene or a scene with large appearance change, improves trajectory precision, improves feature matching accuracy in a complex scene, and has good application prospects. And the stability, the consistency and the tracking performance of the SLAM system are enhanced.

The invention discloses a carriage groove positioning and steel coil placement center determining method based on three-dimensional point clouds, and relates to the technical field of industrial automation, and the method comprises the steps: firstly carrying out the preprocessing of an original point cloud, and extracting a carriage main body point cloud through ground removal, outlier filtering and DBSCAN clustering; on the basis, the overall orientation of the carriage point cloud is determined through principal direction analysis, a central area point cloud is obtained through cutting in the principal direction, and after x and z coordinates of the central area point cloud are extracted, groove valley points are positioned through sliding window calculation, Gaussian smoothing and peak value detection. As the size of the compartment groove is generally larger than that of the steel coil, in order to guarantee the loading safety of the steel coil, sole timbers need to be arranged on the edge of the groove, so that the sudden drop point of the z value is detected forwards based on the valley point, and the edge position of the groove is determined. And calculating the center coordinate of the steel coil to be placed by combining the edge of the groove, the size of the sole timber and the radius of the steel coil, and finally mapping the two-dimensional detection point back to a three-dimensional space by using KD tree nearest neighbor search to obtain the three-dimensional coordinate of the steel coil placing center.

An extreme classification method includes receiving training data-points and classifier vectors associated with the training data-points. A plurality of training epochs are performed wherein each training epoch includes generating query embeddings for each data-point, sampling a predetermined number of negative labels from a set of negative labels for each of the training data-points; and training an encoder and the classifier vectors using the sampled negative labels. Positive labels and the sampled negative labels are then used to compute a loss. Encoder parameters and the classifier vectors are then updated based on the computed loss. For a first portion of epochs, the sampled negative labels include only uniformly random negative labels. For a second portion of the epochs, the sampled negative labels include uniformly random negative labels and hard negative labels. The hard negative labels are identified using an Approximate Nearest Neighbor Search (ANNS) index (308) built on the classifier vectors.

The invention discloses a visual scene matching and positioning method and system based on an improved ViT model. The method comprises the following steps of: for a given aerial image and a satellite map, segmenting the given aerial image and the satellite map into L image blocks with fixed sizes, and mapping each image block to a high-dimensional feature space through linear projection after unfolding to form a sequence; respectively inputting the preprocessed aerial image and the satellite map into an improved ViT neural network model, outputting a global feature vector, aggregating local features, and combining the global features to generate a robust feature expression vector; carrying out dimension reduction on the high-dimensional feature space by adopting an incremental principal component analysis method, and carrying out nearest neighbor search by utilizing a KD tree to obtain a satellite map most similar to the aerial image; and performing position back calculation based on the matching relationship between the aerial image and the satellite map. According to the invention, through heterogeneous image feature extraction and rapid feature matching, autonomous positioning of the unmanned aerial vehicle under the condition of satellite rejection is realized.

The invention provides a multi-source security knowledge fusion driven security risk management large model construction method and application. The method comprises the following steps: constructing a campus security knowledge graph and a semantic vector database based on multi-modal campus security data; performing approximate nearest neighbor search in the semantic vector database based on user query to obtain context evidence related to the user query, splicing the context evidence with the user query to obtain a retrieval enhancement prompt, the user query being campus security risk information of any mode; and training based on the campus security knowledge graph to obtain a security risk management large model, and inputting the retrieval enhancement prompt into the security risk management large model to obtain a risk solution. According to the scheme, the attention confidence, the feed-forward network confidence and the knowledge alignment confidence of all levels are aggregated in the reasoning process to generate the comprehensive confidence score, so that the illusion problem caused by no data reference in the model reasoning process is effectively avoided.

The invention relates to the technical field of positioning and navigation, in particular to a two-dimensional laser positioning method and system in a sparse scene, and the method comprises the steps: endowing a point cloud with a corner semantic label through a semantic segmentation model through fusing two-dimensional laser radar and camera data, and removing a dynamic object; extracting wall corner line and wall corner point features, performing data association with a pre-constructed semantic map, performing coarse positioning to obtain an initial pose, and performing fine optimization by using a semantic PLICP algorithm to obtain a fine positioning pose; and finally, carrying out confidence evaluation and fusion on the fine positioning pose in a particle filtering mode, and outputting a final optimized pose. According to the invention, the wall corner instance and features in the wall corner semantic point cloud are extracted, nearest neighbor search association and coarse positioning are carried out on the semantic map, the initial pose is obtained, semantic PLICP fine optimization is carried out, and particle filtering fusion is supplemented, so that high-precision and robust positioning independent of manual marking in a sparse scene is realized.