352 results about "Dependency information" patented technology

The invention belongs to the technical field of bearing residual service life detection, and discloses a rolling bearing residual service life prediction method and system based on a convolution white box. According to the method, time domain and frequency domain feature extraction, noise reduction processing and health state and degradation state division are carried out on an original vibration signal, and deep neural network training is carried out in combination with a Weibull-MSE loss function, so that prediction of RUL conforms to the actual degradation process of a bearing; and synchronous extraction of long-time dependence information and local degradation characteristics of the bearing signal is completed through a convolution CRATE network architecture fusing an expansion causal convolution attention mechanism DCA and a multi-scale convolution MSC. According to the method, the accuracy of RUL prediction is improved in combination with health state evaluation, the local feature extraction capability is enhanced, the local modeling capability of the model is improved through multi-scale information fusion, and the interpretability and the prediction credibility are improved through a CRATE structure.

The invention relates to a power supply quantity self-repairing method based on deep learning, and the method comprises the following steps: S1, obtaining power multi-dimensional data through an intelligent sensor and terminal equipment, and carrying out the preprocessing of the data; s2, constructing a power network graph model based on the power multi-dimensional data, and optimizing the power network graph model by introducing a graph attention mechanism and a time dynamic model to obtain power grid topology dynamic dependency information; s3, based on the processed electric power multi-dimensional data and power grid topology dynamic dependency information, using deep learning to fuse multi-modal information, detecting abnormal behaviors of power supply quantity, and determining abnormal types and grades; s4, according to key nodes identified in the power network graph model and an abnormal grading result, dynamically distributing a repair priority, and outputting a repair value; and S5, performing restoration based on the restoration value and a predetermined execution sequence, and returning the power supply curve to the power dispatching center after restoration is completed. According to the invention, rapid positioning and grading of power supply abnormity and accurate generation of a restoration scheme are realized.

The invention discloses a special reasoning model-oriented data set construction method, and particularly relates to the technical field of data set construction. According to the method, domain task text information, rule constraint information and causal dependency information are analyzed, and a dependency structure chart of a target reasoning task is constructed; on the basis of the dependency structure diagram, topology path expansion and anti-fact topology path backtracking analysis are carried out according to the semantic relation direction and the constraint reverse relation, forward path topology feature data and reverse topology verification data are formed, and therefore a forward and reverse dependency consistency matrix is established for topology conflict recognition, and a consistency correction result is output; and finally, according to a consistency correction result, carrying out topology label labeling, topology equivalence judgment and multi-path admission screening to obtain a topology fidelity sample set, and carrying out dependent signature labeling and topology label coding to generate a special reasoning model training data set of the target reasoning task. And the reasoning path reliability and the data set structuring level of the special reasoning model are improved.

The invention relates to a rendering method and device, a storage medium, a controller and a program product. The method comprises the following steps: constructing a directed acyclic graph of to-be-rendered components based on dependency information between the to-be-rendered components in an application program; and determining a rendering sequence of the to-be-rendered components based on the directed acyclic graph, and rendering the to-be-rendered components according to the rendering sequence. According to the application, the directed acyclic graph of the to-be-rendered components can be constructed through the dependency information between the to-be-rendered components; according to the embodiment of the invention, the rendering sequence of the to-be-rendered components can be determined through the directed acyclic graph, and the to-be-rendered components can be effectively rendered according to the rendering sequence determined by the directed acyclic graph because any two to-be-rendered components in the directed acyclic graph do not have a cyclic dependency relationship; in the embodiment of the invention, each to-be-rendered component is sequentially processed according to the rendering sequence, so that each to-be-rendered component can be correctly rendered, the rendering efficiency and the rendering accuracy are improved, and an expected visual effect is realized.

The invention discloses a multi-mode communication protocol optimization method based on deep learning, and the method comprises the following steps: S1, collecting operation data of a communication terminal, and carrying out the preprocessing of the operation data; s2, constructing a multi-mode communication protocol structure diagram, and generating an adjacent matrix; s3, constructing a structured LSTM model, and modeling time sequence characteristics of a communication environment and structural dependence information between protocols; s4, performing performance prediction by using the structured LSTM model; s5, constructing a protocol cause subgraph between the time sequence feature vector and the performance prediction vector; and S6, performing multi-target comprehensive evaluation on the communication protocol, and determining an optimal communication protocol configuration scheme. According to the method, structured deep learning and causal modeling are fused, a multimode communication protocol selection process is optimized, and the method has the advantages of high prediction precision, high decision interpretability and good environmental adaptability.

The invention provides an intelligent recommendation scheduling method and system for training resources, and relates to the technical field of data processing, and the method comprises the steps: obtaining a position data set and a resource data set, determining a subject association relationship and a position mapping relationship according to the position data set and the resource data set, extracting subject constraint information and position dependence information, performing time matching on the training demand duration and the resource available duration of each subject to obtain a candidate scheduling pair group set, performing resource conflict detection, removing conflict pair groups in the candidate scheduling pair group set, sorting the candidate scheduling pair groups according to position dependence information, extracting a candidate resource set corresponding to each subject, calculating a score value of each candidate resource to obtain a resource score set, and performing resource scheduling on the candidate resource set according to the score value of each candidate resource. And selecting the resource identifier with the maximum score value in each subject as a recommended resource to obtain scheduling plan data. According to the invention, the resource scheduling can be comprehensively judged under the condition of limited resources.

The invention provides an agent evaluation method and device, an agent, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of large models, intelligent search, smart questions and answers and the like. The agent evaluation method comprises the steps that according to received evaluation demand information, evaluation task planning is conducted on a specified agent through a large model, an evaluation sample is obtained, the evaluation sample comprises sample demand information and environment dependence information, and the specified agent executes a specified task on the sample demand information according to the environment dependence information, outputting an execution result matched with the sample demand information, wherein the environment dependence information is used for prompting an environment condition on which the specified agent depends on executing the specified task; and evaluating the specified intelligent agent according to the execution result to obtain an evaluation result.

The embodiment of the invention provides a document processing method and device and a storage medium, and the method comprises the steps: loading a target component in an editing interface of a document page through a client in response to a selection instruction for the target component in a preset component library, displaying a component editing region and a component preview region corresponding to the target component in the editing interface, displaying the target component in the component preview area; in response to an editing instruction for the target component in the component editing area and / or the component preview area, updating the target component displayed in the component preview area; in response to the document page storage instruction, generating description data and dependency information of the document page; and in response to the document display instruction, obtaining a webpage file corresponding to the document page according to the description data of the document page and the dependency information, and displaying the webpage file corresponding to the document page. According to the embodiment of the invention, document editing can be carried out more conveniently and visually, the document quality is improved, the document editing efficiency is improved, the labor cost is reduced, and the user experience is improved.

The invention provides a cross-modal feature fusion method based on joint attention, which combines multi-modal data from different sources to predict the relative position of a target to be tracked, and comprises the following steps of: firstly, designing a cross-modal data representation network model for the data of different modals; generating a time sequence high-dimensional implicit code retaining key dependency information from the original data through a code conversion algorithm; secondly, a heterogeneous attention network unit is designed, and features of time sequence high-dimensional implicit codes corresponding to different modal data are extracted through two different forms of attention; and finally, through a cooperative training mechanism, information complementation among different modal data is utilized to generate the most effective feature representation for a time sequence prediction model, and accurate prediction of the moving position of the tracked target is realized.

The invention relates to a risk assessment method, device and equipment for an open source software dependency relationship. The method comprises the following steps: acquiring software package attribute information of each open source software package in a project to be evaluated and dependency information between the open source software packages; according to the software package attribute information of each open source software package and the dependency information between the open source software packages, constructing a project graph model for the to-be-evaluated project; wherein nodes in the project graph model represent open source software packages, connecting edges among different nodes represent dependency relationships among different open source software packages, and node information of each node comprises software package attribute information of the open source software package corresponding to the node; the side information of each connection side comprises dependency information between the open source software packages corresponding to the nodes connected with the connection sides; and based on the risk prediction model, according to the project graph model, determining a risk assessment result of the to-be-assessed project. By adopting the method, the risk of the project can be fully predicted and mined.

The invention relates to a task processing method and device of a power grid dispatching cloud platform based on multiple computing resources, computer equipment, a storage medium and a computer program product. The method comprises the following steps: performing task decomposition processing on a to-be-processed calculation task to obtain a plurality of sub-calculation tasks; extracting a first feature vector of calculation complexity of each sub-calculation task, a second feature vector of task data volume, a third feature vector of response timeliness information and a fourth feature vector of hardware dependence information, and performing fusion processing to obtain a fusion feature vector of each sub-calculation task; inputting the fusion feature vector into a trained computing resource prediction model to obtain a target computing resource corresponding to each sub-computing task; and determining a resource load value of the target computing resource, and performing corresponding task processing on the corresponding sub-computing task according to the target computing resource under the condition that the resource load value is smaller than a resource load threshold value. By adopting the method, the processing efficiency of the calculation task can be improved.

The invention discloses a task allocation method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining a to-be-processed task from a global task pool, and analyzing a descriptor of the to-be-processed task to obtain priority information and / or dependency information; determining a processing sequence of the to-be-processed tasks by utilizing the priority information and / or the dependency relationship information; obtaining channel load information from a state register for directly accessing the corresponding channel from the memory; and selecting a target channel from the channels by utilizing the channel load information and combining a load balancing strategy, and distributing the to-be-processed tasks to the target channel according to the processing sequence. According to the task allocation method and device, during task allocation, based on at least one of the priority information and the dependency relationship information and the channel load information, load balancing can be dynamically achieved, at least one of the priority and the dependency relationship of the task can be considered, and therefore the reliability of task processing is guaranteed, and the task allocation efficiency is improved. And different task scheduling requirements can be dynamically met.

The invention relates to a method, a device and equipment for identifying ancient characters of a war bamboo slip, and a medium. The method comprises the following steps: acquiring a to-be-identified war bamboo slip image containing a plurality of ancient characters; flattening a local feature graph containing local detail information of ancient characters in a to-be-recognized war national bamboo slip image output by the OfficientNetV2 convolutional neural network into a sequence structure, and splicing the sequence structure with a feature sequence containing global semantic information and long-range dependency information of the ancient characters output by a Transform encoder in a channel dimension to form a joint feature tensor; layer normalization processing is carried out; and inputting the combined feature tensor subjected to layer normalization processing into a classification module of an ancient character recognition model so as to output a single character recognition result corresponding to a single ancient character or character sequence recognition results corresponding to a plurality of continuous ancient characters. Compared with the prior art, the method has better supplementing and repairing capacity when processing defective and broken strokes, and the recognition capacity for characters with incomplete structures or complex forms is remarkably improved.

The invention relates to an information display method and device, computer equipment, a readable storage medium and a program product. The method comprises: when a first target transaction is in a lock waiting state, in response to dependency information sent by a target node, sending an information acquisition request to a second node to instruct the second node to acquire dependency information in an auxiliary process and send the dependency information to a first node; wherein the dependency information comprises that the first target transaction is in a lock holding state; generating a corresponding graph data structure on the basis of dependency information stored in the graph data structure and the received dependency information; points in the graph data structure comprise transaction information, and edges in the graph data structure comprise lock waiting relation information between transactions; and in response to the loop existing in the graph data structure, displaying transaction information contained in the loop and lock waiting relationship information between transactions. By adopting the method, the deadlock removing efficiency can be improved.

The invention discloses a power robot abnormal target detection method based on improved YOLOX. According to the method, image data shot by an electric power robot are acquired, an improved YOLOX model which introduces a multi-scale cross-level local network MS-CSPNet and a small target decoupling detection head is used for processing an image, and accurate detection and positioning of an abnormal target are realized. The MS-CSPNet enhances the feature expression ability of a small target through multi-path convolution, a decoupling detection head is combined with deep convolution, expansion convolution and 1 * 1 convolution, context and long-range dependency information is effectively extracted, classification and regression task separation is achieved, and the detection precision is improved. Each power robot model is continuously optimized based on a federated online incremental learning algorithm, multi-terminal cooperative training is supported, adaptive updating of the model is realized while data privacy is guaranteed, and the method is widely applied to power equipment state monitoring and fault early warning scenes.

A computer-implemented system analyzes program and data flows in a software system comprising code written in multiple programming languages using a generative large language model (LLM) directed by programming-language-specific prompts. The LLM identifies functional components within the code, generating labeled graph nodes that include a node type, a node name, and dependency information. A graph construction computer system processes the labeled graph nodes to generate a directed graph, where nodes represent functional components and directed edges represent dependencies. The system stores the graph in a database and provides a web-based interface for visualization, allowing users to explore, query, and analyze program and data flows across the software system. The system enables automated, language-agnostic dependency mapping, facilitating software analysis, debugging, and modernization.

The invention provides a test program generation method, electronic equipment and a storage medium, relates to the field of chips, and is mainly technically characterized by comprising the following steps: generating a plurality of subprograms corresponding to each module in at least two modules according to instruction characteristics of the at least two modules in a target processor; wherein each subprogram comprises dependency information with other subprograms, each subprogram comprises a plurality of instruction fragments, each instruction fragment comprises at least one instruction, and the dependency information of the subprogram is used for indicating whether the instruction fragment in the subprogram conflicts with the instruction fragments in other subprograms or not; the to-be-used instruction fragments are continuously selected until a preset ending condition is met, and the next to-be-used instruction fragment in every two adjacent to-be-used instruction fragments is the to-be-used instruction fragment which is sequentially selected from any subprogram of any module and does not conflict with the previous to-be-used instruction fragment; and obtaining a target test program according to a selection result of the to-be-used instruction fragment.

This invention discloses a method and system for hyperspectral image denoising based on a spatial-spectral joint self-attention mechanism, belonging to the field of image processing technology. First, based on the characteristics of hyperspectral images, a spatial-spectral joint self-attention mechanism network is constructed. The noisy hyperspectral image is used as input to the network to extract spatial-spectral features. Then, a global spectral self-attention mechanism is used to extract the band correlations of the hyperspectral image. Finally, the extracted spatial-spectral features are reconstructed using a multiple perceptron and residual connections to reconstruct a clean, noise-free hyperspectral image. The system includes a feature extraction subsystem, a non-local spatial self-attention subsystem, a global spectral self-attention subsystem, and an image reconstruction subsystem. This invention can effectively restore noisy hyperspectral images to obtain noise-free hyperspectral images. Compared to convolutional networks, it can better model long-range dependency information and has better adaptability to target hyperspectral images.

The invention relates to a load prediction method TSR-MAEN based on decomposition representation and an attention encoder network. Firstly, the algorithm provides a trend-seasonal decomposition representation method based on comparative learning so as to enhance the decoupling performance of a model on a trend component and a seasonal component of a load sequence and the representation capability of decomposition characteristics. Secondly, in order to deeply mine and decompose time sequence information contained in a feature hidden space and dependency between features, the algorithm proposes a mixed attention encoder network; through segmented embedding, a trend-season dynamic projection mixed self-attention mechanism and a multi-head mixed output mechanism, trend and season multi-dimensional features after load sequence decomposition are effectively utilized to express and enhance the long-range dependence information extraction capability of the model. Experiments show that the TSR-MAEN algorithm provided by the invention has excellent performance in an electrical load prediction task of oil and gas development, and the prediction error is reduced by 8.51% compared with that of an optimal baseline algorithm.

The invention relates to a patent entity relationship extraction method and device, equipment, a medium and a product, in particular to the technical field of natural language processing. Comprising the steps that a target patent text is obtained and preprocessed, a corpus is constructed, and the corpus comprises marked and classified text entities and semantic relations between the text entities; based on a corpus, semantic information is obtained through a sequence coding module; inputting the semantic information into a graph convolutional network coding module based on a label attention mechanism to obtain structure dependence information output by the graph convolutional network coding module based on the label attention mechanism; and inputting the structure dependence information into a relation prediction module to obtain a subject-object relation output by the relation prediction module. The corpus is constructed through preprocessing, hidden features can be deeply mined through multi-module collaboration, and the extraction efficiency and accuracy of unstructured text knowledge are improved.

The invention discloses a software outsourcing-oriented heterogeneous code warehouse intelligent coupling degree detection system and method, and relates to the technical field of software engineering.The method comprises the steps that an instruction is detected, and code file information, dependency information and development log information are obtained through a distributed crawler module, an abstract syntax tree analysis tool and a natural language processing module; carrying out modular segmentation on the code file by utilizing semantic coupling analysis, inputting the code file into a large language model equipped with a LoRA adapter, identifying a coupling inverse mode, and carrying out preliminary optimization based on a quantum heuristic coupling optimization algorithm; after optimization, a multi-user concurrent scene is simulated through digital twin simulation, and the coupling degree is dynamically detected; meanwhile, a cross-language interface testing tool is used for testing interface compatibility and call chain coupling; the system comprises an information acquisition module, a semantic coupling analysis module, a quantum optimization module and a coupling degree detection module, provides a deep optimization basis for developers, and improves the coupling degree detection and optimization efficiency of the heterogeneous code warehouse.

Apparatuses and methods for a CSI report configuration for CSI predictions in one or more domains. A method performed by a user equipment (UE) includes transmitting capability information indicating capability of the UE to support machine learning (ML) based channel state information (CSI) prediction in one or more domains, receiving configuration information that indicates parameters for ML based CSI prediction in the one or more domains; receiving CSI reference signals (RSs), and measuring the CSI-RSs. The method further includes determining, based on the configuration information and the measured CSI-RSs, a plurality of CSI predictions in the one or more domains; determining a CSI report including one or more of the plurality of CSI predictions and dependency information indicating dependencies between CSI predictions in the plurality of CSI predictions; and transmitting the CSI report.

The invention relates to the technical field of data processing and the field of insurance businesses and smart medical treatment, and provides a code review method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining code change information; performing context-aware retrieval processing on the code change information based on the code multi-dimensional knowledge base to obtain code context dependence information; checking the code change information and the code context dependence information based on a code checking module to obtain initial code comment information; screening the initial code comment information based on a preset value filtering module to obtain screened code comment information; performing adoption evaluation processing on the screened code comment information to obtain code correction evaluation information; and performing optimization processing on the code knowledge graph, the code checking module and the value filtering module according to the code correction evaluation information. Through the technical scheme, the code can be intelligently reviewed and processed, and the code review efficiency is improved.

The invention relates to a cross-view positioning method and system based on hierarchical gating fusion and sequence space aggregation, and belongs to the field of computer vision. The method comprises the following steps: extracting three different levels of feature maps from a pre-trained visual model as the input of a gating residual fusion module; a final fusion feature map is obtained through a gating residual fusion module; and finally fusing the feature map to obtain a feature vector through a spatial displacement and selective state space joint aggregation module. According to the method, the problem that the overall information expression is insufficient due to the fact that feature fusion lacks content self-adaption and interlayer guidance and feature aggregation cannot keep local geometry and long-range dependency information at the same time is solved.

The invention discloses a code review method and device, electronic equipment and a storage medium. Comprising the steps that a full-amount source code file of a target project is obtained from a code repository according to a code change event, multiple programming languages and dependency information corresponding to the programming languages are determined based on the full-amount source code file, and each source code file in full-amount source codes corresponds to one programming language; determining at least one to-be-reviewed source code file in the plurality of source code files, and extracting annotation information in each to-be-reviewed source code file; determining a cue word template corresponding to the to-be-reviewed source code file, and determining cue words of the to-be-reviewed source code file based on the programming language, the dependency information, the annotation information and the cue word template corresponding to the to-be-reviewed source code file; and processing each to-be-reviewed source code file and the corresponding cue word based on a preset large language model to obtain a code review result corresponding to each to-be-reviewed source code file. Flexible construction of the cue word is realized, and the accuracy of code review can be improved.

The invention discloses an unsafe behavior risk portrait generation method based on multi-modal data fusion and dynamic visualization, and relates to the technical field of safety management and risk assessment, and the method comprises the following steps: continuously collecting original data from different modal data sources in real time through network communication; and it is ensured that information dependent on the whole fusion process has freshness and integrity. According to the method, the high-frequency low-value redundant data is effectively identified by sensing and dynamically regulating and controlling the data source information value in real time, the fusion result is prevented from being interfered by the redundant mode, and the dominant position of the key mode information in the risk portrait is improved. The dynamic adjustment of the contribution degree and a self-adaptive recovery mechanism are fused to enhance the response capability of the system to data change, so that the generated unsafe behavior risk portrait is more accurate, dynamic and targeted, and the accuracy and reliability of overall safety monitoring and accident early warning are improved.

The invention provides a test case generation method and device, electronic equipment and a readable storage medium, code change is extracted based on Git change perception, and code change information of a to-be-tested program of a target version is obtained; screening out a first test case based on the code change information and a trained test case screening model; generating a second test case based on the code change information and a trained test case generation model; extracting dependency information of each external dependency associated with the code change, and matching a Mock object and a Mock rule for the external dependencies in the first test case and the second test case; and determining the matched first test case and second test case as a target test case of the to-be-tested program of the target version. Thus, through the trained test case screening model and test case generation model, the test case matched with the code change of the to-be-tested program can be generated, and the accuracy of test case generation in automatic testing is improved.

Embodiments of the present disclosure relate to a request processing method and apparatus, a device, and a storage medium. The method provided herein comprises: receiving a target request to be processed; during the processing of the target request, determining a triggered first component; in response to a first expected duration of the first component not exceeding an available duration of the target request, determining first dependency information of the first component, wherein the available duration is determined on the basis of a preset duration and an executed duration of the target request, and the first dependency information indicates a dependency type of a second component to be executed on the first component; and at least on the basis of the first dependency information, determining a target execution strategy for the first component, wherein the target execution strategy indicates whether to skip the first component.

Implementations generally relate to dependencies in workspaces. In some implementations, a method includes receiving one or more file types. The file types refer to source code files that contain source code. The method further includes fetching the source code files from a repository. The method further includes parsing the source code files resulting in parsed source code. The method further includes identifying import declarations in the parsed source code and extracting dependency information from the import declarations.

The invention discloses a cross-language interaction malicious behavior detection method based on a high-order function, and relates to the technical field of computer security. The method comprises the following steps: receiving a source code of a certain program, modeling a cross-language interface in the program, and identifying and marking a cross-language calling boundary; different language source codes are converted into universal intermediate representations (IR), the IR is combined, and cross-language calling points are marked; constructing a global cross-language function call graph and a control flow graph, determining a function analysis sequence by inverse topological sorting to perform dependency analysis on each function, and abstracting dependency information in the processes into a function abstract in a high-order function form; and parameter instantiation is carried out when function calling is processed, so that the dependency relationship between the cross-language APIs is obtained. According to the method, efficient analysis and transmission of the cross-language dependency relationship are realized through the high-order function abstract and the dependency matrix, the high overhead of constructing a complex system dependency graph by a traditional method is avoided, and the analysis efficiency and the detection accuracy are improved.