864 results about "Evaluated data" patented technology

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 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 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 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 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 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 relates to the field of image processing, and discloses an eye fundus image segmentation method, device and equipment and a storage medium, and the method comprises the steps: collecting an eye fundus image of a patient, and carrying out the data preprocessing of the eye fundus image of the patient, and obtaining original image data and evaluation data; performing physical enhancement on the original image data to obtain enhanced image data; establishing a convolutional neural network, and performing feature extraction on the enhanced image data by using the convolutional neural network to obtain feature data; performing adaptive weighting processing on the feature data to obtain weighted data; performing morphological operation on the weighted data to obtain to-be-evaluated data; performing data evaluation on the to-be-evaluated data by using the evaluation data to obtain an evaluation result, and obtaining output data according to the evaluation result; feature extraction is performed by using the convolutional neural network, and feature weighting processing is performed on the fundus image by using adaptive weighting processing, so that the ability of analyzing the multi-feature fundus image is improved.

The invention provides an identification detection method and system for a radio frequency connector, and the method comprises the steps: obtaining the initial appearance image data of the surface of the radio frequency connector, and carrying out the processing of the image data, and obtaining a first appearance image; extracting an appearance feature set and an identification area image from the first appearance image; performing character segmentation and feature extraction on the identification area image, obtaining a preliminary manufacturing information text in combination with a pre-established text recognition model, verifying the preliminary manufacturing information text and associating the preliminary manufacturing information text with the appearance feature set to generate an associated data set; comparing the appearance feature set with a preset standard appearance database to generate defect marking data, fusing the associated data set with the defect marking data to generate comprehensive analysis data, and generating a visual chart according to the comprehensive analysis data; and further analyzing performance index change and defect severity according to the visual chart, generating risk assessment data, and generating a final detection analysis file according to the risk assessment data.

The present invention discloses a rule-based automated data governance system and method, and relates to the technical field of data management. The system of the present invention includes: a historical data analysis and rule base construction module, a real-time demand analysis and demand determination module, a data governance simulation and effect evaluation module, and a data governance rule adjustment and application module; the historical data analysis and rule base construction module collects and analyzes historical business demand information and historical data, and constructs a data governance rule base; the real-time demand analysis and demand determination module obtains real-time business demand information and data to be governed, and determines whether it is new business demand information; the data governance simulation and effect evaluation module performs data governance simulation on new demands based on historical data governance rules, and evaluates the data governance effect; the data governance rule adjustment and application module outputs adjustment suggestions according to the evaluation results, supports personnel to adjust and apply real-time data governance rules, and optimizes the data governance process.

The invention relates to the technical field of information security, and particularly discloses an information security protection method and system applied to an electronic information platform, and the method comprises the steps: collecting information data, evaluating the reliability of a data source based on a Bayesian network model, analyzing the structural integrity of the information data through an auto-encoder algorithm, and calculating a comprehensive accuracy coefficient; based on an accuracy evaluation result, extracting a data exception rate and an access exception frequency, training a decision tree model in combination with historical data, establishing a risk prediction model, and dynamically calculating a risk coefficient; according to the real-time change of the risk coefficient, generating defense strategies such as data correction, access control, flow limitation and high-risk operation isolation, and re-evaluating the risk coefficient and data accuracy of the platform after execution; potential risks can be identified, various security threats can be flexibly handled, data loss and security vulnerabilities are reduced, and the stability and the anti-risk capability of an electronic information platform are improved.

The invention discloses a medical data set construction method based on AIGC image generation, and belongs to the technical field of image generation models, and the method specifically comprises the steps: constructing a medical field semantic constraint matrix, generating a structured prompt template library, inputting a real medical image and the structured prompt into a diffusion model through LORA fine tuning, and building a mapping relation to generate data. Verifying the reasonability of an anatomical structure for generated data; enabling organ morphological parameters to accord with medical priori knowledge through cyclic correction; generating variants of different scales according to target focus features; forming a three-dimensional continuous parameter space; and extracting a shared feature base layer for pre-training, improving the weight of real data in the later stage of training, monitoring a feature response mode in the training process, and generating a same-type evaluation data set test in real time when an index does not accord with a standard until the standard is reached.

The invention belongs to the technical field of new energy electric power, and discloses a new energy bearing intelligent evaluation and regulation system and method, the system is composed of a data acquisition module, an intelligent evaluation module, a prediction module, a regulation strategy module and an execution and feedback module, and the data acquisition module acquires meteorological, historical operation, load and power grid state data in real time; the time sequence of the prediction module and a deep learning algorithm are combined, short-term to medium-and-long-term prediction is carried out on new energy output and load change, output change is captured in real time, regulation and control lag is avoided, power grid risks are quantified through a multi-model fusion algorithm, and node accessible capacity and a risk assessment result are output; and the regulation and control strategy module generates a regulation and control instruction by utilizing optimization algorithms such as a genetic algorithm based on the evaluation result and the prediction data, and realizes closed-loop control through the execution and feedback module, so that the evaluation data directly guides regulation and control actions.

The invention relates to the technical field of background investigation, and particularly discloses a talent background investigation method based on multi-source data evaluation, which comprises the steps of constructing a dynamic talent knowledge graph, performing semantic alignment processing on multi-source cross-modal data, evaluating data quality based on a multi-dimensional confidence model, and constructing a confidence closed-loop correction mechanism. Performing intelligent correction on the low-confidence data, and reversely updating the credibility weight of the data source according to a correction result; an event-driven incremental update architecture is adopted, and dynamic events of multi-source data are captured in real time through a distributed event awareness engine. According to the method, the dynamic knowledge graph is used as a unified carrier, and the multi-source data is upgraded from fragmented storage to a semantic association network, so that the talent background survey data is converted from static piling to a dynamically evolved knowledge network, and the accuracy and timeliness of evaluation are remarkably improved.

The invention relates to the field of industrial data detection, in particular to an industrial production data anomaly detection system, which comprises a production monitoring module used for detecting a production control difference parameter and a set drift parameter and determining a production data state of a time sequence training data set; the window analysis module is used for responding to the window evaluation condition to determine a window setting strategy of the time sequence training data set; the extraction execution module is used for performing training execution window extraction based on the window setting strategy determined by the window analysis module; the training execution module is used for training the anomaly analysis model based on the acquired training execution windows; and the anomaly evaluation module is used for carrying out reconstruction anomaly analysis on each window analysis data of the to-be-evaluated data to determine a weighted anomaly index of each window analysis data, and responding to the early warning evaluation condition to determine anomaly detection data, and the anomaly detection efficiency of the multivariate time series data is improved.

The invention discloses an intelligent prediction and uncertainty analysis method for pose errors of a six-degree-of-freedom motion platform, and the method comprises the steps: taking an expected pose in a high-precision six-degree-of-freedom motion platform control system as network input data, and obtaining an actual pose through employing a binocular vision measurement system; collecting multiple groups of data, normalizing the data, and dividing the data into a training set and a test set; constructing a pose error prediction network model taking a gated circulation unit neural network as a basic framework, designing a translation and rotation feature coupling attention module, introducing a sparse attention mechanism guided by domain knowledge, and improving the capturing capability of the model for the real mechanical coupling characteristics of the six-degree-of-freedom platform; and inputting a test set into the trained model, and evaluating data of the test set by adopting a Monte Carlo Dropout method to obtain a pose error prediction result and an uncertainty analysis result. According to the method, the static pose precision of the six-degree-of-freedom motion platform can be effectively improved, a visual and reliable network prediction confidence level is provided, and the precision measurement requirement in the metering field is met.

The invention discloses an enterprise ESG risk assessment system and method, and the method comprises the steps: collecting environment assessment data, management related files and enterprise social media data of an enterprise, and collecting social assessment quantitative data; dividing the enterprise social media data into social related data and governance related data by using a data classification model, and obtaining a comment positive-negative ratio of the social related data based on an emotion dictionary; according to a formula, calculating to obtain an environmental risk index and a social risk index, and according to the text analysis large model, obtaining a governance risk index; obtaining the weight of each risk index by using an analytic hierarchy process, and obtaining an ESG evaluation index through a weighted summation method; judging whether the risk indexes and the ESG evaluation indexes exceed corresponding threshold values or not; if yes, sending an early warning signal; and if not, sending a normal signal. The invention relates to the technical field of enterprise risk assessment, and solves the technical problem that the assessment result is not accurate enough due to the fact that traditional ESG assessment data is difficult to quantify.