425 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 provides an automatic incremental compiling method and system for Java development, and the method comprises the steps: carrying out the hash value verification of an obtained Java source code, and obtaining the change event information in the Java source code; extracting static dependency information and a dynamic calling relation from the change event information, and generating a relation dependency graph according to the static dependency information and the dynamic calling relation; based on the relation dependency graph, generating a compiling task queue by utilizing a topological sorting algorithm; compiling the change event information in the Java source code according to the compiling task queue to obtain a machine code corresponding to the change event information; according to the method, code change information is accurately positioned through hash value verification, so that the initial range of incremental compiling is favorably reduced; according to the method, the static dependency information and the dynamic calling relation are extracted from the change event information, the relation dependency graph is generated, the dependency relation between the code units is completely represented, the problem that the dynamic calling relation is omitted is solved, and compiling correctness and efficiency are improved.

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 relates to a TCN and BiGRU-based power load decomposition and identification method and system, and the method comprises the steps: obtaining a data set of the total household electricity utilization power and the electricity utilization power of each electric appliance, carrying out the data preprocessing of the data set, and building a training data set and a test data set; constructing a TCN BiGRU network model comprising a TCN network, a BiGRU network and a GAM attention mechanism; inputting the power sequence into a TCN network to perform preliminary feature extraction, extracting sequence long-time dependence information, and obtaining power sequence shallow layer features; taking the shallow-layer features as input features of a BiGRU network, and extracting bidirectional deep-layer sequence features from the shallow-layer features; taking the bidirectional deep sequence features as input sequence information of a GAM attention mechanism, and outputting a predicted sequence midpoint; training and verifying the TCNBiGRU network model through the training data set and the test data set; and power load decomposition and identification are carried out through the trained TCNBiGRU network model. The method and the system are beneficial to extracting long-time dependence information and improving the power load decomposition and identification precision.

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.

An embodiment of a semiconductor package apparatus may include technology to determine version information for a new firmware component, read dependency information corresponding to the firmware component, and determine if dependency is satisfied between the new firmware component and one or more other firmware components based on the version information and the dependency information of the new firmware component. Other embodiments are disclosed and claimed.

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 discloses a GAF-CNN-Transform-based wind turbine generator converter abnormal state detection method. The method comprises the following steps: S1, continuously sampling three-phase current signals of a wind power system machine-side converter and a grid-side converter through a sliding window to obtain a time sequence data set; s2, preprocessing the time sequence data set, and converting one-dimensional time domain information into two-dimensional graphic information by using a GAF (Gray Angle Field); and S3, establishing a CNN-Transform neural network model, and carrying out abnormal state detection and classification on the wind power system converter through a two-dimensional image data training model. According to the method, the current signals are converted into the image data, dependency information between the data can be fully mined through the CNN-Transform model, the detection precision is high, and the operation reliability of the wind turbine generator can be effectively improved.

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 the technical field of inventory data prediction, and provides a spare part inventory data prediction method, system and device and a medium, and the method comprises the steps: constructing a prediction model, and predicting the future condition according to the previous spare part inventory data; the model comprises a plurality of modules: a convolutional neural network used for mining local features of past inventory data and outputting corresponding representations; the LSTM network is used for generating a current hidden state containing long-term dependence information by combining the local features with the hidden state in the previous step; and the Transform neural network is used for converting a hidden state into an embedded vector, calculating a context vector containing global dependence through a self-attention mechanism, performing multi-head attention enhancement after adding position codes, then sending the context vector into the feedforward neural network, and outputting a prediction result. And obtaining the current inventory data and inputting the current inventory data into the trained model to obtain future inventory prediction data. According to the method, the inventory data of the spare parts can be predicted.

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 provides a low-code software development system, which is responsible for acquiring a creation demand of a user about a Java project through a project management module, and comprises a source code mode and a Jar package mode. The code generation module generates codes and external dependency information according to requirements by using a large language model in a source code mode. And the Jar packet management module manages and calls the shared Jar packet, the project Jar packet and the extended Jar packet according to requirements in the Jar packet mode. The deployment management module completes project deployment through an independent class loader based on an amphiphilic delegation mechanism, and the method calling module dynamically calls a loaded Jar packet based on a Java reflection mechanism. According to the system, through automatic code generation and a flexible deployment mechanism, the development efficiency is remarkably improved, the development cost is reduced, meanwhile, the expandability and stability of the system are guaranteed, and the system is suitable for rapidly constructing and deploying Java projects.

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 descriptor of a network service is determined. The descriptor may be used for controlling management of a network service, such as instantiation or updating of the network service. The descriptor comprises multiple constituents. Further, the descriptor is provided with information on version dependency between at least two of the multiple constituents.

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 method for managing plug-ins, a plug-in type framework system and computing equipment, the method is used for application based on the plug-in type framework system, and the method comprises the following steps: traversing and searching all the plug-ins under a directory where a main program is located; collecting dependency information of each plug-in and forming a dependency list; according to the dependency relationship in the dependency list, constructing a directed acyclic graph to represent the dependency relationship between the plug-ins; and managing a loading sequence and an unloading sequence of the plug-ins according to the directed acyclic graph. According to the technical scheme, the limitation of a traditional software architecture can be solved, the customizability and the flexibility of a software development mode are improved, the development cost and the error rate are reduced, and the system maintainability is enhanced.

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.

This disclosure generally relate to a method and system for mapping application dependency information. The present technology relates techniques that enable user-adjustable application dependency mapping of a network system. By collecting internal network data using various sensors in conjunction with external user inputs, the present technology can provide optimized application dependency mapping using user inputs.

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 embodiment of the invention discloses a construction method and device of a C + + code knowledge base and a medium, relates to the technical field of knowledge maps, and is used for solving the problem that information of an existing knowledge base is incomplete and inaccurate. The method comprises the steps that a compiling configuration file containing detailed compiling parameters is constructed according to a C + + project configuration file; analyzing file elements of the compiling configuration file based on a preset script analysis engine, preprocessing the C + + project source file to obtain a processed C + + project file, and generating a compiling unit based on the file elements; obtaining dependency information of the C + + project file based on a compiling unit, obtaining an abstract syntax tree of the C + + project file for node filtering, and mapping and storing the filtered nodes and the dependency information into a corresponding semantic mapping table to realize first persistence processing; and determining the incidence relation of the C + + project file based on each semantic mapping table, storing each incidence relation in a preset relation database, and realizing second persistence processing to obtain a C + + code knowledge base.

The invention discloses an automatic test script generation method and terminal, and the method comprises the steps: carrying out the storage of dependency data, and obtaining a vector database containing interface dependency information and feature vector data, and a script sample database containing sample script codes; disassembling a text test case provided by a user to obtain a plurality of execution steps, and performing test sub-script generation on the execution steps one by one by adopting a plurality of step script generators and LLM models based on the vector database and the script sample library; and synthesizing the plurality of test sub-scripts into one test script by adopting a step script synthesizer. According to the method and the device, efficient, accurate and complete automatic test script generation can be realized.