1167 results about "Evaluated data" patented technology

The invention relates to the technical field of pipeline data processing and health state evaluation, and discloses a pipeline interior detection method and system based on multi-sensor fusion. The method comprises the following steps: performing health index calculation on pipeline state evaluation data to obtain a pipeline health state index, and performing risk prediction on the pipeline health state index to obtain a risk level; performing pipeline defect feature recognition to obtain defect feature data, and performing multi-category classification processing on the defect feature data to obtain defect type data; performing accurate position analysis on the defect type data to obtain defect space distribution data, performing structural integrity evaluation to obtain a defect severity quantitative data set, performing multi-criterion decision analysis on the defect severity quantitative data set and risk levels to obtain defect repair priority ranking data, and performing defect repair priority ranking on the defect repair priority ranking data; and performing intelligent repair scheme generation on the defect repair priority ranking data to obtain repair suggestion data. According to the invention, the state of the pipeline can be evaluated more comprehensively and accurately.

The local intrinsic dimensionality (LID) for a diffusion model with respect to a particular data sample is determined by using the diffusion model's diffusion process to apply noise to a data sample and evaluate how the estimated log probability of the data sample changes at different levels of noise. Particularly, the differential of change in noise to change in log probability can be used to determine the local intrinsic dimensionality. This may be determined by evaluating the log probability at several noise levels and determining a slope of the difference. In additional examples, the differential is evaluated directly at a selected noise level. The selected noise level can be optimized by calculating the estimated LID for various data samples at a variety of noise levels and selecting the LID that corresponds to a “knee” where the estimated LID sharply changes.

The invention provides a data security analysis method and an intelligent calculation data security workstation, and the method comprises the steps that the intelligent calculation data security workstation obtains data of different data sources, and the data comprises a user operation log and a data access and transmission log; classifying and grading the data based on a preset rule base and a machine learning model, and marking sensitive data; analyzing a user behavior mode based on the user operation log, and identifying an abnormal behavior; and based on the data access and transmission log, evaluating a data leakage risk, and generating a risk report.

The invention discloses a big data stream-oriented memory cache storage system, relates to the technical field of memory cache storage, and is used for solving the problem of poor memory cache storage management. According to the method, the access frequency, the timeliness and the service importance of the data are evaluated, the priority ranking model is constructed, the cache strategy is optimized based on the ranking result, the data storage and scheduling efficiency is improved, the write-in and read operation of the memory cache is adjusted in real time in combination with the flow prediction model, memory overload is effectively avoided, and the data storage and scheduling efficiency is improved. According to the method, non-emergency data writing is delayed by adopting a delayed writing and lazy loading strategy, data is loaded as required, meanwhile, a cache elimination mechanism is designed according to data priority, access frequency, survival time and size, data migration is carried out when a cache space is insufficient, reasonable resource allocation is ensured, and through a multi-level and dynamic cache management scheme, the cache management efficiency is improved. Therefore, the system performance, the resource utilization rate and the response speed are remarkably improved, the data processing efficiency is optimized, and cache conflicts are reduced.

The invention relates to the technical field of transformer substation intelligent management, in particular to a modular prefabricated cabin transformer substation intelligent comprehensive management system with a multi-source data integration and collaborative management function. Comprising a multi-source data acquisition unit; the edge intelligent processing unit is used for carrying out local preprocessing, feature extraction and abnormal early warning on the acquired multi-source heterogeneous data of the transformer substation, and realizing data noise reduction, abnormal recognition and equipment health degree evaluation through a lightweight AI algorithm and a hybrid communication protocol carried by the edge computing terminal module; and a data integration collaboration unit. According to the invention, through an adaptive sampling strategy and a multi-modal feature fusion mechanism of the multi-source data acquisition unit, multi-dimensional data such as electric power parameters, power environment, fire protection and security protection and the like are incorporated into a monitoring system, feature layer association analysis is realized by depending on a Jousseme distance and a Dempster synthesis rule, the assessment limitation of single equipment and single-dimensional data in a traditional scheme is broken through, and the method has the advantages of high reliability and high reliability. And a more comprehensive state basis is provided for equipment health degree research and judgment.

The invention discloses a method for training and evaluating a large model in the field of table data analysis. Screening and scoring the obtained table and text data; compiling a plurality of sets of different prompt words, and making'question 'data and'question-answer' data pairs under a table data scene as basic training samples; performing data enhancement on the generated'question 'and'question-answer' sample pairs; collecting and screening common text questions and answers, logic mathematics and code generation task data, selecting a proper data ratio, and performing all-parameter instruction alignment training on the base model; collecting and making an evaluation data set, compiling an evaluation script, and running to obtain a corresponding evaluation index; and iteratively optimizing the training data set according to the evaluation index and the feedback of the evaluation set until the model reaches an expected index. According to the invention, the large model in the field of table data analysis obtained by training not only has strong table question and answer ability, but also has strong general problem solving ability.

The invention provides a data security risk assessment early warning system, which relates to the field of data risk assessment, and comprises a data collection module used for collecting security logs, network traffic information and access behavior information of data, a data analysis module connected to the data collection module and used for analyzing the collected data and assessing security risks faced by the data, and an early warning module connected to the data analysis module and used for assessing the security risks faced by the data. And the early warning module is connected to the data analysis module and is used for giving out early warning when the analyzed safety risk exceeds a preset threshold value. According to the application, through the data collection module, security logs, network traffic information and access behavior information of data are comprehensively collected, comprehensiveness and accuracy of risk assessment are ensured, and the data analysis module can accurately assess security risks faced by the data through a risk assessment model construction technology, analyze new data in real time and quickly judge the security risks.

The invention relates to the technical field of edge computing, and discloses a network data efficient caching method and system based on edge computing, which realize dynamic identification of hotspot data and optimal caching position decision by analyzing edge node data flow characteristics and network topology states in real time. Firstly, data packet meta-information is extracted from an edge node, a content feature vector is generated, and data popularity is evaluated; secondly, analyzing a network topology structure and a link state, and screening candidate cache nodes; then, factors such as service priority, network reliability and topology stability are fused, and an optimal cache position is determined by adopting a reinforcement learning algorithm; and finally, the cache content is updated, the cache state is synchronized between the edge nodes, and the cache distribution is optimized. The data access efficiency and the resource utilization rate of the edge network can be effectively improved, and the method adapts to dynamically changing network environments and user requirements.

The invention relates to the technical field of power equipment monitoring and diagnosis, in particular to a GIS partial discharge high-sensitivity monitoring and microdefect diagnosis system, which comprises a signal acquisition module, a signal processing module, a defect identification module and a data fusion module, wherein the signal acquisition module is used for acquiring partial discharge signals and related environment data in real time; the signal processing module is used for carrying out noise suppression and filtering processing on the partial discharge signals from the signal acquisition module and extracting feature data of the partial discharge signals; the defect identification module is used for diagnosing potential micro-defects and performing defect level evaluation; the data fusion module is used for generating a multi-dimensional equipment health condition report; according to the invention, through multi-dimensional data fusion and accurate signal processing and defect evaluation, the diagnosis precision of the partial discharge signal of the GIS equipment is improved, and comprehensive evaluation and fault early warning of the health state of the equipment are realized.

The invention provides an electric energy metering detection data quality evaluation method and system, and relates to the field of data processing. Data abnormity generated by a current transformer aiming at gradual change characteristics caused by contact oxidation is firstly eliminated through improved wavelet transform and feature extraction; the abnormal rate of the processed data is calculated according to a support vector machine model, the conditions of noise interference and abnormal fluctuation in actual current data are considered when a support vector machine is designed, and the contact resistance is slowly increased due to contact oxidation of the secondary winding of the current transformer; therefore, the problems that the gradual change abnormity is difficult to find in time and the electric charge metering deviation is easily caused by the existing fixed threshold value evaluation method are solved.

The invention discloses an industrial wastewater monitoring data authenticity verification method based on multi-modal feature fusion. The method comprises the following steps: carrying out adaptive preprocessing and feature extraction on monitoring data; performing integrity verification based on the spatial-temporal feature sequence; generating an encrypted two-dimensional code through multi-modal feature fusion; and evaluating data consistency and generating early warning information. According to the method, a complete verification chain from sensor physical feature extraction to data consistency evaluation is constructed, so that the authenticity of industrial wastewater monitoring data is comprehensively guaranteed; by adopting the technologies of adaptive data processing, multi-head attention feature fusion and the like, the key problems of data source anti-counterfeiting, feature extraction accuracy, multi-modal feature fusion and the like are effectively solved, and the reliability of data verification is improved.

The invention belongs to the technical field of agricultural Internet of Things, and discloses an Internet of Things-based intelligent agricultural cloud platform data acquisition and management system, which comprises the following steps of: acquiring plant rhizosphere microenvironment information, generating rhizosphere original data and performing correlation analysis to obtain rhizosphere electrochemical interaction data; collecting canopy microclimate information, and performing temperature and humidity gradient quantification processing and pore dynamic response characteristic extraction to obtain pore response characteristic data; collecting field illumination information, and carrying out multispectral distribution analysis and photoperiod sensitivity differentiation analysis to obtain a crop photoperiod sensitivity spectrum; performing multi-dimensional association fusion to generate crop physiological status comprehensive evaluation data; agricultural measure analysis and resource allocation optimization are carried out, and finally a resource allocation instruction is generated and agricultural operation scheduling is carried out; according to the invention, intelligent acquisition, analysis and management of data in each link of agricultural production are realized, and the utilization efficiency of agricultural resources and the production accuracy are improved.

The invention relates to the technical field of database capacity evaluation, in particular to a method for dynamically evaluating database capacity. The technical problems that in the prior art, a method for evaluating the capacity of a database may only pay attention to the current or short-term data volume, the possibility of future data increase is ignored, evaluation on the performance of the database may not be comprehensive or deep enough, and consequently capacity configuration cannot meet the performance requirement are solved; according to the technical scheme, the method for dynamically evaluating the database capacity comprises the steps that a prediction model is constructed, future storage requirements and load changes are predicted based on historical data, a machine learning algorithm is used for analyzing a use mode, resources are dynamically adjusted, and the resources are automatically increased or decreased according to the requirements; according to the method, by estimating the data growth rate and reserving the extra space, it is ensured that capacity configuration can meet future service requirements, and database performance is comprehensively evaluated and optimized by means of load testing, scene simulation and user behavior analysis.

The invention relates to the technical field of data processing, and discloses a large steel structure hoisting precision real-time monitoring method and system. The method comprises the following steps: scanning and collecting the edge of a steel structure through a distributed sensor network, and obtaining structure data to obtain an original monitoring data set; extracting and analyzing structure transient response features based on the original monitoring data set, and generating a state feature matrix; analyzing a coupling relationship between local deformation and overall torsion to obtain a dynamic response prediction matrix; performing space-time registration and data fusion on the monitoring data, and constructing a state evaluation data set; carrying out optimization calculation on the hoisting parameters to generate a hoisting control data packet; and performing intelligent reasoning analysis in combination with historical case data, and outputting a control instruction sequence. Real-time monitoring of the coupling relation between local deformation and overall torsional deformation in the hoisting process of the large steel structure is achieved, and the technical defect that in the prior art, the structural deformation development trend cannot be accurately recognized and predicted is overcome.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention discloses a test evaluation method and device for an industrial park integrated management system. The method comprises the following steps: acquiring a performance index data set of the industrial park integrated management system; the performance index data set comprises a performance index test data subset of each subsystem; the performance index test data subset comprises a test data sequence of each performance index; pre-processing the performance index data set to obtain a to-be-evaluated data set; and performing test evaluation processing on the to-be-evaluated data set to obtain a performance evaluation result value of the industrial park integrated management system.

The invention discloses an automatic software function testing and evaluating method based on a multi-modal large model, and particularly relates to the field of non-embedded software function testing and evaluating, which comprises the following steps: collecting software texts, images and structured data, generating corresponding feature vectors, aligning through a cross-modal comparison algorithm, and constructing a multi-modal testing and evaluating data set; deep features of all modes are extracted through a mode exclusive encoder, and a unified multi-mode function vector is output based on information entropy dynamic weighted fusion; constructing triple training data, and optimizing the multi-modal large model by combining a joint loss function with an enhanced training strategy; and finally, deploying the model at a cloud end and an edge node, carrying out real-time triggering test by docking a CI / CD process, and realizing functional integrity verification, interactive compliance detection and accurate defect positioning through semantic vector comparison, graph isomorphism rate calculation and cross-modal attention backtracking. According to the scheme, the test comprehensiveness and precision are improved, and the rapid iteration requirement of software is met.

Systems and methods for dynamically evaluating metadata consistency and data reliability in a data management system are disclosed herein. The system may retrieve first metadata and second metadata. The system may retrieve a metadata ruleset. Based on the metadata ruleset, the system may generate a first metadata consistency metric indicating a first measure of consistency. The system may determine to process each record of the first metadata as a batch. The system may generate a second metadata consistency metric indicating a second measure of consistency. The system may determine to process each record of the second metadata independently.

The invention discloses a construction monitoring system for a horizontal warehouse arched roof based on multi-modal data fusion, relates to the technical field of construction monitoring, and is used for solving the problem of poor structural construction anomaly recognition. The method comprises the steps of collecting multi-modal data and completing synchronous preprocessing, setting a sampling frequency according to modal characteristics, dividing construction stages, constructing a stage response function, dynamically updating during stage switching, fitting an actual observation value and a prediction value, extracting a modal response deviation degree, constructing a credibility function and evaluating data reliability. Further analyzing a residual trend, adjusting an abnormal score in combination with credibility, identifying a collaborative abnormal behavior between modals, systematically constructing a modal causal graph, identifying an abnormal propagation path, extracting modal conduction characteristic information, and triggering a risk response strategy according to the modal causal graph, so that dynamic tracking of the structural abnormality in the construction process of the horizontal warehouse arched roof is realized; and the accuracy of construction process monitoring identification is improved.

The invention relates to the technical field of data resource management, in particular to an online evaluation method for data resource value, which comprises the following steps of: constructing a multi-dimensional evaluation index set, and acquiring metadata and market dynamic information of to-be-evaluated data in real time through a distributed interface; carrying out weight distribution on the index set based on a dynamic weighting algorithm, generating a weight coefficient set and constructing a value calculation model; performing standardized preprocessing on the collected original data, inputting the data into the model, and calculating an initial estimated value; and dynamically correcting the initial value estimation value based on the timeliness attenuation factor and the data scarcity factor, and generating a final value estimation value and an estimation report. Through multi-dimensional index construction, a dynamic weighting algorithm, standardized preprocessing and two-factor correction, automatic evaluation of the data value is realized, the problems of single dimension, static weight and neglect of timeliness scarcity of a traditional method are solved, the real-time performance and scientificity are improved, and a quantitative decision basis is provided for data capitalization.

The invention presented relates to a method (100) for determining a state of at least one electrochemical cell (301) of an electrochemical energy converter (300), the procedure (100) comprises: - Determining (101) a trend of values ​​determined by at least one sensor (303) of the energy converter (300) over time, - Determining (103) evaluable data packets, - Aggregating (105) at least one range of respective evaluable data packets (205, 209) of values ​​determined by the at least one sensor into at least one aggregated trend, - Assigning (109) a characteristic value to the at least one aggregated trend using a machine learner (400), including a runtime parameter that specifies a runtime of the at least one electrochemical cell (301), - Output (111) of the characteristic value on an output unit, wherein an evaluable data package comprises a multitude of data points whose values ​​differ from each other by no more than a predetermined threshold, at least for a given duration.

The invention relates to the technical field of sensors, and discloses a detection and management method and system for purification equipment. The method comprises the following steps: carrying out three-phase coupling dynamic modeling on a gas phase flow state, a particle phase movement track and a sensor phase interface characteristic in the purification equipment to obtain a gas mass transfer characteristic parameter; performing response characteristic analysis on a real-time detection signal of a sensor array in the purification equipment to obtain a sensor calibration compensation parameter; based on the sensor calibration compensation parameters and the gas mass transfer characteristic parameters, deposition kinetics analysis of pollutants on the surface of the sensor is carried out, and equipment operation state evaluation data are obtained; and performing spatial layout optimization on the sensor array according to the equipment operation state evaluation data and the gas flow consistency constraint condition to obtain an optimal layout scheme and generate a staged adaptive management control instruction. According to the invention, self-adaptive adjustment and intelligent switching of the sensor management strategy are realized, and the detection performance and reliability of the purification equipment sensor system are significantly improved.

Various embodiments include methods and systems for automatically evaluating data structures and / or data management documentation for compliance with relevant standards, requirements, and policies. Various embodiments provide a framework for automating compliance reviews of data structures and / or data management against the latest version of standards, requirements, and policies. Methods may include transforming input requests for analysis and data structures and / or data management documents into formats suitable for processing by a large language model (LLM), prompting the LLM to identify concepts and / or elements that are likely subject to a standard, requirement, or policy, submitting a query to a vector database to identify standards, requirements, or policies relevant to the requested analysis and documents, prompting the LLM to assess compliance the data structures and / or data management documents with standards, requirements, or policy, and producing a report on compliance of the data structures and / or data management documents with the relevant standards, requirements, or policies.

The invention discloses a sensor fault diagnosis method, which comprises the following steps of: reading acquired data of a sensor in real time, and recording the data as a sensor reading at the current moment; judging whether the obtained sensor reading is within a preset threshold range or not; calculating sensor reading increments of adjacent time points, constructing increment distribution histograms of a plurality of time points, counting frequencies of different increment intervals, extracting increment distribution characteristic values, judging a ratio relation of the increment distribution characteristic values, and performing moving average processing and regression analysis on all sensor readings to obtain a plurality of sensor reading values; calculating a regression coefficient R to evaluate the linear relation strength between the data; comparing the Rvalue with a preset regression coefficient value; and judging whether the regression coefficient R is smaller than the preset value. Accurate diagnosis and positioning of sensor faults are realized, the false alarm rate is reduced, the reliability of a detection result is improved, and the method has universality, can be suitable for various types of environmental sensors, and meets different environmental monitoring requirements.

A user specific tic behavior detection-based method and system of performing an authentication of video of a person in order to authorize access to a secured resource. The user provides audio and / or video data in which they exhibit one or more tic behaviors. When a person later requests access to the user's account, the system and method are configured to capture a video and / or audio recording of the person requesting access. The data is evaluated to determine whether the previously detected tic behavior is present. If the tic behavior is present, the system determines the image or voice is authentic and can verify an identity of the person. In some cases, the system can further be configured to automatically grant the person access to one or more services, features, or information for which he or she is authorized.

The invention discloses a coal-bed gas well productivity evaluation method based on artificial intelligence, and the method comprises the steps: collecting the production data of an existing coal-bed gas well, carrying out the preprocessing of the production data and basic data, inputting the basic data into a coal-bed gas well production condition prediction model, and obtaining the predicted production data, comparing the predicted production data with actual production data to obtain an actual-to-actual proportion, performing influence analysis on the basic data according to a change correlation degree to obtain a predicted change coefficient, and constructing a production capacity function according to the actual-to-actual proportion and the predicted change coefficient, and constructing a coal-bed gas well productivity prediction model according to the production capacity function, optimizing the coal-bed gas well productivity evaluation model, inputting to-be-evaluated data into the coal-bed gas well productivity evaluation model, and outputting an evaluation result. The method not only can improve the precision of coal-bed gas well productivity evaluation based on artificial intelligence, but also has good interpretability, and can be directly applied to coal-bed gas well productivity prediction of field production.

The invention discloses a multi-level dynamic isolation data processing system and method, and relates to the technical field of data security, the system comprises a data access module supporting a cloud native environment and a hybrid deployment mode, a strategy matching module, a sandbox execution module and an audit analysis module; the data access module receives the data, evaluates the sensitivity level and the purpose of the data, and labels the data based on an evaluation result; the strategy matching module reads the data label, searches an isolation strategy and an auditing rule which are matched with the data label from a strategy library, and automatically loads the strategies to the sandbox execution module once the matched strategies are found; the sandbox execution module selects an isolation mode according to the sensitivity level of the data, and processes and analyzes the data in an isolation environment according to a preset rule and algorithm; and the auditing analysis module records all operations on the data in real time, and analyzes the operation compliance based on a preset auditing rule. The problems of large resource consumption, insufficient strategy flexibility, coarse behavior audit granularity and the like in the existing data sandbox technology can be solved.

The invention discloses a bidding and tendering risk early warning method and system based on artificial intelligence, and relates to the field of artificial intelligence risk assessment. Bidding and tendering related data and bidding and tendering enterprise credit data are collected from multiple angles and multiple sources, and are stored in a bidding and tendering database after format alignment is completed; calling bidding and tendering data in the bidding and tendering database, generating a text data set, analyzing financial data based on the text data set, extracting trend characteristics, and calculating a financial ratio; cyclically traversing the text data set, and obtaining keywords and key sentences with bidding and tendering relevance higher than a preset threshold in the text data set through text similarity and keyword feature extraction; and constructing a risk prediction model, inputting the financial ratio, the keywords and the key sentences into the risk prediction model, outputting an early warning value, and determining a current bidding and tendering risk level. According to the invention, the accurate threshold value is preset according to the project and the history, and the evaluation data is updated in real time, thereby realizing real-time accurate early warning to assist related parties in reducing loss.

The invention relates to a correlation analysis method and device for computing power resources of a data center and energy consumption of refrigeration equipment, and the method comprises the steps: obtaining related data of the computing power resources of the data center and the energy consumption of the refrigeration equipment, carrying out the correlation analysis of the preprocessed related data, and obtaining key parameters related to the computing power resources of the data center and the energy consumption of the refrigeration equipment; selecting a model according to the key parameters and the modeling target, training the selected model by using historical data to learn the correlation between the computing power resource and the energy consumption of the refrigeration equipment, and evaluating the performance index of the selected model; according to the modeling target and the constraint condition, an optimization algorithm is selected to carry out joint optimization logic of the data center computing power resources and the refrigeration equipment energy consumption, the optimization algorithm is operated, and distribution of the data center computing power resources and operation parameters of the refrigeration equipment are adjusted through iteration; and obtaining a performance index for evaluating the correlation between the computing power resource of the data center and the energy consumption of the refrigeration equipment. According to the method, the prediction accuracy, generalization ability and interpretability of the model are remarkably improved.

The invention discloses an equipment abnormity early warning method and system based on machine learning, and the method comprises the steps: collecting the working data of equipment, carrying out the preprocessing of the working data, screening the operation data, obtaining abnormal data, importing the abnormal data into an abnormal function, obtaining an abnormal coefficient, and carrying out the early warning of the equipment abnormity. Decomposing the abnormal coefficient and the state data characteristics to obtain a characteristic vector, comprehensively evaluating the state data to obtain a state evaluation value, and calculating the similarity between the abnormal coefficient and the state evaluation value to obtain an abnormal state degree, and according to the feature vector and the abnormal state degree, constructing an equipment abnormity early warning model based on machine learning, inputting to-be-evaluated data into the equipment abnormity early warning model to obtain an equipment abnormity early warning value, and according to the equipment abnormity early warning value, carrying out abnormity early warning. The method not only can improve the precision of the equipment abnormity early warning method, but also has good interpretability, and can be directly applied to an equipment abnormity early warning system.