18 results about "Similarity query" patented technology

The invention discloses a power equipment fault diagnosis and control strategy recommendation method based on a knowledge graph, and relates to the technical field of power equipment diagnosis, and the method comprises the steps: collecting power equipment operation parameters, defining a node type and a relation type of the knowledge graph, and constructing a power equipment fault diagnosis knowledge graph; the method comprises the following steps of: extracting node features of nodes, and storing obtained node vectors into a vector database by taking optimization of a space distance between adjacent node vectors as a target to realize similarity query and semantic matching between the nodes; performing multi-hop reasoning in the knowledge graph by taking the alarm node as a starting point, screening a high-confidence path as a diagnosis conclusion, and generating a power equipment strategy recommendation list; evaluating the execution effect of the power equipment strategy recommendation list, adjusting the edge weight of the mapping knowledge domain corresponding relation, dynamically updating the structure and node vector of the power equipment fault diagnosis mapping knowledge domain, and achieving the self-adaptive optimization of the power equipment fault diagnosis mapping knowledge domain. According to the invention, the power equipment fault diagnosis capability is improved.

The invention discloses a header field-based report similarity analysis method and device and a medium, and the method comprises the steps: recognizing header regions of a plurality of reports according to header identifiers of the reports; obtaining a structured header field of the header area of each report by analyzing the field of the header area; performing text vectorization processing on the structured header field of each report header area, and converting each structured header field into a header vector; traversing the header vector of each report, and executing similarity query by taking the header vector of each report as a query vector in sequence and taking the header vectors of other reports as comparison vectors to obtain the similarity between each query vector and each comparison vector; determining similar fields of each query vector by comparing the similarity with a similarity threshold value; and performing quantitative analysis on the similar fields corresponding to the query vectors to obtain a field similarity analysis result of the multiple reports. According to the method, the report similarity analysis efficiency and accuracy are improved.

The application discloses a power grid malicious traffic detection method and system based on adaptive integration, relates to the technical field of power grid network security, and aims to solve the problem that the prior art cannot effectively detect encrypted malicious traffic in a resource-limited power grid edge environment. The application comprises the following steps: a client collects traffic data, monitors its own resource state and performance index, encapsulates the user demand into a request, and sends the request to a server; the server constructs a query feature vector, performs similarity query in a historical strategy library to quickly reuse or fine-tune the strategy, and if no strategy is found, an adaptive integrated decision algorithm is started to generate an optimal integrated learning scheme; the scheme is sent to the client, the client loads and runs the scheme and detects traffic features; and when malicious traffic is detected, the client triggers a security response action. According to the technical scheme, the detection strategy can be dynamically adjusted according to actual resources and demands without decrypting the traffic, and the optimal balance between detection effect and resource consumption can be achieved under the condition of resource limitation.

The invention provides a copyright registration method and system, a storage medium and a program product, and relates to the technical field of block chains. The copyright registration method is applied to a multi-block chain system comprising a main block chain and a plurality of sub-block chains, and the current sub-block chain generates a feature value corresponding to copyright data to be processed and performs similarity query on the copyright data to be processed in the plurality of sub-block chains based on the feature value, so that the copyright registration result is obtained. The distributed computing power of the current sub-block chain is fully utilized, the computing power consumption of the main block chain is further reduced, the main block chain evaluates whether the copyright registration processing of the data volume is supported or not based on the current computing power, and when the current computing power supports the copyright registration processing of the data volume, the copyright registration processing of the data volume is completed. And carrying out copyright registration processing on the to-be-processed copyright data so as to avoid the overload of the computing power of the main block chain.

The invention discloses a similarity query method for polygonal geographic data in a spatial database, and belongs to the technical field of spatial databases and geographic information. The method aims at solving the problem that in the prior art, when mass data are processed, the shape similarity searching efficiency is low. The core of the method comprises the following steps of: firstly, converting each polygon into a fixed-length digital signature with translation and zoom invariance through normalization, gridding and MinHash coding; secondly, the signature is divided into blocks (Band), an index is built, and the signature is stored in a distributed No-SQL database such as Apache Cassandra; and finally, during query, a candidate set is quickly filtered out by using a Band key of a query signature, accurate geometric similarity calculation and sorting are performed on the candidate set, and Top-K most similar polygons are returned. According to the method, efficient and accurate real-time shape similarity query in a large-scale spatial database is realized through a filtering-refining two-stage strategy, and the query efficiency and the system expandability are remarkably improved.

Embodiments automate software discovery and delivery by decoupling repository understanding from change implementation. To aid the product- and technical-discovery phases of the SDLC, a large language model parses source code to produce human-readable technical documentation stored in a documentation store and machine-readable representations comprising vector embeddings linked in a graph store. When product requirements are received via a conversational user interface or a programmatic API, the system generates a change plan. An LLM identifies affected subsystems using graph and similarity queries, composes structured prompts, and conditions automated code transformation tools; communicated through a machine-to-machine orchestration layer (e.g., a Model Context Protocol (MCP) gateway); to generate candidate artifacts. Artifacts are validated by policy gates and packaged as a reviewable change record with documentation, embedding, and graph updates staged in a pre-deployment overlay; upon promotion, the system atomically commits the staged updates. Telemetry from review and deployment informs subsequent planning.

A computer-implemented method (300) for processing a sample image of a medical condition is disclosed, which comprises: processing (305), by a set of computer vision models for a medical modality associated with the medical condition, the sample image to obtain a set of determination related to a plurality of predicted diagnoses for the medical condition; processing (310) the sample image to determine an associated visual embedding to be used as a query for querying a database; querying (315), based on vector similarity between the determined visual embedding and the respective keys in the plurality of the entries, the database to retrieve a top-k most similar entries from the database; and processing (320), by a medically-trained generalist foundation model (GFM) using the set of determination as reference context and images corresponding with the retrieved top-k most similar entries, the sample image to make a predicted determination on the medical condition shown by the sample image.

The application provides an image detection method and device, electronic equipment and storage medium, comprising: collecting an image to be processed; using a pre-trained image encoder to extract features of the image to be processed to obtain target image features; inputting training set text features and the target image features into a pre-trained contrast network to perform feature matching calculation to obtain a target image query result value; performing similarity calculation on the target image features and training set image features to generate a target similarity matrix; performing nonlinear mapping calculation on labels and the target similarity matrix based on first target hyperparameters to generate a target similarity query result value; performing weighted calculation on the target image query result value and the target similarity query result value based on second target hyperparameters to obtain a target classification result value; and the target classification result value is used to indicate a detection result of the image to be processed. Thus, the application can be more conveniently and quickly applied to various different application environments.

The invention relates to the technical field of database information retrieval, in particular to a length-independent set similarity query method based on a B + tree and application thereof. Through pre-construction of length partitions, key generation and index construction, in the query stage, length partition indexes are utilized, length filtering, key boundary generation and combined bucket difference filtering are used, key boundaries are directly generated, a large number of sets which are impossible to be similar are filtered, candidate sets are reduced, and therefore the query efficiency is improved. The objective of the invention is to solve the problem of how to reduce candidate sets so as to reduce the calculation cost in a multi-candidate-set scene.

The invention provides a vector data similarity query method and device, equipment and a medium, and the method comprises the steps: dividing a plurality of pieces of vector data to be queried into a plurality of data buckets, and storing the vector data included in each data bucket to a corresponding storage area; determining neighbor data buckets associated with the data buckets, constructing directed edges between the data buckets and the corresponding neighbor data buckets, and generating a data bucket dependency graph; based on the data bucket dependency graph and the capacity of the target cache, determining a processing sequence when the plurality of data buckets are written into the target cache; and sequentially writing each data bucket and the corresponding neighbor data bucket into the target cache from the storage area according to the processing sequence, determining the distance between two pieces of vector data respectively belonging to each data bucket and the corresponding neighbor data bucket, and screening vector pairs of which the distance is smaller than or equal to a preset threshold value from the plurality of pieces of vector data. Therefore, I / O waste is effectively reduced, and operation and maintenance and hardware investment are reduced.

The application provides a distributed index construction method based on improved iSAX coding, first, aiming at the digital feature selection problem, in order to increase the coding similarity of similar data, a similarity digital coding is designed, and through the transposition of the matrix, the problem of different weights of high bits and low bits of each digital coding in the whole digital coding is solved. Secondly, aiming at the problem of high time consumption in the traversal process caused by the too high index tree, a B+ index tree based on the similarity digital coding is designed, the number of child nodes is increased, the height of the tree is reduced, and the access speed of adjacent nodes is improved. And a leaf partition snake-shaped packing algorithm is designed, which guarantees the load balance, shortens the leaf node packing time, improves the index construction speed, and improves the overall index construction speed compared with the traditional distributed index construction algorithm, and provides a more efficient index framework for the similarity query process.

The invention discloses a method and a system for acquiring real-time data generated by retrieval enhancement. The method comprises the following steps of: adding a self-defined XML (Extensible Markup Language) tag into a static text fragment of an RAG (Registered Assistant Generation) vector database in advance; performing similarity query search based on the RAG vector database according to a received retrieval query request; judging an XML (Extensible Markup Language) tag in the vectorization result according to the searched vectorization result, and rendering the obtained XML tag; and based on the rendered XML tag, calling an external service to obtain real-time data corresponding to the vectorization result. Through the technical scheme, the problem of real-time acquisition of the dynamic data in the static content is solved, the function requirement for Function Calling of a large language model is avoided, the realization is simple, the problem of calling a large language for multiple times is avoided, and meanwhile, the cost for calling the large language model is saved.

This invention discloses a data-driven method and system for estimating selectivity using the `like` predicate, relating to the field of query selectivity prediction technology for relational databases. The method includes the following steps: receiving a query statement containing a `like` predicate and determining the type of the `like` predicate; constructing a trie, finding the corresponding trie based on the type of the `like` predicate to convert the `like` predicate query into a numeric range predicate query; performing a similarity query in a cardinality estimation result database based on the numeric range predicate query; if there are similarity query results exceeding a set threshold, directly reusing the retrieved cardinality estimation results; if there are no similarity query results exceeding the set threshold, performing cardinality estimation using a data-driven model. This invention enables rapid cardinality estimation for SQL queries containing `like` predicates.

The invention discloses a power grid malicious traffic detection method and system based on adaptive integration, relates to the technical field of power grid network security, and aims to solve the problem that it is difficult to effectively detect and encrypt malicious traffic in a power grid edge environment with limited resources in the prior art. The method comprises the following steps: a client collects flow data, monitors own resource state and performance indexes, packages the flow data and user requirements into a request, and sends the request to a server; the server constructs a query feature vector, performs similarity query in a historical strategy library to quickly reuse or finely adjust a strategy, and starts an adaptive integrated decision algorithm to generate an optimal integrated learning scheme if the strategy is not found; the scheme is issued to a client, and the client loads and operates and detects traffic characteristics; and when malicious traffic is detected, the client triggers a security response action. According to the technical scheme, on the premise of not decrypting the traffic, the detection strategy is dynamically adjusted according to the actual resources and demands, and the optimal balance between the detection effect and the resource consumption is achieved under the condition that the resources are limited.

The application provides a stock pattern similarity query system and method based on domain generalization, and relates to the technical field of deep learning.The system comprises a data layer, a matching model layer and an analysis layer.The application obtains stock historical data, pre-processes the data, then uses an RReliefF algorithm to perform feature selection and data segmentation, uses a Lasso algorithm in combination with a domain generalization method to train a model, matches similar stock data, and obtains result analysis for feedback.The application can more accurately match similar stock data, and provide more reliable auxiliary results for users.

Embodiments of the present application provide a shell identification method, a database establishment method, an apparatus and a storage medium. The shell identification method comprises: obtaining a feature set to be detected of a file to be detected; performing similarity query on the feature set to be detected in a preset database, and determining a sub-shell label set of the file to be detected according to a query result; and performing statistical analysis on the sub-shell label set of the file to be detected, and determining whether the file to be detected is a file with a shell. In this way, the similarity query on the feature set to be detected of the file to be detected by using the preset database can quickly and accurately identify whether the file to be detected is a file with a shell, so as to perform safety analysis on the file to be detected in a targeted manner, and the disadvantage that the file with a shell is difficult to analyze is remedied.

The present application belongs to the technical field of neutron diffraction analysis, and proposes a neutron diffraction vectorization generation and identification method. The generation method comprises the following steps: specifying a chemical system, generating all single elements and compounds by means of high-throughput calculation; removing repeated and unstable structures to generate neutron diffraction spectra of stable structures; mixing different concentrations of components to generate neutron diffraction spectra of mixed phases; generating one-dimensional vectors according to rules; performing half-peak broadening and noise enhancement processing on the simulation spectrum; extracting the features of the strongest diffraction peak to form a feature vector; and embedding the vector database to generate a simulation diffraction vector database. The method has the following beneficial effects: improving analysis efficiency through high-throughput calculation and vector database; enhancing generalization by not relying on specific chemical system training data; improving accuracy through data processing and correction; reducing manual intervention due to high automation; improving data quality through data preprocessing and feature extraction; and realizing rapid prediction through similarity query in the vector database.

The application discloses a TEE-assisted safe and efficient distributed high-dimensional data similarity query method, which comprises the following steps: constructing a TEE-assisted safe and efficient distributed high-dimensional data similarity query system and initializing the query system; a data owner constructs the data in a data set into a TPSS form and distributes the data to a first cloud server and a second cloud server; a PB tree is constructed by using the distributed data; when an update data is added or deleted in the data set, the first cloud server, the second cloud server and a trusted execution environment search the PB tree by cooperative operation to add or delete the update data; and according to a similarity query request proposed by a query agent, the first cloud server, the second cloud server and the trusted execution environment provide query services for the query agent. The application can support safe query index construction, efficient similarity query processing and data dynamic updating based on distributed high-dimensional data outsourcing.