1342 results about "Analysis tools" patented technology

The invention discloses a multi-level energy management system based on multi-dimensional data, and relates to the technical field of energy intelligent management, and the system comprises a multi-source data collection module which collects power utilization, environment and equipment state data in real time; the data fusion processing module is used for processing abnormal values through an algorithm and fusing multi-scale data features; the energy state evaluation module is used for realizing equipment state evaluation and early warning by using a fusion algorithm and a prediction model; the multi-level energy scheduling module adopts an optimization algorithm to balance the energy cost, the production efficiency and the carbon emission, and dynamically adjusts the strategy; the energy performance analysis module is used for developing an analysis tool and an evaluation model; and the decision support module is used for configuring an expert knowledge base and developing a fault diagnosis system and a knowledge graph. Through multi-dimensional data acquisition and multi-level management, the energy data error is greatly reduced, the comprehensive energy cost and carbon emission are remarkably reduced, the cost of participating in enterprise operation is reduced, and the accuracy, safety and sustainability of energy management are improved.

The invention discloses a knowledge exploration method and system based on a generative thinking chain and a feedback mechanism, and the method comprises the steps: receiving a multi-modal query inputted by a user, and carrying out the intention analysis to recognize a core demand; preprocessing the multi-modal query, extracting key information, and decomposing a complex problem into a plurality of sub-problems or sub-tasks; dynamically constructing a hierarchical reasoning path based on the generative thinking chain, and marking a logic basis for each step of reasoning path; reasoning path priority is optimized through dynamic weight distribution, a reasoning strategy is dynamically adjusted on line in combination with a meta-reinforcement learning framework, and a reasoning path is optimized; verifying the reasoning path by using a causal reasoning analysis tool, and generating an anti-fact path to correct error nodes; and outputting a structured text and a visual reasoning process, and optimizing model parameters in real time based on multi-user feedback aggregation of the game theory. According to the method, the problems of intelligent query and reasoning in a complex scene can be effectively solved, and the accuracy, real-time performance and credibility of knowledge service are realized.

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention provides an intelligent data quality monitoring method and system. The method comprises the following steps: performing data processing on multi-source heterogeneous data by utilizing a unified data model; inputting historical data as a training set into the pre-training model for training optimization to obtain an optimized pre-training model, and performing threshold determination on the standardized distribution data stream by using the optimized pre-training model in combination with an adaptive threshold module; inputting the processed abnormal event list into a context awareness and dynamic priority management module to carry out de-duplication optimization transmission; carrying out root analysis on the abnormal event list after the de-weighting optimization by utilizing a root analysis tool; generating a repair strategy for the root analysis result by using the assistance of a repair suggestion engine; and inputting the repair strategy and the visual report into a cross-platform monitoring interface, and generating a repair suggestion and an early warning notification. According to the method, the data consanguinity map is constructed based on the graph database, minute-level traceability of abnormal events is achieved, and the troubleshooting time is shortened by 50%.

The invention provides an intelligent data production method and system based on a graph neural network and adaptive learning, and the method comprises the steps: collecting a text, a time sequence, an image and sensor data in real time, and converting unstructured data into a structured JSON format through a format analysis tool; extracting text entity features based on a large language model, modeling a cross-modal entity association relationship in combination with a graph neural network, and fusing multi-source heterogeneous data through a dynamic knowledge graph; the detection threshold is dynamically optimized by adopting a reinforcement learning algorithm, and the false alarm rate is reduced in combination with a double-track verification mechanism; and predicting a data trend by using the space-time diagram convolutional network, and outputting an interpretability analysis report through the generative large model. According to the invention, a multi-protocol adaptation engine and a distributed stream processing architecture are adopted, and a protocol analysis layer automatically identifies a plurality of industrial protocols, so that the manual adaptation time is reduced; and real-time synchronization of production line-level data is realized through edge node parallel acquisition and compression transmission.

The invention discloses an intelligent inspection system and method for a photovoltaic module of a photovoltaic power generation system, and relates to the technical field of intelligent inspection. The method comprises the steps that a high-definition camera and an infrared imager carried by an unmanned aerial vehicle automatically inspect a photovoltaic panel, real-time operation data are acquired and transmitted to a preprocessing module, the preprocessing module optimizes an image through denoising, graying, contrast enhancement and geometric correction, a Unet algorithm is utilized to accurately segment strings and assemblies in the image, and the photovoltaic panel is obtained. And a defect identification module analyzes the processed image, calculates the average temperature, compares the average temperature with a threshold value, identifies hot spot defects, and finally outputs specific information, fault types and maintenance suggestions of a defective component by the system, and generates an inspection report. According to the technical scheme, a fault source can be quickly positioned, and cloud storage and an AI analysis tool are used for managing data and predicting potential maintenance requirements.

The invention relates to the field of voice signal processing, and discloses a multi-level labeling method and device in a dialogue scene. Through the endpoint detection and automatic speech recognition method of the dialogue audio data, noise and mute in real speech can be removed, and effective speech segments can be obtained. And carrying out clustering analysis based on the effective voice segments, obtaining target voice segments and text data of the corresponding speakers, and carrying out emotion feature extraction through multi-modal emotion analysis. And finally, realizing automatic annotation of the inquiry strategy or intention understanding of the corresponding speaker by utilizing a large language model. According to the method, the recorded conversation voice can be subjected to multi-level labeling, an efficient and accurate information management and analysis tool is provided for a conversation scene, and the arrangement efficiency of conversation records can be greatly improved. The annotation information can be used for training and improving an artificial intelligence system, so that the artificial intelligence system plays a greater role in future psychological consultation service, and the overall quality and efficiency of psychological health service are improved.

The invention provides a code peculiar smell detection method and system based on AST code measurement and code semantics. The method comprises the following steps of: firstly, constructing a FA-AST (Flow-Augmented Abstraction Syntax Tree) by analyzing a code snippet, and extracting three types of AST-based code metrics, namely a structure metric, a syntactic metric and a semantic metric from the FA-AST; then, a deep learning model is used for capturing contextual information and a dependency relationship of the code from the source code so as to extract code semantics; and finally, by fusing AST-based code measurement and code semantics, inputting a classification model to carry out training and testing so as to identify the peculiar smell in the code and evaluate the severity of the peculiar smell. Compared with software code measurement extracted by a traditional static code analysis tool, the AST-based code measurement provided by the invention is more comprehensive and easier to extract, and has obvious advantages in the aspect of improving the code odor detection precision and efficiency.

The invention provides an SCA and SAST combined vulnerability reachable path detection method and device. The method comprises the steps of obtaining a to-be-detected item; extracting an entry function based on the to-be-detected item; when the to-be-detected item is a source code item or a product item, generating a function call graph based on call graph generation logic corresponding to the source code item or call graph generation logic corresponding to the product item; identifying a vulnerability function in the dependent component of the item to be detected through a software component analysis tool based on preset software bill of material information; taking the vulnerability function as a starting point, performing depth-first search traversal in the function call graph, and detecting whether an entry function is traversed or not; and if yes, determining a path from the entry function to the vulnerability function as a vulnerability reachable path. According to the method and the device, the reachable path of the vulnerability can be automatically detected, so that whether the security vulnerability is triggered or not is determined, invalid checking of the SCA tool in the use process can be avoided, and the reliability of the SCA tool is improved.

The embodiment of the invention discloses a life omics research method and device based on artificial intelligence, and the method comprises the steps: carrying out the preprocessing of a project according to the intelligent interaction between a user and a system, and generating standardized data; and meanwhile, carrying out transfer learning on the large language model to construct a life science large language model. And according to the standardized data, decomposing a research target by adopting a life science big language model in cooperation with an intelligent agent, and generating an analysis plan. And based on the analysis plan, the intelligent agent is scheduled through the coordinator, and a hierarchical task is generated. And calling a multi-omics analysis tool by the intelligent agent to calculate and analyze the grading task, and outputting an analysis result. And based on data features, integrating analysis results through an integrated advanced model, and outputting a final report to complete the research of life omics. According to the embodiment of the invention, full-process automation and natural language interaction are realized, the standardization of data analysis and the repeatability of results are ensured, the research efficiency is remarkably improved, and the technical threshold is reduced.

The invention provides an intelligent project management system based on supervision integration, and the system employs a cloud platform for deployment, and comprises a project management module which is used for task management, progress tracking and document sharing, so as to achieve the planning, starting, execution, monitoring and ending of a whole life cycle of a project; the data acquisition and integration module is used for collecting project data in real time and performing integration processing; the risk assessment and early warning module is used for performing risk assessment on the project data and giving out early warning when the risk exceeds a preset threshold value; the intelligent decision support module is used for providing decision suggestions for a project manager based on the data analysis result; and the visual display module is used for visually displaying information such as a project state, progress, cost and quality in forms such as charts and reports. According to the system, through an integrated management platform and an intelligent analysis tool, real-time monitoring, risk assessment, resource optimization and decision support of the full life cycle of a project are realized, so that the project management efficiency is improved, and the project risk is reduced.

The invention discloses a logistics center intelligent optimization scheduling system based on data analysis, and relates to the technical field of intelligent optimization scheduling, during operation of the system, key data in logistics center operation is collected and processed, after preprocessing, the inventory state of a logistics center is managed and optimized, and based on real-time inventory data and order demand information, the logistics center intelligent optimization scheduling system based on data analysis is obtained. Based on real-time traffic data, order demands and vehicle positions, calculation is carried out to obtain a comprehensive optimization index SOF, according to the order demands, the cargo capacity, the vehicle capacity and the real-time positions, vehicles are intelligently distributed, the loading sequence and the loading rate are optimized, and through the Internet of Things technology and a data analysis tool, the real-time traffic data and the real-time traffic data are analyzed. And the vehicle position, the transportation state, the environment change and the order processing progress are monitored in real time, the scheduling optimization level of the logistics system is evaluated in real time by comparing the comprehensive optimization index SOF with a preset threshold value, and an abnormal early warning and feedback mechanism is provided.

Event-triggered forensics capture technologies balance security incident data availability against data processing and storage costs. A forensic correlation engine receives basic status data of a monitored computing system. A forensic computing system detects a trigger event in the basic status data, and starts capturing extended status data per a corresponding capture specification. Captured data is submitted to a forensic analysis tool. Different trigger events may cause different data captures. A query specifying which data to capture from a live stream or from virtual machines may operate as a capture trigger start event. Extended status data capture activity may be stopped by a change in the basic status data being received, by a timeout, or by forensic analysis that finds no vulnerability or threat based on captured data. Data transfers and storage may be restricted to comply with privacy regulations or policies.

The invention relates to the technical field of model quantification and compression, and discloses a low-computing-power environment-oriented model quantification and compression method, which comprises the following steps of: analyzing the demand of a large model in a specific task, and evaluating the hardware resource limitation of a low-computing-power environment by using a demand analysis tool; dynamically optimizing the quantization process of the neural network model by using a reinforcement learning method, and constructing a quantization perception training framework; performing structure optimization on the neural network model by applying a pruning technology and a rarefaction technology, and removing redundant parameters and connections; performing adversarial ability testing on the compressed neural network model by using an adversarial sample generation method, and evaluating the robustness of the model; converting the optimized neural network model into a low-computing-power environment format, and converting the model into a low-precision format by using a quantification tool; by monitoring operation feedback of the neural network model, a performance analysis tool is used to collect reasoning results and resource consumption data. The method has the advantage of improving the adaptability of the model in a low-computing-power environment.

According to the network security risk assessment method for the unmanned aerial vehicle system, real-time analysis of an AI algorithm is adopted, dynamic assessment and an automatic defense strategy are combined, real-time monitoring and dynamic adjustment of a risk assessment result can be carried out, it is ensured that effective risk assessment is carried out in a rapidly changing threat environment in the flight process of an unmanned aerial vehicle, and the risk assessment efficiency is improved. The use requirements are met; comprising the following steps: scanning source codes of safety key components of an unmanned aerial vehicle system by adopting a static code analysis tool, and identifying all potential attack points; the system state, network data and sensor information of the unmanned aerial vehicle are collected, threat intelligence is obtained, an AI algorithm is adopted to analyze data in real time, and abnormal behaviors are identified; quantifying the security threat of the current abnormal behavior, and evaluating the attack possibility and potential influence; dynamically adjusting a defense strategy according to a risk assessment result; all security events and response conditions are recorded, attack traceability analysis is formed, the AI model is updated regularly, and the recognition capability of novel attacks is enhanced.

The invention provides an RAG knowledge base construction method based on layout analysis and query generation. The method comprises the following steps: receiving a plurality of query documents; for each query document, performing layout analysis on the query document by utilizing a layout analysis tool; segmenting and merging the text content according to the layout analysis result to obtain a plurality of text segments; generating a title for the query document by using a large language model, and respectively generating a preset number of queries for each text segment; for each text segment, generating a first vector based on the title and the text segment, and querying and generating a second vector based on a preset number of the text segments; and merging and storing each text segment with the first vector and the second vector of the text segment to construct an RAG knowledge base. Therefore, the text segments are obtained by performing effective layout analysis on the query document, and the query is generated for each text segment to expand semantics, so that a more comprehensive RAG knowledge base is constructed, and the retrieval enhancement generation effect of the system can be improved.

A system and method are provided for dynamically modifying textual content by applying a text analysis tool and selectively using a large language model (LLM). The method includes applying a text analysis tool to text having been added to a document, identifying a portion of text in the document as violating a rule in a rule set associated with the document, and providing a first input to a large language model (LLM). The first input comprises at least one prompt requesting a revision to the portion of text in the document. The method also includes receiving, from the LLM, a response to the first input, the response comprising a suggested modification to the document related to the portion of text; and providing an option to apply the suggested modification to the document.

The invention relates to the technical field of enterprise data encryption, and discloses an enterprise data encryption method and system based on artificial intelligence, and the method comprises the steps: collecting internal and external heterogeneous data streams of an enterprise, carrying out the standardization processing, and recognizing potential risk features through combining time sequence alignment and vulnerability analysis; a risk probability analysis tool is utilized to carry out quantitative evaluation from two dimensions of data sensitivity and an influence range, an encryption parameter adjustment scheme is dynamically generated, calculation and storage resources are dynamically allocated according to a scene risk level, an encryption strategy is updated in real time, and hierarchical protection is carried out on sensitive information. According to the method, corresponding encryption strength can be adopted for data of different risk levels, resource utilization is optimized while safety is guaranteed, the data leakage risk is effectively dealt with, and the enterprise information safety protection capability is improved.

The invention relates to an aircraft concept scheme design method and system based on generative artificial intelligence. The method comprises the following steps: selecting a large language model as a system basic model; constructing an aircraft collaborative design cloud platform; driving the large language model to generate a design scheme in a local calling mode, wherein the design scheme comprises model type selection and cue word selection; weight, pneumatic, performance and modeling professional analysis tools are packaged into a capability unit of a design system, and related modeling, evaluation and verification links comprise appearance, power, pneumatic, weight, RCS and flight performance parts and are deployed and operated in a distributed mode. On the basis of a design project in a collaborative design cloud platform, an aircraft CATIA model, overall parameters, aerodynamic parameters, weight parameters and performance data formed by a design scheme are extracted, and elements therein are displayed. According to the system, the aircraft design efficiency is remarkably improved, and the design cost is reduced.

Systems are provided for implementing methods for generating and processing fix suggestions for alerts produced by code analysis tools. The methods involve receiving an alert, parsing the alert to identify relevant information, and assembling a prompt for a Large Language Model (LLM). The prompt includes a general description of the problem, a description of the alert message, a source-code location, and a request for a proposed fix to the alert. The prompt is sent to the LLM, and a response is obtained. The response includes a proposed fix comprising a set of edits to be applied to the source code and / or configuration files. The response is processed and validated, and a fix suggestion is assembled and provided based on the response. The fix suggestion includes a natural language explanation of the proposed fix, suggested source-code changes, and selectable options for accepting, rejecting, and editing the fix suggestion.

The invention discloses a network fault diagnosis and simulation method and device of a power grid system and a medium, belongs to the technical field of cloud computing networks and smart power grids, and aims to solve the technical problem of how to monitor, diagnose, effectively simulate and accurately predict various faults possibly occurring in the operation of a power grid in real time. In order to improve the self-healing capability, stability and reliability of a power grid and ensure the continuity and safety of power supply, the adopted technical scheme is as follows: analyzing fault scenes and types, identifying fault scenes possibly encountered by the power grid and classifying the fault scenes; deploying a real-time monitoring system, and uninterruptedly collecting power grid operation data; key features related to the fault type are extracted by adopting a feature extraction algorithm; a fault diagnosis model is constructed by using a machine learning or deep learning technology, and automatic identification of fault types is realized; and creating a power grid digital twinborn model, simulating physical characteristics and behaviors of the power grid, and developing a data analysis tool to perform statistics and trend analysis on a simulation result.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing and analysis method, device, equipment and medium, and the method comprises the steps: obtaining a data source set, and registering a source label and field mapping relation; executing extraction, cleaning, conversion and loading in the data processing assembly line based on the data source set, the source label and the field mapping relation to generate a processing table set; performing quality verification and exception handling on the processing table set to generate a verified processing table set; generating an index cloud based on the verified processing table set and the configuration strategy; the index cloud is pushed to the analysis tool set through the interface transmission channel, and the analysis tool set generates analysis output according to the index cloud. According to the method, through unified data registration, standardized processing and quality verification, the index cloud is generated in combination with the configuration strategy and pushed to the analysis tool set, the analysis result is generated and transmitted, the data consistency is ensured, and the real-time performance is improved.

The invention provides a welding residual deformation simulation prediction and parameter optimization method for a structural welding part, which comprises the following steps of: firstly, establishing a three-dimensional model of the structural welding part, and performing simulation analysis on a welding temperature field, welding residual stress and welding deformation of the three-dimensional model by using a professional simulation analysis tool; secondly, the influence of clamping conditions, heat source forms and welding parameters on welding deformation of the structural welding part is researched, and simulation and physical verification is achieved. And then, by taking the minimum welding residual deformation as an optimization target, optimizing the welding sequence by adopting a genetic algorithm and taking the welding sequence and the welding direction as design variables to obtain a group of optimal solutions, and obtaining the welding sequence with the minimum welding deformation through simulation. And finally, on the basis of a welding sequence optimization result, performing simulation optimization by utilizing tightening position adjustment and reversible deformation design in sequence to obtain a process design scheme with minimum welding deformation.

The invention discloses a power grid analysis tool calling method and system based on a large language model, and belongs to the technical field of electric power automation, and the method comprises the steps: collecting the interface information of each power grid analysis tool, carrying out the standardization processing of the interface information, and building an interface information library; generating a calling instruction of the power grid analysis tool according to the interface information base, and classifying and marking the calling instruction to form an instruction data set; the instruction data set is used for conducting fine adjustment on large language model parameters called by the power grid analysis tool, and an optimized large language model is obtained; after an instruction is input, retrieving and returning a group of instructions corresponding to input instruction semantics from the instruction data set, and performing multi-round interaction with the optimized power grid analysis tool calling large language model to generate a power grid analysis tool calling structured statement; and calling the structured statement according to the generated power grid analysis tool, and executing calling operation on the corresponding power grid analysis tool. The accuracy and efficiency of power grid analysis are improved.

The invention provides a software code defect detection method and system based on program code feature fusion, and the method comprises the steps: carrying out the grammar and structure inspection of a code through a static analysis tool, and extracting defect features related to control flow chaos, exception handling and resource leakage; running a preset test case through a dynamic analysis tool, capturing an execution path and an abnormal behavior in a runtime state, and obtaining defect features which are not covered by static analysis; if the output result of the decision tree model is that defects exist, grouping comprehensive defect feature vectors by adopting a density-based clustering algorithm, determining the types and distribution ranges of the defects, and selecting the density clustering algorithm to process defect features in non-spherical distribution; and according to a clustering result, generating a defect repairing suggestion, combining code logic and execution path information, and determining a specific position and an influence range of the defect. According to the method, the stealth defect in the code can be effectively identified and positioned, the software quality and reliability are improved, and timely and accurate defect repairing guidance is provided for developers.

The invention relates to an ADS-B (Automatic Dependent Surveillance-Broadcast)-based civil aviation flight pre-arrival data calculation method, which integrates ADS-B data, meteorological satellite data, airport runway state monitoring data and air traffic control real-time instruction data through multi-source data fusion, and dynamically adjusts the weight of each data source according to the flight stage of a flight. A dynamic adjustment time synchronization interval mechanism is adopted, a short synchronization interval is set in the take-off stage and the landing stage so as to capture rapidly-changed data, and the synchronization interval is prolonged in the cruise stage so as to optimize computing resources. Meanwhile, the deep learning model based on the Transform architecture utilizes a multi-head self-attention mechanism and position coding to enhance the feature capture capability, and reduces the calculation burden through a sparse activation strategy. The system is provided with a real-time monitoring layer, log records and visual analysis tools are integrated, and interpretability and traceability of the model are ensured. An efficient aviation management decision is supported, and the efficiency and safety of overall aviation traffic management are improved.

The invention provides an abnormal log processing method and device based on a big language dialogue model. The method comprises the steps that after a pre-configured error code database and a code vector retrieval library are obtained, abnormal log content involved in abnormal execution of a target application service at the current moment is collected; determining model input data based on the error code database, the code vector retrieval library and the abnormal log content; and inputting the model input data into a pre-trained big language dialogue model to obtain an error code or an operation and maintenance solution corresponding to the abnormal log content output by the big language dialogue model. According to the method, errors can be quickly diagnosed through an automatic log analysis tool, intervention of developers is not needed, and the operation and maintenance efficiency and the response speed of customer problems are improved.

A system for personalized product recommendations in an e-commerce platform, comprising: a user data collection module configured to collect browsing history, purchase records, search queries, and demographic information; a data preprocessing module configured to apply cleaning, feature extraction, and natural language processing (NLP) techniques to structure raw data; a product information management module configured to extract, classify, and update product attributes using NLP and image recognition; a recommendation engine that uses machine learning models, including collaborative filtering, content-based filtering, and deep learning techniques, configured to generate personalized product recommendations; a contextual filtering module configured to dynamically refine recommendations based on real-time factors such as user location, device type, and market trends; a feedback learning module configured to continuously update recommendation models using user ratings, reviews, and engagement metrics; and an API integration module configured to enable seamless connectivity with third-party e-commerce applications, inventory management systems, and analytics tools.

Techniques for implementing an AI threat modeling tool are disclosed. A static analysis tool is used to extract a candidate code snippet from a code repository. The candidate code snippet is identified as potentially being a security relevant code element. The static analysis tool generates additional context associated with the candidate code snippet. An LLM prompt is generated. This prompt is structured to include the candidate code snippet, the context, and a directive to assign a classification to the candidate code snippet. The classification includes a source classification, a sink classification, a sanitizer classification, or a flow step classification. The LLM operates on the prompt to generate output comprising a specific classification for the candidate code snippet. The output is formatted into a data extension file that is consumable by the static analysis tool.

The invention provides a multi-level collaborative urban physical examination knowledge reasoning method and related equipment. The method comprises the following steps: acquiring a knowledge base constructed by an urban physical examination knowledge graph and a question request; the question request is input into an entity-level reasoning module for entity and attribute recognition, an initial evidence triple is obtained, knowledge mining is conducted on the initial evidence triple according to a knowledge base, and an evidence triple is obtained; inputting the question request and the evidence triple into a function level reasoning module for identification to obtain an urban physical examination analysis function required in the question request, and calling a space-time analysis tool for space-time analysis according to the urban physical examination analysis function to obtain an evaluation analysis result; inputting the problem request, the evidence triple, the knowledge base and the evaluation analysis result into a decision-making reasoning module to perform knowledge reasoning on the problem request to obtain a reasoning result; the problems that in the urban physical examination knowledge reasoning process, knowledge mining is not deep enough, tool arrangement efficiency is low, and knowledge reasoning is not fine enough are solved.