48 results about "Data level" patented technology

Aiming at privacy protection mean value estimation of multi-dimensional numeric data, a personalized privacy protection mechanism meeting-localization differential privacy is designed, and personalized privacy protection of a user level and a data level is provided. According to the invention, each user can select one privacy protection level from a plurality of preset privacy protection levels according to own privacy protection requirements, so that personalized privacy protection of the user level is realized, which is the first personalized privacy protection. And based on the expectation of the minimum estimation variance, determining an optimal dimension extraction parameter, and randomly extracting part of dimension data from all dimensions to carry out disturbance submission. And a user scoring strategy is adopted to determine a distribution strategy of privacy protection parameters, so that personalized privacy protection on a user data level is realized, which is second personalized privacy protection. And finally, a weighting factor is constructed based on the expectation of the estimation variance, and a weighted combination mode is adopted for mean value estimation under multiple privacy levels, so that the accuracy of the overall mean value estimation is further improved.

The invention relates to the technical field of production quality control, and discloses a production data hierarchical processing and quality tracing system. The system comprises a production data acquisition module, a data hierarchical processing module, a quality fluctuation index module, a multi-dimensional quality difference analysis module and an abnormal source positioning module. The production data acquisition module acquires a real-time parameter data sequence of each production link; the data layering processing module is used for layering the data into a plurality of data hierarchies; the quality fluctuation index module obtains a quality fluctuation index based on the fluctuation condition of the data hierarchy; the multi-dimensional quality difference analysis module analyzes the multi-dimensional quality difference based on the index; and the abnormal source positioning module positions a production abnormal source based on the multi-dimensional quality difference. According to the system, through hierarchical data processing, quality fluctuation quantification and multi-dimensional difference analysis, accurate positioning of a production abnormal source is realized, and systematicness and accuracy of production data processing and quality tracing are improved.

The invention relates to the technical field of multi-source data fusion, in particular to a multi-source data fusion-based intelligent detection method for a transportation state of combined transportation of iron and water, which comprises the following steps of: in a transportation process of combined transportation of iron and water, obtaining at least two monitoring data sequences, and in a process of carrying out multi-source data fusion on all the monitoring data sequences, carrying out multi-source data fusion on all the monitoring data sequences; performing feature extraction on each monitoring data sequence through data level fusion and feature level fusion to obtain at least two feature sequences; classifying all the feature sequences, obtaining an optimal attention mechanism according to the category to which each feature sequence belongs, performing decision-level fusion on all the feature sequences according to the optimal attention mechanism of each feature sequence to obtain a transportation state detection result of the combined transportation of the iron and the water, and obtaining a detection result of the transportation state of the combined transportation of the iron and the water through analysis of a self-adaptive attention mechanism. And the robustness of transportation state detection based on a multi-source data fusion technology in a railway-iron combined transportation scene is improved.

The invention discloses a segmented multi-source shoreline extraction and calibration method and system based on physical prior, and particularly relates to the technical field of remote sensing image processing and computer vision, a consistency evaluation and reliability measurement mechanism of a multi-source surface water product is integrated on a data level, and a high-quality training sample set is constructed by using water persistence characteristics; on the model level, a feature extraction framework fusing physical prior guidance and a geometric self-adaptive operator is researched and developed, a multi-dimensional physical constraint and noise tolerance loss function is constructed, and pixel-level accurate recognition and topological rigorous quality closed-loop calibration of a shoreline under a large-range remote sensing image are achieved.

The invention discloses a big data problem clue mining method based on a mutual exclusion rule. Relates to the technical field of big data supervision, in particular to a big data problem clue mining method based on a mutual exclusion rule. According to the method, 'exclusive contradiction between data records' is taken as a core entry point and analysis dimension, and key features which violate logic consistency are accurately captured on a data level. The method comprises the following steps: acquiring a multi-source heterogeneous data set, and preprocessing the multi-source heterogeneous data set to obtain an initial feature set and an entity subgraph; extracting mutually exclusive line features according to the initial feature set; based on the mutually exclusive line features, evidence features are generated; constructing a dual-channel model to calculate a mutual exclusion risk; using a decision tree improved algorithm as a rule enhanced feature channel; and constructing a graph relation perception channel: aggregating the abnormal probability of the feature space and the abnormal probability based on the graph structure to obtain a risk score through a dynamic weighting mode.

A method for training a machine learning algorithm including uncertainties. The method includes the following steps: for each point in time of the plurality of points in time, determining in each case an influence, which the data detected at the corresponding point in time have on uncertainties instantaneously contained in the initial model, for each point in time of the plurality of points in time, determining a resolution of the corresponding detected data based on an established data level and on the respective influence, which the corresponding data have on uncertainties instantaneously contained in the initial model, for each point in time of the plurality of points in time, transferring the detected data to the control unit based on the corresponding determined resolution, and retraining of the initial model by the control unit based on the data transferred to the control unit.

Disclosed is a method for managing gas data, which is implemented by at least one processor of an Internet of Things (IoT) system for managing the gas data. The method comprises: obtaining to-be-stored-gas data and downstream user features; determining a user importance level based on the downstream user features; determining accessing frequency distribution features of the to-be-stored-gas data; determining a risk degree of data through a second prediction model; constructing query feature vectors based on pipeline data, and determining a risk degree of the gas pipeline based on the query feature vectors; determining a gas data level; determining a data redundancy level; generating redundant data blocks of the to-be-stored-gas data; and storing the to-be-stored gas data and the redundant data blocks in the at least one storage node of the at least one sub-data center based on loading distribution features.

This application provides a method for selecting training corpus based on the results of a text classification model fusion using multiple loss functions. At the model level, by fusing multiple loss functions, the text classification model adaptively learns the weights of each loss function on the classification performance, thereby improving its robustness. At the data level, based on the results of the text classification model fusion using the aforementioned multiple loss functions, variance calculation is used to determine the quality of the output category division of the training sample data, identifying poor-quality data for review and processing. The text classification model is then retrained based on the processing results to improve its classification or prediction performance. By calculating the confusion between output categories, a quantitative score is generated for the classification system of the text classification model, serving as the basis for adjusting the classification definitions within the model, thereby improving its prediction performance.

The invention relates to the technical field of power system reliability evaluation, in particular to an offshore wind power submarine cable transmission power probability safety evaluation method and system. According to the method, multi-dimensional data management, dynamic prediction modeling and self-adaptive decision optimization are creatively fused, so that double breakthrough of safety assessment precision and robustness is realized. Abnormal data removal, blank value filling and normalization are carried out on the data in the data level, data noise is reduced, and key features are completely improved; in a prediction level, an Att-BiLSTM model is constructed, and wind power output prediction is realized through bidirectional time sequence feature extraction and attention weight dynamic distribution; in the aspect of probability safety assessment, a deep reinforcement learning framework based on a PPO algorithm is designed, the submarine cable power safety margin is dynamically described, full-chain technical support is provided for offshore wind power high-reliability grid connection and power transmission safety monitoring, and the method has wide industrial application prospects.

The application discloses a kind of track traffic equipment health degree prediction method and system based on multi-source information fusion, it is related to track traffic equipment health degree prediction technical field, including: the multidimensional fault index data of track traffic equipment is collected;Data dimension reduction processing is carried out;The time series analysis is carried out to the fault feature vector after dimension reduction processing in combination with time stamp data;Health degree prediction is carried out by integrating algorithm model coupling time series analysis result and multi-source data;Based on health degree prediction value, the overall operation state of equipment and potential failure risk are evaluated and early warning.The application realizes the cooperation of data level, algorithm level and optimization level, the fault feature output by PCA model is one of the inputs of Prophet and XGBoost model, and XGBoost also combines other multi-source data for comprehensive prediction, the accuracy, real-time performance and reliability of prediction are ensured through effective model cooperation, which helps to discover potential faults and problems in time.

Real-time monitoring and analysis of data anomalies is described. An example system for detecting data anomalies includes a network interface configured to receive individual information from each of a plurality of individual devices, the individual devices being computing devices. The system also includes a processing unit configured to extract relevant data indicators from the individual information using predefined algorithms, compute a statistical value indicative of a collective data level of the plurality of individual devices by integrating the relevant data indicators, and dynamically adjust data monitoring parameters based on real-time data to enhance accuracy. The system also includes a memory unit configured to store the individual information, the relevant data indicators, and the computed statistical value. The system also includes a feedback module configured to provide personalized data management recommendations associated with the individual devices based on their collective data levels and predefined data relief protocols.

The application provides a sintering flue gas CO emission concentration prediction method, and relates to the field of sintering process pollutant treatment. It comprises the following steps: S1, data acquisition and preliminary rejection; S2, constructing a sample data set and preprocessing it; S3, characteristic value screening, based on the preprocessed sample data set, combining a nonlinear correlation analysis method and a model-driven recursive elimination strategy, constructing an optimal feature subset; S4, integrated modeling, adopting a Stacking integrated learning framework, integrating the advantages of XGBoost, random forest and gradient boosting tree algorithms, and constructing an effluent CO concentration prediction model. Through multi-layer screening of characteristic values, the application clearly determines the dominant action mechanism of each process link on CO generation from the data level, and significantly improves the prediction accuracy and robustness compared with existing models.

The application discloses a segmented multi-source shoreline extraction calibration method and system based on physical prior, and particularly relates to the technical field of remote sensing image processing and computer vision, and integrates a consistency evaluation and reliability measurement mechanism of multi-source surface water products at a data level, and constructs a high-quality training sample set by using water body persistence characteristics; at a model level, a feature extraction architecture is developed by fusing physical prior guidance and a geometric self-adaptive operator, a multi-dimensional physical constraint and a noise tolerance loss function are constructed, and pixel-level accurate identification of a shoreline under a large-range remote sensing image and quality closed-loop calibration with a rigorous topology are realized.

The application discloses an engineering project cost whole-process collaborative management method and system, relates to the technical field of engineering project cost management, and comprises a cost matter main line construction module, which is used for receiving project estimation, design budget estimate, bidding control price, contract price, process measurement, change visa, claim and item data of final account, performing unified matter code mapping and cross-stage integration processing, and generating cost matter unit data. In the application, the project estimation, the design budget estimate, the bidding control price, the contract price, the process measurement, the change visa, the claim and the item data of the final account are subjected to the unified matter code mapping and the cross-stage integration processing by the cost matter main line construction module, the cost matter unit data is generated, cross-stage item integration record data is formed by the different-stage item data, and thus a stable main line structure is established at the data level, and consistent organization and centralized management of the cross-stage item data are realized.

The invention discloses a target detection method and device based on multi-sensor fusion and an automatic driving vehicle, and relates to the field of target detection, and the method comprises the steps: obtaining image data and point cloud data after completing the time synchronization of an image collection module and a radar module; determining first position information and categories of the S first targets under the image coordinate system according to the image data and a preset deep learning algorithm; determining second position information of T second targets in the point cloud coordinate system according to the point cloud data and a preset semantic segmentation algorithm, and further determining spatial perception information; and completing association of the first target and the second target belonging to the same detection target based on the conversion matrix. According to the scheme, the first target is determined by directly utilizing the image data, the second target is determined by utilizing the point cloud data, and fusion on the data level is realized based on the conversion matrix, so that respective advantages of the image acquisition module and the radar module are well utilized, and the perception precision of the target is improved.

This invention discloses a method for inverting and cascading early warning of hidden dangers in reservoir groups based on spatiotemporal network coupling. The method includes: acquiring hydrological time-series monitoring data and geospatial attribute data of reservoir groups in the region, and constructing a multi-source feature matrix including water level data and elevation information; filtering water level data, constructing a cumulative deviation curve, and judging the hidden leakage trend; constructing a multi-dimensional resilience assessment model based on water level data, obtaining the comprehensive resilience score of each reservoir node, and mapping the comprehensive resilience score to the node's anti-disturbance ability. By fully exploring the value of existing water level and rainfall monitoring data, and using neighborhood dynamic benchmarks and residual accumulation models, natural evaporation and meteorological interference can be effectively filtered out at the data level, and hidden leakage signals inside the dam can be interpreted from conventional hydrological data, providing a low-cost and universally applicable safety monitoring path for small reservoir groups.

A big data problem clue mining method based on mutual exclusivity rules. It relates to the technical field of big data supervision, and particularly relates to the technical field of a big data problem clue mining method based on mutual exclusivity rules. The present application takes the mutual exclusivity contradiction between data records as the core breakthrough point and analysis dimension, accurately captures at the data level, and extracts the key features that violate logical consistency. The method comprises the following steps: obtaining a multi-source heterogeneous data set and pre-processing to obtain an initial feature set and an entity subgraph; extracting mutual exclusion features according to the initial feature set; generating evidence features based on the mutual exclusion features; constructing a double-channel model to calculate the mutual exclusion risk: using a decision tree improvement algorithm as a rule-enhanced feature channel; constructing a graph relationship perception channel: through a dynamic weighting method, the abnormal probability of the feature space and the abnormal probability based on the graph structure are aggregated into a risk score.

The invention belongs to the technical field of industrial process monitoring and fault diagnosis, and provides a heavy medium coal separation process fault detection method based on a dynamic and static mixed graph, which can be wholly called a space-time synchronous attention network (HG-STAN) based on the dynamic and static mixed graph. The method comprises the steps that key variable data in the dense medium coal separation process are collected and preprocessed; constructing a static graph based on process prior knowledge, learning dynamic association from data by using a self-attention mechanism, and fusing to form a dynamic and static mixed graph; constructing space neighborhood information and historical time information of synchronous aggregation nodes of a space-time synchronization graph attention auto-encoder (STGAAE); training the model by using a combined loss function including reconstruction loss and graph structure sparsity regularization; establishing a multi-level monitoring system of data level statistics and graph level statistics; and each statistic control limit is determined through kernel density estimation, and online fault detection is realized. According to the method, the accuracy and sensitivity of fault detection in the dense medium coal separation process are remarkably improved.

The invention discloses a water quality monitoring system and method for environmental protection, relates to the technical field of water quality monitoring, and combines water quality data and environmental data to form multi-dimensional data fusion analysis and avoid the limitation of single parameter analysis, so that the system has global water quality change judgment capability. The obtained water quality data and environment data are combined and fitted into the original data set W, and optimization is carried out on the data level, so that the data have higher consistency and integrity, the influence of missing data on subsequent analysis is reduced, and the data stability is improved. By monitoring the complexity of the algae community structure, the eutrophication degree of the water body can be accurately evaluated, and an accurate regulation and control basis is provided for water quality treatment; a high-precision sensor and a remote sensing technology are adopted, automatic, remote and continuous collection of water quality data is achieved, manual intervention is reduced, the monitoring cost is reduced, the efficiency and the real-time performance of data acquisition are improved, and a foundation is laid for intelligent water affair and automatic environment management.

The invention discloses a sensing task processing method and device, computer equipment and a storage medium. The method specifically comprises the following steps: receiving a first perception task request which is sent by an AF network element and carries perception demand information; and determining a target perception data level from the candidate perception data levels according to the perception demand information. And sending a second sensing task request carrying the target sensing data level to the RAN. And the RAN obtains a target data parameter based on the target sensing data level, and sends the target data parameter to the SPF network element, so that the SPF network element processes the target data parameter to obtain a sensing task processing result. According to the method and the device, hierarchical labor division aiming at the perception requirements is realized, so that the RAN acquires the target data parameters in a targeted manner based on the target perception data level, the flexibility of processing the perception task is improved, the perception requirements of perception services in different scenes are met, and the user satisfaction is improved.

This invention provides a remote sensing data management method, apparatus, device, storage medium, and product. The method includes: upon receiving a retrieval request from a user terminal for retrieving remote sensing data, acquiring the requested data present in the retrieval request; calculating the target image boundaries of the requested remote sensing image based on the request data; determining a storage index based on the target image boundaries and the data level of the request data; the storage index indicating the storage location of the data corresponding to the retrieval request in the target storage level of a preset remote sensing image management system; retrieving the remote sensing image from the remote sensing image management system according to the storage index; and outputting it to the user terminal. In other words, this invention improves the efficiency of remote sensing data management, thereby enhancing the near real-time application of remote sensing images.

The invention discloses an oversampling and software defect prediction method based on space mapping and probability constraint. The method comprises the following steps: firstly, constructing a minority class probability score model by using dual-kernel density estimation, and realizing layered operation on a preprocessed defect sample; secondly, calculating the number of synthetic samples based on layered weight, performing three-dimensional space mapping and adaptive spherical domain nonlinear sampling on the seed samples, and reconstructing high-dimensional synthetic samples through inverse mapping; and finally, introducing manifold consistency and likelihood ratio dual probability constraints to perform quality verification on the samples, and triggering a radius feedback adjustment mechanism for unqualified samples until a balanced data set is constructed. And training the classifier by using the balanced data set to realize software defect prediction of an unknown sample. By selecting the high-quality seed samples and controlling the synthetic samples, the class imbalance problem is further processed from the data level, and the overall prediction performance of the model is remarkably improved.

The invention discloses a method and a system for predicting left atrial thrombus of an atrial fibrillation patient based on an unbalanced data set. The method comprises the following steps: collecting feature values of key features of the atrial fibrillation patient; inputting the feature value of the key feature into the trained left atrial thrombus prediction model, and outputting a left atrial thrombus prediction result; the training of the left atrial thrombus prediction model and the screening of the key features are as follows: balancing a left atrial thrombus class imbalance data set by using a double-layer processing framework, and processing the left atrial thrombus class imbalance data set based on atrial fibrillation conditions and self-service sampling to construct a training data set of a transfer learning strategy; constructing a left atrium thrombus prediction model, wherein the model comprises a data level prediction model and an algorithm level prediction model; and training a data level prediction model by using the balanced left atrial thrombus-like unbalanced data set, training an algorithm level prediction model by using the training data set, and screening common key features of the trained data level prediction model and algorithm level prediction model according to a feature importance function.

The invention relates to a data analysis method and device based on an auditing platform, equipment and a storage medium, and the method comprises the steps: determining a current data level where a data analysis control is located and a target data level where candidate data associated with the data analysis control is located in response to a click operation for the data analysis control in a data analysis page of the auditing platform; based on the current data hierarchy and the target data hierarchy, screening out a target associated field matched with the current data hierarchy and the target data hierarchy from a preset hierarchy association rule; according to page display content of the data analysis page, filling the target associated field to obtain a target associated field value; and querying candidate data associated with the data analysis control from a database according to the target data hierarchy and the target associated field value, and outputting abnormal target data in the candidate data according to a business rule corresponding to the data analysis control. The tedious operation of querying the business systems one by one is avoided, and the data analysis efficiency is improved.

The application discloses a broken surface attribution determination method and device, equipment and a storage medium, and relates to the technical field of geographic information science, comprising: obtaining a to-be-processed broken surface and a plurality of peripheral polygons within a first preset radius of the to-be-processed broken surface from the polygon data set of a target area; establishing an association relationship list based on the to-be-processed broken surface and each peripheral polygon; obtaining a comprehensive score of each peripheral polygon according to the association relationship list, and determining a candidate merging object based on the comprehensive score; verifying the candidate merging object based on the ownership information and data level of the to-be-processed broken surface, and obtaining a verification result; when the verification result is verified, it is determined that the to-be-processed broken surface belongs to the candidate merging object, so that the broken surface merging of the target area is performed according to the to-be-processed broken surface and the candidate merging object. The problem of insufficient attribution determination accuracy caused by single determination decision dimension and lack of compliance constraints in the prior art is solved.

The application provides a data storage processing method, device, storage medium and program product. The method comprises: obtaining a set of data sets to be processed; performing feature quantization processing according to data set values, operation times and detailed operation contents of the data sets to obtain statistical information tuples of the data sets; according to a preset score calculation rule, respectively according to access time quantization values, access frequency quantization values, storage capacity quantization values and correlation quantization values in the statistical information tuples, scores corresponding to the quantization values of the data sets are calculated; according to the scores corresponding to the quantization values of the data sets, a processing score of the data sets is calculated; according to a preset cold-warm-hot data division rule and the processing score of the data sets, a storage processing identifier is added to the data sets; and according to the storage processing identifier, the data sets are stored. The accuracy of data level division and the storage resource utilization efficiency are improved.

The application discloses a kind of wind turbine virtual sensing fusion methods based on machine learning, comprising the following steps: selecting the subsystem of wind turbine as research object, and obtaining historical operation data;The historical operation data is preprocessed;Correlation analysis is carried out to the data after preprocessing, and high-correlation data is selected;According to the selected high-correlation data, a sensor information correlation model is established based on machine learning;The fitting effect of sensor information correlation model is judged;Virtual sensor information reconstruction model is established, sensor is detected and fault alarm is given;The virtual sensor information reconstruction model is verified and hung machine test, and the wind turbine virtual sensing fusion is completed;The application mines the correlation of sensors between different measuring points and different sensors of the same measuring point by the method of machine learning, establishes a neural network model, constructs a virtual sensor to increase the redundancy on the data level, and effectively improves the reliability of the operating equipment.

This invention discloses a method, apparatus, and electronic equipment for processing aerodynamic load data corresponding to tasks, belonging to the field of aerospace measurement technology. The task processing method establishes a unified data profile model for aerodynamic load data at the data level through three-layer decoupling of physical parameters, operating conditions, and tasks. At the algorithm level, it achieves standardized management of multi-source algorithms and flexible adaptation across parameters and operating conditions through a plug-in collaborative engine and algorithm template mechanism. At the task scheduling level, it achieves collaborative optimization between acquisition and computation through real-time requirement hierarchies and task profile paradigms. Compared with existing technologies, this invention can maintain the consistency and physical rationality of the analysis process when facing different combinations of physical parameters and complex operating conditions, and can explicitly manage multi-level task requirements under limited computing power, thereby improving the overall reliability and engineering application value of flexible intelligent skin aerodynamic load measurement and digital twin systems.

This application discloses a hierarchical display system for power plant operation data, belonging to the field of power plant data management technology. The system includes: a data processing module configured to acquire power plant operation data from a power plant data source and perform quality assessment processing on the power plant operation data to generate quality labels characterizing the reliability of the power plant operation data; a hierarchical aggregation module configured to perform hierarchical aggregation processing on the power plant operation data based on the quality labels to form aggregated data with different display levels; and a display control module configured to determine the target data level of aggregated data from the aggregated data for loading and rendering according to the quality labels and current display requirements; different loading and rendering strategies are adopted for different display levels. This application is applicable to large-scale power plant operation data scenarios and can solve the problems of data loading delay and limited display performance.

A user load group typical power consumption mode extraction method, system, device and medium, the method comprises: acquiring meteorological and power consumption data of a selected area; extracting dimension reduction features of load data in time domain and frequency domain at data level; at mechanism level, obtaining the correlation between different meteorological factors and load by factor analysis, combining redundant meteorological information, then summarizing the statistical characteristics of load from four aspects of load size characteristics, load characteristic time point, load demand response potential and industry characteristics, and calculating the importance score of meteorological characteristics and statistical characteristics, selecting the characteristics according to the importance score to obtain key mechanism characteristics; clustering the dimension reduction features of load data in time domain and frequency domain again to obtain a primary classification result; then based on the key mechanism characteristics, adopting hierarchical clustering algorithm to perform hierarchical clustering on the primary classification result to obtain the user load group typical power consumption mode. The present application can better reveal the power consumption demand of users.