3925 results about "Analysis data" patented technology

The invention discloses an intelligent management system for energy consumption optimization and fault self-diagnosis of cleaning equipment, and relates to the field of intelligent maintenance of the cleaning equipment, and the system comprises the steps: obtaining three groups of core parameters, i.e., a historical vibration spectrum, a motor current harmonic component and a bearing temperature gradient, constructing a dynamic failure mode knowledge graph, performing time sequence correlation analysis on the historical fault data to obtain failure mode analysis data; establishing a multi-dimensional analysis platform, identifying a high-risk component, and updating a fault threshold value; introducing a service time attenuation factor and a working condition correction coefficient, establishing an aging degree quantitative model, and calculating an aging coefficient; and constructing and developing an energy consumption-reliability joint optimization module, and adjusting equipment operation parameters. The method has the advantages that the dynamic knowledge graph and the time sequence analysis model are constructed by integrating multi-source sensor data, precise diagnosis and self-adaptive threshold adjustment of the coupling fault of the cleaning equipment are achieved, aging evaluation and task scheduling optimization are combined, the energy consumption efficiency is improved, and the maintenance cost is reduced.

The invention discloses a data exception traceability tracking and positioning method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of insufficient data anomaly detection accuracy, low traceability positioning efficiency, incomplete knowledge association and the like in analysis and application links of power marketing data are solved. The method comprises the following steps: carrying out preprocessing and feature extraction on power marketing data, constructing a data association relationship network through a clustering analysis and statistical method, analyzing the difference of data features, analyzing the propagation path of an abnormal point identified in a data stream by adopting a path reasoning technology, and constructing a data abnormity tracing positioning model. A semi-supervised learning method is adopted to improve the accuracy of the model, result optimization and hyper-parameter adjustment are carried out, and multi-condition factors influencing business data information are introduced into the model as hyper-parameters. According to the technical scheme, the anomaly detection and traceability positioning capability of the power marketing data is greatly improved.

The invention relates to the technical field of real-time data processing, in particular to a distributed real-time data stream processing system and method, and the system comprises a state mapping modeling module, a load dynamic calculation module, a node task adjustment module and a resource scheduling execution module. According to the method, the dynamic identification and classification of the flow state are realized by analyzing the data flow change sequence and identifying the frequency and trend of state switching, the monitoring and understanding capability of real-time data is improved, the node resource pressure is accurately quantified, the predictability of load balancing is enhanced in the resource occupation and load range analysis, and the load balancing efficiency is improved. A scientific basis is provided for resource scheduling, task priority ranking and dynamic task adjustment are combined, the node computing resource utilization rate and the task processing response speed are optimized, flexible configuration of internal storage and computing resources is achieved, peak load pressure is relieved, and data processing efficiency and system stability are improved. And the adaptability and the reliability of processing large-scale data streams by the system are improved.

The invention relates to methods and systems for integrating generative artificial intelligence (AI) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining AI agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows AI agents to be added as platform users. The generative AI agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of AI outputs, and management of AI resources as limited assets. This approach enhances SaaS functionality by enabling AI-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

A system for analyzing a boiler fault and a diagnosis method, the system comprising: a sensor system, a data acquisition and analysis system, and an early warning system. The sensor system is used to monitor temperature, pressure, and water level indicators inside of a boiler and transmit data. The data acquisition and analysis system is used to acquire, count, and analyze the temperature, the pressure, and the water level inside of the boiler in real time. The data acquisition and analysis system comprises a data acquisition module and an intelligent algorithm module. The data acquisition module is used to acquire real-time temperature, pressure, and water level data inside of the boiler. The intelligent algorithm module is used to analyze and process data and to predict and diagnose an operation state of the boiler. The early warning system comprises an early warning module. The early warning module is used to receive a signal of a fault model and to send, on the basis of the signal, a first-stage early warning signal and second-stage early warning information. Thus, real-time monitoring and early warning of the operation state of the boiler is achieved, and problems are promptly discovered and measures can be taken, therefore normal operation of the boiler is guaranteed, and production safety is guaranteed.

The invention provides a science and technology finance platform data sharing method, which comprises the following steps: extracting key field information and a metadata label from a data stream of a science and technology finance platform, the key field information comprising data content and a source identifier; analyzing a data type characteristic value according to the metadata label, and determining a data type and a corresponding sensitivity level by adopting a logistic regression model; querying a source credibility rule base based on the source identifier, and determining a risk level of the data source; according to the sensitivity level, the risk level and the receiver permission identifier, calculating to obtain a dynamic sensitivity score and generating a dynamic sensitivity tag; according to the risk level mapping table, in combination with the sensitivity level, determining a final desensitization mode of the field; and performing format verification on the processed data stream, transmitting the processed data stream, and recording execution information of the dynamic sensitivity label and the desensitization strategy.

The invention discloses a wind field wind resource fine simulation method, system and device based on WRF-CFD medium and micro-scale coupling and a storage medium, and belongs to the technical field of wind energy resource evaluation.The method comprises the steps that mesoscale meteorological analysis data is collected and calculated through a mesoscale WRF numerical simulation technology; performing fitting processing to obtain a fitting function between the wind speed component and the height; extracting wind speed contour information at a micro-scale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining a boundary condition of a micro-scale CFD model; performing directional calculation and correction on the wind speed of each sector by using a virtual anemometer tower; performing grid division on the wind power plant area to generate a refined grid corresponding to the wind power plant area; and driving numerical simulation of the micro-scale CFD model, and carrying out wind power plant regional range stroke resource evaluation. According to the method, the complex characteristics of the wind field can be comprehensively reflected, the wind resource evaluation precision and the calculation efficiency are remarkably improved, and the resource utilization is optimized.

The invention discloses a geological disaster deformation monitoring method based on remote sensing data, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, data source selection and preprocessing; s2, region feature extraction; s3, differentiating the multi-temporal remote sensing images; s4, analyzing a time sequence; s5, performing three-dimensional reconstruction on the deformation area; s6, settlement and landslide monitoring; s7, performing fusion analysis on environment and meteorological data; s8, performing data anomaly detection and automatic early warning; and S9, risk assessment and influence analysis. According to the geological disaster deformation monitoring method based on the remote sensing data, SAR, optical images and LiDAR data are combined, the defect of dependence of a single data source in the prior art is overcome, dynamic data fusion and time sequence analysis are used, multi-dimensional real-time monitoring of earth surface deformation is achieved, the monitoring precision and timeliness are improved, and the geological disaster deformation monitoring method based on the remote sensing data has good application prospects. More comprehensive geological disaster early warning can be provided, and the effectiveness of disaster prevention and control is greatly enhanced.

The invention discloses an engineering project supervision real-time visual decision-making platform based on multi-source heterogeneous data fusion, and relates to the technical field of engineering supervision. The visual decision platform is in communication connection with a data acquisition module, a project risk feature module, a risk identification model construction module, a real-time data stream processing module, a risk early warning module and a decision support module. According to the invention, the risk in the engineering project is accurately identified in combination with historical multi-source heterogeneous data and project risk characteristics, and meanwhile, the real-time data stream processing module can analyze data in real time, identify potential risks and problems and generate early warning information, so that the accuracy and timeliness of risk identification are greatly improved, and the risk identification efficiency is improved. According to the invention, powerful decision support is provided for supervisors, and through the preset early warning level and the early warning signal, managers can rapidly understand the risk condition and take corresponding measures, thereby effectively reducing the project risk.

The embodiment of the invention discloses an RAG multi-mode document analysis method and device and a medium, relates to the technical field of natural language processing and is used for solving the problem that an existing analysis mode is low in processing capacity. The method comprises the following steps: identifying a document type of a multi-modal document, and distributing an analysis task of the multi-modal document to a corresponding Kubernetes task node based on the document type; based on the analysis requirement of the multi-modal document, the analysis process of each Kubernetes task node is adjusted, the analysis task of the multi-modal document is executed based on the updated analysis process, and analysis data is obtained; preprocessing the parsed data, and performing semantic slicing on the processed parsed data in a dynamic text windowing mode based on a preset large language model to obtain semantic blocks corresponding to the processed parsed data; and converting the semantic blocks into embedded vectors, and storing the embedded vectors in a preset vector database so as to carry out rapid retrieval based on the preset vector database.

Systems and methods include discovering and classifying any of data discovered by inline cloud inspection, data stored across one or more cloud services, and data stored across one or more endpoints; continuously monitoring access to and usage of classified data, wherein the monitoring is performed in real-time and includes analyzing data access patterns, user behaviors, and application interactions; evaluating a security posture of the classified data by identifying misconfigurations, compliance violations, excessive permissions, and vulnerabilities; and enforcing one or more security policies based on the evaluated security posture.

The invention belongs to the technical field of data detection, and particularly relates to an efficient data anomaly automatic detection and alarm system. The detection and alarm system comprises a sensor and data acquisition device, a data processing and analysis module, an alarm module, a monitoring center and visual interface, a communication and network module, a storage and backup module, an intelligent decision support module and a user feedback and adjustment module. According to the system, data can be monitored and analyzed in real time, abnormity can be quickly found, an alarm is given, manual monitoring requirements are reduced, data misjudgment is avoided, data accuracy is ensured through data cleaning and processing, an intelligent decision support module provides optimization suggestions based on AI, and a user can adjust rules to adapt to requirements. The system has the functions of data processing, anomaly recognition and trend prediction, the monitoring center visually displays the state, the storage and backup module ensures long-term storage of data, the automatic mechanism improves the response speed and the operation benefits, and the adaptability is high.

The invention discloses a short-time rainfall prediction method based on radar image and reanalysis data fusion, and the method comprises the following steps: collecting radar images and reanalysis data at continuous times, and generating input data in a unified grid format through spatial interpolation, time alignment and standardization processing; respectively extracting spatial and temporal features of the radar image and the reanalysis data by using a dual-channel encoder, and carrying out weighted fusion through a channel attention mechanism to generate a fusion feature tensor; inputting the fusion features into a ConvLSTM (Convolutional Long Short-Term Memory Neural Network), modeling a spatio-temporal evolution process of a rainfall system, and outputting a preliminary rainfall prediction image in 0-3 hours in the future; constructing a residual learning network, and performing deviation correction on the preliminary prediction result based on historical residual and observation information; when the radar image input is missing, the completeness of the input structure is maintained through the replacement feature generation module; generating a rainfall intensity image or a probability graph in 0-3 hours in the future; the method supports visual output.

The invention discloses a network / security equipment security policy generation and evaluation method and system based on a large language model, and the method comprises the steps: carrying out the fine adjustment of a base model through employing a threat research and judgment data set and a policy generation data set, and obtaining a threat research and judgment LLM and a policy generation LLM; a threat research and judgment LLM performs threat research and judgment on the received abnormal log output by the log acquisition module, and outputs a threat information tetrad; the policy generation LLM generates an equipment security policy by calling a knowledge base Agent according to a threat information quadruple output by the threat research and judgment LLM; and the policy generation LLM call evaluation Agent evaluates the equipment security policy from three aspects of policy correctness evaluation, policy validity evaluation and policy suitability evaluation. According to the invention, the effectiveness and reliability of the equipment security policy are improved.

The embodiment of the invention discloses a multi-agent collaborative data visualization analysis method and device and a medium, and relates to the technical field of visualization analys.The method comprises the steps that a natural language analysis request input by a user is received, task splitting is conducted on the natural language analysis request through a preset task planning agent, and the task splitting result is obtained; generating corresponding task plan information, obtaining real-time operation data of a preset functional agent cluster, carrying out agent distribution on the plurality of sub-tasks based on the real-time operation data and the task plan information, determining a target functional agent corresponding to each sub-task, and sending the target functional agent to a server; the target function agent comprises any one of a data analysis agent and a visual display agent; and executing the corresponding data analysis subtask through the data analysis agent to obtain a data analysis result, performing agent cooperative verification on the data analysis result, and after the verification is passed, generating visual analysis data corresponding to the data analysis result through the visual display agent.

The invention relates to the technical field of data processing, and discloses a cross-border trade data intelligent processing method and system based on an intelligent port. The method comprises the steps of performing functional model hierarchical processing on cross-border trade business data, obtaining a functional module calling relation network, performing process template construction processing, obtaining a dynamic process assembly rule base, performing service burying point processing, obtaining a user behavior analysis data set, performing guide strategy construction processing, and obtaining a user behavior analysis result set; and obtaining a service guidance rule base, carrying out knowledge graph construction processing, obtaining a service knowledge inference rule set, and carrying out cross-border trade data processing to obtain a port service intelligent decision scheme. According to the method, intelligent processing of cross-border trade data is realized, a complete technical chain from data acquisition, behavior analysis to decision support is constructed, accurate business guidance and intelligent decision support are provided, and the problems of low business processing efficiency, inaccurate service guidance, weak decision support capability and the like in the prior art are solved.

The invention discloses a building waterproof construction quality monitoring and early warning system based on the Internet of Things, relates to the technical field of building engineering, and realizes omnibearing and real-time monitoring and intelligent early warning of a building waterproof construction area through a multi-layer architecture formed by a data acquisition layer, a data transmission layer, a data processing layer and an application layer. The data processing layer analyzes data features from a time sequence and a spatial distribution dimension through normalization, denoising processing and a multi-head attention mechanism in a Transform model, and accurately recognizes the abnormality and risk trend of a weak point in combination with dynamic threshold and trend analysis, and the application layer determines the risk trend of the weak point through multi-dimensional data visualization, historical playback and real-time interaction. And the early warning module is combined with intelligent reasoning to generate dynamic maintenance suggestions, a maintenance scheme is optimized, the construction quality and the maintenance efficiency are improved, the intelligent level of building waterproof monitoring is improved, risk prediction and accurate maintenance are realized, the management cost is reduced, and the system has wide application value.

The present disclosure is directed to enabling asynchronous analytics via an intercept device. The intercept device may intercept a request transmitted by a user device to a host server, the request indicating data of interest to be provided by the host server in a response; transmit the request to the host server; asynchronously extract analytic data from the request; transmit the analytic data to an analytics server for performance of analytics on the analytic data; receive, from the analytics server, a result of performance of the analytics on the analytic data; determine, based at least in part on analyzing the result, whether the response from the host server is to be modified; selectively effect modification of the response based at least in part on determining whether the response from the host server is to be modified; and transmit the response to the user device. Various other aspects are contemplated.

The invention relates to the technical field of data analysis, in particular to an intelligent hydrological data analysis method and system, and the method comprises the following steps: collecting water level, flow velocity and precipitation data, calculating the change rate of adjacent moments, marking hydrological parameter abrupt change points, carrying out the statistics of time intervals, predicting the duration of extreme events, and obtaining the distribution characteristics of the hydrological abrupt change points. According to the method, by analyzing the time interval and the duration time of the data points, the distribution characteristics of hydrological abrupt change can be mastered in the early stage, response can be made in advance, the propagation sequence and the diffusion rate of the abrupt change points in different river reaches can be analyzed, and accurate division of the influence range and the risk level of hydrological events is enhanced; effective management of hydrological event space risks is achieved, understanding of hydrological key influence factors is deepened by screening key variables and evaluating the evolution relation of the variables along with time, accuracy of short-term and long-term hydrological risk prediction is enhanced, and a scientific basis is provided for dynamic early warning of hydrological abnormal events.

The invention discloses a data risk management system and method based on a large model, and relates to the technical field of data risk identification, and the data risk management method specifically comprises the following steps: obtaining data; the obtained data is preprocessed; analyzing data by using a large model, constructing a data relation graph, and identifying a potential hidden relation; performing association rule mining to obtain semantic association among the data; obtaining time sequence dependence among the data through dynamic time warping; carrying out correlation analysis through multi-modal fusion by taking the hidden relationship, the semantic association and the time sequence dependence as references; introducing a sensitivity dynamic grading and confidence correction mechanism to fit a risk assessment function; constructing a dynamic threshold function according to time, space and confidence feedback of the large model as elements; and providing coping suggestions according to the risk assessment result. According to the method, the hidden association between the data is identified, semantic analysis and time sequence dependency are combined, a risk assessment function is fitted, and the risk identification accuracy is improved.

The invention discloses a method and system for encrypting sensitive data based on a large model, relates to the field of data security, and solves the problem that a traditional fixed encryption strategy lacks dynamic adaptability. An encryption strategy container is generated through a data evaluation server, an evaluation module analyzes data content characteristics through a large model, a multi-dimensional sensitivity evaluation matrix and a use scene-risk level association model are established, and meanwhile the performance of an encryption module is tested; calculating a risk level according to a data sensitivity weight and a scene risk coefficient, triggering an alarm if the risk level exceeds a threshold value, otherwise, comparing a standard scene risk model to adjust encryption algorithm parameters, and generating a scene self-adaptive dynamic encryption strategy in combination with an encryption efficiency curve after performance compensation; and finally, the encryption gateway executes encryption. The method can improve the resource utilization rate through dynamic adaptive encryption, guarantees the stable efficiency through real-time performance compensation, improves the perspectiveness through intelligent prediction, reduces the safety risk and management cost through whole-process protection, and is suitable for a multi-industry sensitive data protection method.

The invention provides a dynamic security risk assessment and intelligent response system and method based on AI, relates to the field of dynamic security risk assessment and intelligent response, and improves the accuracy and response speed of security protection. The method comprises the following steps: firstly, collecting multi-dimensional security data, carrying out data preprocessing by utilizing an AI technology, and outputting a structured security data stream; then analyzing the data flow based on an AI model, identifying cross-dimension attack features, generating real-time threat intelligence, updating a risk judgment threshold value, generating a security assessment report, automatically generating and executing hierarchical response measures according to the assessment report, feeding back a response execution effect, performing dynamic adjustment, and generating a new security assessment report; and finally, automatically selecting a data transmission mode and rate according to the new security assessment report, and adjusting the acquisition frequency of the sensor. According to the method, through dynamic optimization and an intelligent response mechanism, the recognition and defense capability on complex attacks is remarkably improved.

The invention discloses an error correction system and method for marine environment observation data. The error correction system comprises a data acquisition module, a compensation modeling module, an error analysis module, a data compensation module, a data fusion module and a feedback optimization module. The data acquisition module acquires ADCP flow measurement, GPS, IMU, temperature, pressure and water quality monitoring data. And the error analysis module detects a drift error by using Kalman filtering and a hidden Markov model, and analyzes an environmental error based on a long short-term memory network. The data compensation module optimizes a compensation model and improves data precision. And the data fusion module fuses multi-sensor data by adopting a particle filter and a Bayesian optimization algorithm. The feedback optimization module dynamically adjusts the weight of the compensation model according to the error correction data, and improves the adaptability of the system. According to the invention, the accuracy of ocean current monitoring data is improved, and more reliable data support is provided for underwater environment research.

The invention relates to the technical field of target detection, and discloses a personnel state recognition method and system based on target recognition detection. The method comprises the steps of performing parameterization processing on collected original image data to obtain intelligent BIM data; performing multidisciplinary data exchange processing on the intelligent BIM data to obtain target detection data; performing intelligent posture analysis processing on human body information in the target detection data to obtain optimized posture data; behavior recognition processing is carried out on the optimized attitude data to obtain behavior analysis data, and the behavior analysis data comprises violation behavior types, climbing state judgment and risk level evaluation; performing fuzzy logic evaluation processing on the behavior analysis data to obtain safety compliance data; and performing intelligent early warning processing on the safety compliance data and the real-time monitoring data to obtain full-life-cycle management data. According to the invention, the efficiency of personnel state recognition based on target recognition detection is improved.

The invention relates to methods and systems for integrating generative artificial intelligence (Al) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining Al agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows Al agents to be added as platform users. The generative Al agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of Al outputs, and management of Al resources as limited assets. This approach enhances SaaS functionality by enabling Al-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

The invention relates to the technical field of power grid monitoring, in particular to a power grid abnormal state monitoring and early warning system which comprises a historical reference establishment module, an operation deviation analysis module, an abnormal threshold adjustment module and an abnormal early warning module. According to the invention, through real-time data acquisition and historical data analysis, the monitoring and early warning efficiency of power grid operation is improved, and through collection of power grid operation records, extraction of data of differential time periods, evaluation and analysis of the average load and volatility of the data, and construction of a historical power grid operation reference, the operation trend and potential abnormity can be captured more accurately. The method reduces the uncertainty of judgment depending on experience, can recognize a deviation mode and an adjusted load detection threshold value by comparing current data with a historical reference, enables the abnormal early warning to be more accurate, avoids excessive false alarms and missed alarms, optimizes the evaluation of early warning levels and the implementation of early warning notification, guarantees the safety and stability of a power grid, and improves the safety and reliability of the power grid. And the economic efficiency and the reliability of power grid operation are also improved.

The invention relates to the technical field of reservoir evaluation, in particular to a deep learning-based reservoir evaluation model construction method and system. The method comprises the following steps: acquiring logging data and core analysis data of a target work area, and respectively carrying out abnormal value processing and missing value filling to obtain complete logging data and complete core data; calculating reservoir pore structure parameters based on the complete core data; and inverting the macroscopic porosity of the reservoir based on the complete logging data. According to the method, the accuracy, the stability and the robustness of reservoir evaluation are remarkably improved by fusing the core data and the logging data and combining the pore structure parameters, the macroscopic porosity and the geological attention mechanism, the problems of data missing and small samples are solved, and the interpretability and the geological consistency of the model are enhanced.

The WTRU may determine that it has a need to request a NAS network service and may generate an application layer NAS message that comprises the request. The WTRU may determine, based on configuration information, one or more network nodes configured to provide NAS network services. The WTRU may determine, based on analytics data and policy data, a first network node in the one or more network nodes to which the application layer NAS message may be sent. The WTRU may determine, based on the policy data, one or more parameters for sending the application layer NAS message to the first network node. The WTRU may determine an endpoint address associated with the first network node. The WTRU may send the application layer NAS message to the first network node using the endpoint address and the one or more parameters.