29 results about "Functional semantics" patented technology

The invention provides a distributed storage method based on source code semantic partitioning, and particularly relates to the technical field of cloud data distributed storage. The method comprises the steps of performing semantic partitioning on a source code, and segmenting the source code into a plurality of semantic blocks according to dimensions such as functional semantics, an abstract syntax tree structure, author information and version information; generating metadata containing information such as grammar type tags, file paths, line number ranges, author identifiers, version identifiers and access popularity for each semantic block; constructing a weighted directed acyclic graph (DAG) based on the semantic chunks and the dependency relationship thereof; superposing a metadata layer in the DAG structure, and recording information such as function call dependency, inter-block reference relationship and version evolution chain; blocks with relatively high access frequency and close semantics are aggregated into super blocks, the traversal depth is reduced, and meanwhile, hot data and cold data are differentiated for hierarchical storage by adopting a cold and hot data management strategy; and evaluating a parent block aggregation degree through a BDS algorithm, determining a block sorting priority, and optimizing super block boundary division. Compared with the prior art, the method has the advantages that the semantic retrieval efficiency, the incremental updating capability and the distributed query performance of the source code storage system are improved.

The invention discloses a functional programming intelligent recommendation method and system based on a semantic knowledge graph, relates to the technical field of data processing, and constructs a high-dimensional semantic embedding space by performing semantic feature vector extraction on a multi-source functional programming corpus. The method comprises the following steps: firstly, embedding a plurality of candidate entity clusters into a semantic embedding space, preliminarily clustering the embedded vectors by utilizing a semantic aggregation pre-screening mechanism to obtain each candidate entity cluster and performing dynamic processing, then constructing a semantic relationship among the candidate entity clusters according to neighborhood distribution of the semantic embedding space, performing self-adaptive correction on inter-cluster distribution, and constructing a semantic knowledge graph generation recommendation method. A closed loop is formed from the corpus to the knowledge graph to the recommendation strategy, so that the intelligent recommendation system can accurately recognize the function semantic relationship, reduce noise interference and improve the recommendation hit rate and the personalized matching effect, and the accuracy, stability and user satisfaction of functional programming intelligent recommendation based on the semantic knowledge graph are remarkably enhanced.

A semantic robotic device includes a processor, transceiver, and a memory storing software applications. The processor is arranged to ingest user guidelines for the software applications, and to infer capabilities semantics associated with them. The processor further emulates user input device signals and communicates them to a target device to launch and operate the software applications, and to capture data from user interface controls. Semantic inference is performed on the data to determine semantic factorization with respect to a semantic goal, and to determine a software application which is more affirmatively factorized in rapport with the semantic goal. The processor emulates further input device signals and communicates them to the target device, based on the determination regarding affirmative factorization of the software applications.

The invention relates to a program vulnerability detection method based on redundant semantic compression and large language model enhancement, which comprises the following steps of: mapping an input source code into a semantic space, and dividing the semantic space into a functional semantic subspace and a redundant semantic subspace; guiding the large language model to automatically execute at least one of variable rewriting, redundant statement deletion and control structure replacement based on a predefined cue word; on the code samples subjected to semantic cleaning, enabling the large language model to generate function-related natural language descriptions through few sample prompt, encoding the generated natural language descriptions into vectors through a text embedding model, and performing feature fusion with original code embedding to obtain enhanced features; and inputting the enhanced features into a downstream deep learning vulnerability detection model, and realizing robust defense for backdoor attacks in training and reasoning. Compared with the prior art, the method has the advantages that the robustness of the vulnerability detection model to redundant semantics is improved under the condition that expert rules are not needed, the accuracy of the vulnerability detection model is effectively enhanced, and the success rate of backdoor attacks is reduced.

The invention relates to the technical field of artificial intelligence and bioinformatics, and discloses a biological function feature fusion method based on a layered weight perception attention mechanism. The method comprises the following steps: acquiring gene sequence data and multi-source function annotations, and converting into vector representation by using a pre-training model; fusing the functional semantics in the library into a library-level summary vector by adopting a hierarchical fusion mechanism of weight perception; constructing a hierarchical fusion architecture, and realizing deep adaptive fusion of a gene sequence and multi-source knowledge through a shared projection layer, a cross attention mechanism and a gating network; and the model performance is improved through end-to-end multi-objective optimization training. According to the method, the problems of semantic gap, insufficient weight utilization and biological logic deficiency in multi-source functional data fusion are solved, the unified gene function feature vector with high biological characterization capability is generated, and the accuracy and interpretability of gene function annotation are improved.

The invention discloses a molecular property prediction-oriented hierarchical guidance collaborative attention method, and relates to the crossing field of artificial intelligence and chemoinformatics. The method comprises the following steps: S1, constructing atomic-scale graph representation and group-scale graph representation of molecules; s2, providing top-down context guidance for atomic-scale attention calculation through the group-level semantic features; s3, dynamically adjusting the weight of cross-scale information fusion by using a context gating mechanism; s4, breaking the independence of each attention head in the traditional multi-head attention by adopting a multi-head collaborative attention mechanism; and S5, generating a molecular representation fusing the coarse-grained functional semantics and the fine-grained local structure for property prediction. According to the method, a hierarchical guidance collaborative fusion mechanism is introduced, the problem of information conflict caused by simple splicing or summation in a traditional multi-scale fusion method is solved, collaborative enhancement of atomic-level details and group-level functional semantics is achieved, the accuracy and robustness of molecular property prediction are remarkably improved, and the method has good application prospects. And an efficient multi-scale molecular representation learning tool is provided for drug discovery and material design.

This invention discloses an automatic analysis and intelligent porting system for embedded engineering driver libraries, relating to the field of embedded engineering. The system includes: an engineering parsing module for scanning embedded engineering directories, parsing target files, and generating an engineering structure semantic model. Compared with existing technologies, the advantages of this invention are: it automatically constructs an engineering semantic description structure and simultaneously generates a driver function semantic model, realizing a digital description of the engineering structure and driver capabilities; an intelligent matching module generates an adaptation mapping relationship based on feature alignment and similarity calculation, and a driver adaptation generation module automatically generates complete adaptation code accordingly, forming a driver adaptation layer. This system transforms the traditional driver porting process, which relies on manual experience, into a standardized and automated process, significantly reducing porting costs and error rates, improving cross-platform driver reuse efficiency, and realizing the intelligent and systematic nature of embedded driver porting.

The invention discloses a multi-view fusion cloud native malicious software detection method and system based on high-speed introspection of a virtual machine, and the method comprises the steps: 1), deploying a high-speed introspection module in a monitoring layer of the virtual machine, capturing an API call sequence of an internal process of a target virtual machine from the outside in a safe and low-invasion manner, and structuring the API call sequence into a runtime log; (2) the API calling sequence is regarded as a sentence, and a Word2Vec model is used for training to generate a structure embedding vector of the API; then, constructing a directed heterogeneous graph containing a file, a thread and API calling for each sample, taking the structure embedded vector as an initial feature of an API node, encoding the graph by using a graph attention network, and extracting a structure context feature vector; 3) extracting an API official function description text by utilizing the pre-training language model to generate a semantic embedding vector; constructing a directed heterogeneous graph for each sample, replacing the initial features of the API nodes in the graph with the semantic embedding vector, coding the graph by using the graph attention network again, and extracting a functional semantic feature vector; 4) firstly performing function classification on the APIs, and performing dimensionality reduction on the complete API calling sequence to obtain a limited function state sequence; constructing a Markov transition probability matrix for the state sequence of each sample, and selectively stacking a multi-order transition matrix to form a multi-channel feature tensor; inputting the feature tensor into a convolutional neural network for coding, and extracting a macroscopic behavior evolution feature vector; 5) splicing the structure context feature vector, the function semantic feature vector and the behavior evolution feature vector to form a final comprehensive feature vector; and inputting the comprehensive feature vector into a multi-layer perceptron classifier, and training the classifier in an end-to-end manner to enable an output sample of the classifier to be a classification result of malicious software or benign software.

This application relates to the field of artificial intelligence and discloses a spatiotemporal graph convolutional method for order demand prediction that integrates functional semantic similarity. The method includes: collecting multi-source heterogeneous data streams containing orders, delivery personnel poses, road conditions, and the environment; constructing a unified grid feature vector representation through spatiotemporal gridding processing; performing order demand prediction using a spatiotemporal graph convolutional network that integrates spatial adjacency edges and functional semantic similarity edges; capturing spatiotemporal correlation features and generating prediction results through a multi-head attention mechanism and gated recurrent units; encoding the prediction results as state vectors and inputting them into a reinforcement learning policy network to output optimal allocation instructions; triggering a pre-scheduling mechanism based on the prediction results to guide idle delivery personnel to move to high-demand grids before orders are generated; performing distributed computing using an edge-cloud collaborative architecture; and dynamically adjusting and optimizing weights and performing anomaly detection and emergency recovery through a fuzzy logic controller.

The present application provides a method and system for collaborative acceleration of heterogeneous computing power resources for AIGC, which relates to the field of data processing technology. The method includes: inputting an activated AIGC model, determining the actual execution task subgraph, and performing model parsing of the task subgraph; layering the task subgraph using structural semantics and functional semantics; extracting operator features under dual-granularity constraints based on the operator sharding processing results and layered labels; performing computing power state collection of heterogeneous computing power resources, using multi-dimensional feature sets and computing power state feature sets as matching features, performing matching optimization between heterogeneous computing power resources and operators, configuring scheduling response plans; and performing collaborative acceleration management. This application solves the technical problem in the prior art of low operator scheduling efficiency during the execution of the AIGC model due to the strong dynamic nature of the computing power state of heterogeneous computing power resources. By realizing dynamic scheduling of heterogeneous computing power resources and model operators, the utilization rate of heterogeneous computing power resources is improved.

This invention relates to a method for constructing a disaster-bearing entity based on a structure-function-behavior model, comprising the following steps: S1 entity structural semantic modeling; S2 entity functional semantic modeling; S3 behavioral semantic modeling; S4 constructing a semantic fusion module and building urban natural disaster risk factor data and knowledge data, and using the risk factor data and knowledge data to fuse the semantics formed in the models built in steps S1-S3 to realize the construction of the disaster-bearing entity. The method also includes a data and mechanism-driven twin model update method, specifically including: updating the twin model using a change detection model, and update management, wherein the change detection model includes the detection of structural and semantic changes. The method also includes a disaster risk prediction method, which uses a hybrid approach consisting of decision trees and node data formed by urban natural disaster risk factor data and knowledge data. This achieves data analysis and prediction of urban entity disaster situations with visualization of the sum of structure-function-behavior factors.

The application belongs to the field of high-end equipment manufacturing and intelligent manufacturing, and provides a three-dimensional semantic feature extraction method and system for machine case parts, wherein the method collects multi-view point clouds through a fixed line laser scanning head and a high-precision servo rotating platform, and arranges reference mark points; combined with the rotationally symmetrical axis extracted by principal component analysis and the mark points, coarse registration, fine registration and point cloud fusion are performed to generate a complete machine case surface three-dimensional point cloud model; based on the topological category identification of the machine case surface three-dimensional point cloud model, a geometric template is loaded, global parameters and local features with function semantics are extracted, and a structured three-dimensional semantic feature set is organized. The method can directly drive digital prototype updating, realize high-fidelity mapping from a physical entity to a digital model, and support virtual verification and closed-loop optimization in high-end equipment research and development.

The application provides a self-development method and system based on a morphogenetic field, and a storage medium. The self-development method comprises the following steps: receiving a user intention, converting the user intention into a structured intention message; generating a functional semantic manifold and a mixed manifold space, and constructing a morphogenetic field; determining an implementation mode of a task target; decomposing the task target into a plurality of design tasks, and determining a design task of each morphogenetic engine; for each local state field, a corresponding morphogenetic engine is used to solve it; determining whether a cooperative stable state is reached among the plurality of morphogenetic engines; after the cooperative stable state is reached, collecting achievement data of the morphogenetic engine, and generating a standardized delivery package. Through intention analysis and construction of a morphogenetic field, the hardware and software implementation mode of the task target is objectively determined, the subjective bias of manual disassembly is eliminated, and the delivery process from abstract intention to standardized engineering products is realized.

The invention discloses an operation planning method and system of a self-moving robot and related equipment. The operation planning method of the self-moving robot comprises the steps that historical object behaviors and historical equipment states are acquired; on the basis of relevance between historical object behaviors and historical equipment states, functions corresponding to all the operation sub-areas are determined to obtain area function semantics, and activity categories corresponding to the target objects are determined to obtain object activity semantics; collecting real-time object behaviors, and predicting the intention of the target object in combination with the real-time object behaviors, regional function semantics and object activity semantics to obtain an intention prediction result; and generating an operation plan of the self-moving robot based on the intention prediction result. Through correlation analysis of the behavior data and the equipment state data, understanding of high-level semantics such as regional function semantics and object activity semantics is realized, the intention prediction accuracy is improved, and then active and forward-looking operation planning can be performed according to an intention prediction result.

This invention provides a Web API recommendation method based on functional semantics and structural interaction, belonging to the field of Web API recommendation technology in the Web environment. It solves the problem of excessively short descriptions of existing Mashup service requirements. This Web API recommendation method based on functional semantics and structural interaction includes the following steps: Step S1: Collect description data from existing Mashup applications and their related API services, and extract textual description information of the Mashup applications and API services; Step S2: Preprocess the textual description information of the Mashup applications and API services; Step S3: Construct a Mashup-API bipartite graph based on the collaborative relationships between API services used in the Mashup application; Step S4: Construct functional semantic components and extract functional semantic features from the textual description information; Step S5: Construct structural interaction components and extract potential implicit correlations in sparse interactions; Step S6: Integrate multi-model training and integrate different types of feature descriptions. This invention has advantages such as reducing the impact of data sparsity on recommendation results.

The application discloses a knowledge graph-based local livestock and poultry germplasm resource popular science content intelligent generation method, and particularly relates to the technical field of computer information processing, and is used for solving the problem that the existing knowledge graph-based popular science content generation method cannot automatically establish the analogy correlation between breed traits and common sense concepts, resulting in insufficient popularization of the popular science content; the method is realized through the following steps: acquiring trait attributes from a local livestock and poultry germplasm resource knowledge graph and identifying comparable attributes, using the mirror image reverse search evaluation function of the semantic opposite relationship in the common sense knowledge base to describe the cognitive coverage degree to eliminate attributes that are not suitable for analogy, respectively searching the common sense knowledge base based on the physical dimension and the functional semantics of the retained comparable attributes and respectively sorting, screening candidate analogy items based on the statistical significance of the sorting consistency and establishing an analogy mapping relationship, and finally filling the analogy mapping relationship into a popular science narrative template to generate a popular science content text with analogy.

The invention relates to the technical field of computers, discloses an architecture description language-based software and hardware collaborative design automatic generation method and system, and aims to solve the problems of model splitting, description redundancy, difficulty in guaranteeing consistency and insufficient software and hardware collaborative optimization in the existing special processor design. The method comprises the following steps: constructing a unified multi-level semantic intermediate representation comprising a functional semantic layer, a micro-architecture mapping layer and a physical resource layer through unified architecture description language input; and generating a hardware register transfer level code, a software tool chain and a co-verification environment in parallel based on the intermediate representation. The system comprises a front-end parser, a software tool chain generation engine, a hardware code generation engine and a co-verification environment generation engine. According to the method, full-automatic generation of software and hardware is realized through a single truth source, the behavior consistency is ensured, the design threshold and period are remarkably reduced, and flexible optimization and efficient verification of a micro-architecture are supported.

The application discloses a kind of modernization reservoir operation management matrix platform construction method and system based on semantic role self-adaption, belongs to the technical field of intelligent water conservancy.It realizes intelligent permission adaptation by semantic role self-adaption mechanism.Core steps include: function semantic modeling is carried out to each business function unit of platform, and function semantic label is configured;Responsibility semantic modeling is carried out to user role, and standardized post responsibility description text is configured;The semantic of both is vectorized using pre-training natural language processing model;Calculate semantic similarity based on cosine similarity algorithm, and determine the candidate business function set corresponding to each role through threshold determination;According to the preset mapping relationship, the matched function semantic label is converted into operation permission set;Finally, the personalized reservoir operation management workbench interface is dynamically generated.The corresponding system includes five modules of matrix platform function library, role responsibility management, function recommendation, permission configuration and personalized workbench generation.

The application discloses a medical health service demand classification method based on similarity, and comprises the following steps: step one, data preprocessing; step two, training a global semantic vector; step three, training a word vector; step four, calculating a function semantic weight FunSemWeight; step five, keyword extraction; step six, training a theme semantic vector; step seven, calculating similarity and selecting a kernel function; and step eight, constructing a support vector machine classifier. When the similarity is calculated, the understanding of user demand is improved through more accurate semantic vector representation, the theme semantic vector is combined on the basis of the global semantic vector, theme information is supplemented, the recognition ability for a specific field is improved, and the accuracy of similarity calculation is improved, so that classification is more accurate.

This invention relates to the field of computer technology, and in particular to a method and apparatus for automatic generation and verification of test cases based on a large language model. The method includes: parsing an onboard software requirements document to generate a structured requirements text; receiving onboard software source code, parsing the source code to extract function information for each function, and generating a functional semantic summary for each function, thereby forming a summary function data set integrating function information and corresponding functional semantic summaries; establishing a semantic correspondence between requirements and code based on the structured requirements text and the summary function data set to generate a requirements-code matching list; generating a test case set that satisfies the requirements constraints based on the structured requirements text, the summary function data set, and the requirements-code matching list; executing the test cases in the test case set and generating a test report, thereby solving the problems of low efficiency and strong reliance on manual intervention in traditional solutions.

The invention discloses an application programming interface recommendation method and device of an application program development technology. The method and device are used for solving the technical problem that web API recommendation is inaccurate due to an existing mainstream web API recommendation method. The method comprises the following steps: acquiring a demand document, an affiliated type and selected API information of a to-be-developed application program, and simultaneously acquiring interactive adjacency matrixes of the application program and the API as well as the application program and the affiliated type, and metadata (including function description, the affiliated type and API calling information) of the existing application program; the functional semantic similarity is calculated by presetting a semantic component, based on the demand description and the selected API of the to-be-developed program and in combination with the functional description and the calling API of the existing program. And calculating the interaction similarity by utilizing a preset interaction component according to the interaction adjacency matrix, the category nodes to which the to-be-developed program and the existing program belong and the calling API information. And finally, integrating the two similarity indexes, and selecting a target calling interface from the calling API information of the existing program.

This invention relates to the field of module recognition, and more particularly to a method and system for recognizing and configuring a beverage robot. The method includes the following steps: when a new physical functional module contact signal is detected, configuration protocol information is read, and the robot enters a module confirmation state; the functional execution code of the physical functional module is downloaded, abstracted and encapsulated, and a functional semantic data package is constructed; the functional semantic data package is sent to the activated robot to enter a pre-configured operating state; transient response data of the robot when the functional module is inserted is collected, and a current ecological impact assessment is performed to generate an impact feature set; configuration parameters are adjusted and matched based on the impact feature set, and the configuration state is executed to complete the module configuration operation. This invention enables the rapid, safe, and efficient insertion, removal, and operation of new functional modules in a beverage robot.

A system for treaty-governed genomic compilation and secure biological synthesis that regulates the translation of digital genetic information into physical matter. The apparatus comprises a Symbolic Verification Kernel configured to deconstruct target nucleic acid sequences into functional semantic representations and execute predictive biological simulations to assess toxicity and dual-use risk against an immutable Treaty Ledger. Physical synthesis is controlled by a hardware-locked DNA synthesis apparatus that remains mechanically disabled until it receives and validates a cryptographically signed Sovereign Synthesis Token issued by the kernel. The system further includes a Bio-Encryption Engine that interleaves non-coding watermarks into the synthesized material, establishing a permanent, molecular-level chain of custody for all fabricated genetic content. The architecture thereby enforces international biological safety constraints directly at the point of manufacture, preventing the unauthorized synthesis of pathogenic or restricted biological agents.

The application discloses a BIM-based digital twin model creation method, comprising: S1 semantic fingerprint evaluation and completion, calculating a semantic fingerprint vector from three dimensions of geometric integrity, attribute integrity and relationship integrity to identify and complete an unready component; S2 intention graph clipping and enhancement, querying an intention graph according to a target application scenario to clip a minimized submodel and performing directional semantic enhancement; S3 semantic geometry decoupling construction, extracting a building topology skeleton to construct a semantic topology layer and a geometric performance layer respectively, and outputting a decoupled multi-resolution digital twin model; and S4 function semantic inference injection, inferring implicit function information based on a function space semantic graph and a building function knowledge base and injecting the semantic topology layer. The application improves the data quality, scene adaptability and semantic depth of the digital twin model.

The invention provides an Android application detection method and system based on differential embedding and a program product, and relates to the technical field of Android applications. According to the method, a function call graph is constructed through static analysis, important function nodes with discrimination are recognized in combination with a malicious sample training set, then key sub-graphs with neighborhood structures are extracted and reserved, and core call relations related to malicious behaviors are effectively reserved while original graph redundancy is greatly reduced; further, differentiated semantic embedding strategies are designed for internal function nodes and external function nodes, the former is based on statistical characteristics of instruction types, and the latter is based on character numeralization characteristics of semantic segmentation of method names, so that functional semantics of different nodes are described more accurately; and finally, the key sub-graphs with simple structures and rich semantics and the nodes of the key sub-graphs are embedded into the neural network of the input graph for classification, so that the accuracy, the robustness and the calculation efficiency of Android application malicious detection are remarkably improved.

This invention discloses a method and system for fusing diverse heterogeneous data based on a BIM model, belonging to the field of building information data processing technology. The method includes: constructing different types of initial functional semantic fields; building a fusion computing environment integrating data import, field calculation, and result caching based on calibrated functional semantic field data; performing conflict detection and data verification on the functional semantic fields in the comprehensive field strength data based on preset conflict judgment and verification rules, identifying and marking conflict locations and conflict levels; and converting the unified functional semantic field into a format suitable for multiple terminals based on multi-terminal characteristics and interaction requirements, constructing standardized scene labels adapted to multiple terminals. This invention transforms BIM semantic features into physical constraints and combines path reasoning to achieve deep binding of semantic and physical information across modal data, forming a unified functional semantic field with clear physical connotations, thus realizing the leap from morphological fusion to functional semantic fusion of diverse data.

The invention discloses an operating system dependency structure cross-version evolution detection method and device, and belongs to the technical field of Web operating systems. According to the method, after metadata of a multi-version software package is preprocessed, a cross-version isomorphic dependency graph is constructed, and node source and target embedding vectors are obtained through an HOPE algorithm; and in combination with source vector function semantic similarity and neighborhood Jaccard similarity, an intermediate layer candidate packet is screened, and then evolution types such as a reconstruction type and a migration type are judged through a target vector, so that accurate detection of dependent structure cross-version evolution is realized.

The invention relates to the technical field of parameter configuration, in particular to a programmable audio parameter configuration system which comprises a label boundary extraction module, a semantic field binding module, a structure block sorting module, a configuration parameter path matching module and a control command writing module. According to the method, the voice instruction text is automatically extracted, the effective statement boundary is recognized, functional semantic classification of the configuration command is achieved by combining annotation structure analysis, command and physical resource mapping is completed according to the component configuration path in the unified extensible firmware interface, and a configuration block set capable of being directly executed is formed; a controller command interface is called to complete configuration data real-time write-in and write-in result comparison, each command change field is recorded in the process, a filing track is formed, automatic closed-loop processing of the whole process from command extraction to write-in response is achieved, manual intervention and format errors are avoided, the configuration efficiency and parameter verification precision are improved, and the method is suitable for large-scale popularization and application. The cross-role cooperation frequency is reduced, and the audio debugging response speed is improved.