96 results about "Multiple indicator" patented technology

The invention belongs to the field of power system engineering, and discloses a power distribution automation terminal diagnosis method and system based on multi-source wave recording feature fusion, and the method comprises the steps: obtaining the electric quantity data and equipment operation state data collected by a power distribution automation terminal; performing adaptive decomposition on the electrical quantity data by using a variational mode decomposition algorithm to obtain an intrinsic mode function; constructing a deep residual network model, carrying out fusion analysis on the time-frequency domain features of the intrinsic mode function, and generating a fault feature vector; establishing a multi-dimensional evaluation matrix based on the fault feature vectors, and integrating a plurality of indexes to output fault types and credibility scores; according to the fault type and the credibility score, generating a fault isolation strategy based on a Petri network model; and executing a dynamically adjusted self-adaptive self-healing control algorithm. According to the method, complex and changeable fault modes can be effectively identified, a complete collaborative verification mechanism is formed, seamless connection from fault diagnosis to self-healing control is realized, and the operation reliability of the power distribution network is remarkably improved.

The invention discloses a transformer substation defect identification method and system based on multi-mode open set associative reasoning. The method comprises the steps that transformer substation oily equipment defect images are collected and marked; constructing a multi-modal data set of image-text matching; yOLOv8 is adopted to extract visual features, and a CLIP model is adopted to process text features; designing a re-parameterized visual language path aggregation network for feature fusion; training the model based on a contrast loss function; the applications are deployed after the performance is evaluated through multiple indexes; through the multi-modal feature fusion and the re-parameterization path aggregation network, the problem that the oil leakage defect is difficult to mark due to liquid flowability and form variability is effectively solved, the false detection condition under multi-device cross interference is remarkably improved, and by combining multi-scale feature learning and an attention mechanism, the oil leakage detection accuracy is improved. And the identification accuracy of the oil stain defect is greatly improved.

The invention discloses an intelligent data management system, and relates to the field of intelligent data management. The system comprises a data acquisition module for acquiring and preprocessing multi-source data and extracting field information; the decoupling analysis module is used for calculating a mutual information index and an information cementation degree according to the field information; the map generation module is used for constructing an attribute fusion map according to the field information and generating risk mapping skewness and a risk control map; the behavior analysis module is used for collecting action execution logs, extracting field behavior sequences and calculating field behavior interference factors; and the quality evaluation module is used for extracting a key field missing proportion and information entropy based on the field information to calculate an information completeness index, and fusing multiple indexes to generate a comprehensive quality score. Through graph structure construction, behavior sequence analysis and quality scoring fusion, the multi-source data field fusion accuracy and risk identification precision are improved, and intelligent assessment and compression abstract generation of high-quality data are realized.

The invention requests to protect an emotion analysis method based on a text-guided comparative learning and gating fusion mechanism, which is used for solving the problems of insufficient modal feature representation discrimination and insufficient modal information fusion in multi-modal emotion analysis. Firstly, features of three modes are extracted in a feature extraction module, then a text mode is selected as a dominant mode, a text-guided weighted comparison learning strategy is designed, the strategy screens positive and negative sample pairs through the text mode, and weights are dynamically distributed for hard pairs and easy pairs, so that the model keeps universal features while focusing on the hard pairs, and the model is optimized. Therefore, the discrimination capability of modal feature representation is improved. Secondly, in a feature fusion module, a text-guided gating fusion mechanism is provided, weights of text modals and cross-modal fusion features are adaptively adjusted through a gating unit, and a self-attention mechanism is used to suppress fusion noise, so that the modals are fused more fully. Experiments on public data sets CMU-MOSI and CMU-MOSEI show that compared with a reference method, the method provided by the invention has advanced performance on multiple indexes such as F1, ACC-2 and the like.

According to the xerophthalmia core index multi-task AI detection system, method and terminal provided by the invention, the xerophthalmia multi-modal image is collected and exclusive preprocessing is completed, and core index detection is carried out on the preprocessed image based on the xerophthalmia multi-index detection model, so that the xerophthalmia core index detection result is obtained. Outputting detection results such as meibomian gland deletion rate, tear film rupture time, staining classification, tear river height and blepharitis marginal inflammation classification; and dynamically adjusting the index result according to a calibration coefficient corresponding to the type of the data acquisition equipment, and finally generating a standardized detection report. A xerophthalmia multi-index detection model is constructed based on an improved U-Net3 + architecture, a dry eye feature enhancement encoder and a cross-scale feature fusion decoder are adopted, a customized multi-task detection head is matched, an inter-task attention collaboration mechanism is established, meanwhile, a preprocessing algorithm and a dynamic calibration mechanism matched with a dry eye image are designed, and the detection accuracy of the xerophthalmia is improved. And multi-index synchronous, high-precision and high-efficiency detection is effectively realized.

The invention relates to a safety monitoring method for an irrigation and drainage project operation state and an intelligent system, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting and marking irrigation and drainage project operation monitoring data through a sensor, and constructing a training data set; completing data normalization by combining quantile and median robust scaling with adaptive nonlinear transformation, and mining and screening high-order interaction features by combining a mutual information theory and a gradient boosting decision tree; constructing a deep classification network fusing physical prior and adaptive feature interaction, introducing physical constraint and multi-scale feature fusion, and optimizing a model through adaptive marginal classification loss and physical feature manifold alignment loss; real-time data is preprocessed and then input into the model, and operation state grade classification and graded alarm are achieved. According to the method, data noise can be inhibited, a multi-index coupling relationship can be mined, the interpretability and robustness of the model can be improved by integrating a physical rule, irrigation and drainage project abnormity can be accurately identified and early warned, and the method is suitable for intelligent safety monitoring of an irrigation area.

The invention relates to a road material multi-index comprehensive performance evaluation method, and belongs to the technical field of data processing. The method comprises the following steps: constructing a road material multi-index data set and carrying out data labeling; calculating an attribute correlation weight and a noise robustness factor, and dynamically adjusting a normalization scale to obtain normalized road material multi-index data; a feature enhancement mapping module is constructed, a local structure of normalized data is extracted through a radial basis kernel function, an attribute interaction item is adopted to enhance the nonlinear expression ability, high-order nonlinear features are extracted, and final feature enhancement mapping output is obtained; constructing a road material multi-index comprehensive performance evaluation model; training the model by using the final feature enhancement mapping output to obtain a trained model; and S3, processing a new road material data sample in the steps S2 and S3, and inputting the processed new road material data sample into the trained model to obtain a performance grade evaluation result. According to the invention, the accuracy of road material multi-index comprehensive performance evaluation can be improved.

The invention discloses an intensive care state real-time prediction method and system based on mask dynamic composition. The method directly faces the challenge that clinical time sequence data is extremely sparse, and a monitoring missing mode is creatively converted into valuable prediction features. A monitoring mode dynamic relation graph is constructed to encode the clinical cooperative monitoring intensity between the indexes; comprehensively modeling time sequence dependence in combination with a time sequence attention mechanism and multi-level local feature extraction; and finally, through adaptive multi-channel fusion, accurate and end-to-end prediction of the future multi-index clinical trajectory of the patient is realized. Compared with a traditional filling or special architecture method, the method has the advantages that knowledge hidden in clinical monitoring behaviors can be more effectively mined, more excellent prediction performance can be achieved on an intensive care state data set with extremely sparse data, and a new technical path is provided for development of an intelligent clinical early warning system.

The invention discloses a news event risk indicator association relationship prediction method based on an equilibrium manifold graph, and belongs to the technical field of risk assessment. The method comprises the steps of collecting an original text of a news event, labeling risk indexes, and constructing a training set, a verification set and a test set; constructing a graph neural network based on a balanced manifold graph; using the samples in the training set to train the balanced manifold network, verifying through the verification set, and obtaining the risk index prediction accuracy on the test set; and collecting a news event text to be analyzed, and predicting the event risk index by using the trained model to obtain one or more indexes with the highest association degree. According to the method, the incidence relation between the event and the waiting index can be fully mined, and the precision is improved.

The present disclosure provides a flow detection method, device, electronic device and storage medium, which relate to the fields of computer technology such as the Internet and big data. The specific implementation scheme is: obtaining the business flow data to be detected, the corresponding detection dimensions and statistical indicators; determining multiple dimension values ​​of the detection dimensions and determining multiple index values ​​of the statistical indicators; based on the multiple dimension values ​​and the multiple index values, aggregating the business flow data to obtain the flow distribution data of each dimension value under the multiple index values; determining the correlation between the flow distribution data of each dimension value under the multiple index values, and determining the target business flow data in the business flow data based on the correlation. In this way, it is achieved that the target business flow data with abnormalities can be detected from the business flow data to be detected, and it has strong versatility, low complexity and high detection efficiency.

This invention discloses a method and system for implementing role-playing dimensions, relating to the field of data processing technology. It includes: creating a meta-model of dimension models related to role-playing dimensions, the meta-model comprising five types of data tables; selecting role-playing dimension names from their sources according to requirements; when a role-playing dimension name differs from a physical dimension table name, the dimension corresponding to the role-playing dimension name is considered a role-playing dimension; when displaying the related dimensions of an indicator, using the role-playing dimension name to replace the physical dimension name to demonstrate the actual business meaning of the dimension; when summarizing the indicator using selected dimensions, constructing dynamic SQL using the role-playing dimension name; when analyzing composite indicators, if the facts of the multi-atomic indicators constituting the composite indicator are all associated with the same physical dimension, then the composite indicator is summarized using that physical dimension; when performing parallel analysis of multiple indicators, if the facts of each indicator involved in the analysis are all associated with the same physical dimension, then the multiple indicators are summarized using the physical dimension; and displaying the list of related dimensions using the physical dimension name to avoid confusion caused by different role names.

The present invention relates to the field of artificial intelligence technology, and provides an artificial intelligence-based intelligent recommendation method, device, electronic device and medium, the method comprising: collecting a first data set corresponding to the previous cycle of the current cycle from multiple data sources at preset intervals and preprocessing the data to obtain a second data set; cutting the second data set to obtain a target data set; inputting each sub-target data set in the target data set into a plurality of pre-trained indicator calculation models to obtain multiple indicator values ​​for each customer; and determining a target recommendation scheme based on the multiple indicator values ​​of each customer and the product portraits of all types of recommended products. The present invention calculates multiple indicator values ​​for each customer through multiple indicator calculation models, and when determining the target recommendation scheme, takes into account the multiple indicators of each customer, thereby improving the accuracy of the target recommendation scheme.

The invention discloses an index data processing method and device, a storage medium and electronic equipment. Relates to the field of data processing. The method comprises the steps that an original data set is acquired, and the original data set comprises at least one object, at least one index and at least one time interval; screening the original data set to obtain a target data set, and converting the target data set into a matrix form to obtain a target data matrix; and performing process performance baseline analysis and process performance model analysis on the target data matrix to obtain a target analysis result. According to the method and the device, the technical problem of poor analysis effect of comprehensive analysis of a plurality of object groups, a plurality of indexes and a plurality of time intervals in related technologies is solved.

The invention relates to a fund multi-dimensional quantitative ranking method and system, and the method comprises the steps: collecting the real-time position data of a target fund, and dividing the fund types based on all ranking framework dimensions; calculating each quantitative index value of the target fund in each time window, and obtaining the similar ranking, ranking level and similar mean value of the target fund according to the quantitative index values; obtaining structured input data of the target fund based on the structured cue word, and submitting the structured input data to the large language model to generate a natural language report, the natural language report including fund analysis, risk cue and adaptation suggestion; and repeating the steps based on each ranking framework dimension, generating a natural language report of the same target fund under different fund categories, and constructing a multi-dimensional ranking system of the target fund. A fund multi-dimensional quantitative ranking system is constructed by covering a plurality of classification dimensions, a plurality of time periods and a plurality of indexes and giving appropriate comments to each index data.

The invention relates to the technical field of physiological index data processing, in particular to a user feedback driven physiological index adaptive health assessment method and device. The method comprises the following steps: acquiring a BCG signal of a user based on a non-contact sensor, and acquiring index monitoring data of a plurality of indexes based on the BCG signal; the user inputs a user feedback text representing the abnormal state of the body, matching is conducted on the basis of the user feedback text and the disease words in the lexicon, and if the disease words are matched, indexes corresponding to the disease words serve as final monitoring indexes; if the disease word cannot be matched, screening out an initial monitoring index based on the user feedback text, and determining a final monitoring index based on the initial monitoring index; and obtaining daily index monitoring data based on the final monitoring index. In this way, mapping from coarse-grained subjective perception to refined objective indexes can be realized, and monitoring blind areas and misjudgment risks are remarkably reduced while the early-stage anomaly detection rate is increased.

The embodiment of the invention provides an index map processing method and a related device, and the method comprises the steps: obtaining a triggering sequence and first coordinate data of a first index node; determining a second index node associated with the first index node according to the trigger sequence, and determining a connection position of the first index node and the second index node according to the trigger sequence and the first coordinate data; the first index node and the second index node are connected according to the connection position to obtain an index map, and the index map corresponds to a plurality of index data tables; and based on the first coordinate data and the second coordinate data of the connection position, performing updating processing on the index data table corresponding to the first coordinate data and the index data table corresponding to the second coordinate data. By adopting the embodiment of the invention, the data volume for storing the index atlas can be reduced.

The present invention discloses a remarketing advertising effect prediction method and system based on multi-task learning, which belongs to the field of online advertising marketing information. The present invention learns and predicts multiple user behaviors (clicks / browsing / add-to-cart / purchase) after the advertisement is displayed, considers multiple influencing factors at the same time in the display link, automatically learns the correlation between features and targets, and solves the remarketing advertising effect prediction problem with one model. Specifically, knowledge is learned from massive data through offline feature design, target determination, sample construction and model training, and feature extraction and model scoring are completed for requests online in real time. The knowledge trained from the offline model is applied to the production environment to realize real-time remarketing advertising effect prediction. The present invention uses one model to realize the prediction of multiple indicators, which reduces the complexity of traditional multi-model solutions and overcomes the problem of poor training effect caused by sparse data of purchase / add-to-cart and other behaviors.

Systems and methods are provided for assessing cyber attack preparedness associated with organizations. One example computer-implemented method includes accessing data indicative of multiple controls of an organization, where the organization includes an information network, which includes the controls and where the controls are associated with securing one or more information assets. The data is indicative of the controls including multiple indicators. The method also includes aggregating one or more ratings for at least one of the indicators to a criteria, aggregating the aggregate rating for the criteria to a category, and aggregating the aggregate rating for the category to one of the controls as a control maturity score for said one of the controls. The method then includes displaying the control maturity score for said one of the controls to a first user associated with the organization.

This invention relates to the field of psychological assessment technology, specifically a VR-based mental health assessment method, comprising the following steps: analyzing event types, interaction states, and priorities based on virtual scene event nodes; selecting suitable nodes; adjusting the acquisition window; judging changes in EEG amplitude; identifying segmented time points; combining extreme values ​​and response intervals; matching high-risk benchmarks; and classifying psychological risk indicators. In this invention, user interaction behavior and virtual situation events are dynamically mapped to achieve closed-loop management of the entire data flow. The acquisition window is adjusted in conjunction with multi-source parameters, signal segments are defined in real-time based on actual EEG fluctuations and scene cognitive states, EEG electrode values ​​and behavioral response timing are extracted synchronously, multi-dimensional features are coupled and analyzed, and the dynamic grouping of psychological risks is based on the joint setting of multiple indicator intervals. Data is guaranteed to be timely in high-frequency interaction scenarios, and the assessment results can reflect the continuous evolution of the user's psychological state in complex environments.

When searching based on the similarity of multiple indicators, it is possible to check the similarity of each indicator. [Solution] The drawing search system includes a search unit that searches for similar drawing information, which is existing drawing information similar to drawing information, from among multiple existing drawing information registered in an existing drawing database based on a similarity calculated from a detailed similarity, which is the similarity between drawing information and similar drawing information for each of multiple indicators, and a similarity display unit that displays the detailed similarity used by the search unit to search for similar drawing information together with the similar drawing information.

The invention belongs to the technical field of bridge structure health monitoring, and particularly relates to a multi-span long-connected continuous beam bridge lightweight safety monitoring and state evaluation method which comprises the following steps: S1, acquiring displacement, prestress and temperature data of a bridge in real time through a sensor network, and cleaning and normalizing the data; s2, constructing a decision tree based on the cleaned data, and dynamically adjusting hyper-parameters of Monte Carlo tree search; s3, searching and inverting the flexural rigidity and equivalent prestress of the bridge through a collective Monte Carlo tree, and screening an optimal solution; and S4, fusing the multi-level indexes based on a fuzzy evaluation method, and outputting a bridge safety level. According to the bridge structure health monitoring system, multiple technical innovations are integrated, especially the collective Monte Carlo tree search and the fuzzy evaluation method are combined, the technical problems of a traditional structure health monitoring system in the aspects of real-time performance, accuracy, long-term performance and the like are solved, and a more accurate and intelligent solution is provided for safety evaluation and long-term maintenance of a bridge structure.

The invention provides a coke quality fuzzy comprehensive evaluation method and system based on multiple indexes, and the method comprises the steps: defining coke quality indexes through employing a fuzzy mathematical theory, and representing the comprehensive influence of the fluctuation of each index of the coke quality on the production of a blast furnace under an existing production condition; and the function of reasonably and comprehensively evaluating the coke quality is realized. The method reflects the advantages and disadvantages of a certain index of the coke through the membership degree, is convenient for iron and steel enterprises to reasonably and comprehensively judge the coke quality, and provides support for stable and smooth operation of a blast furnace and reduction of coke ratio, fuel ratio and iron cost. The method solves the problem that the evaluation of the coke quality fluctuation is limited in a certain index range at the present stage, and accurate judgment is difficult to make when the coke indexes have advantages and disadvantages crossing, and also changes the phenomenon that the evaluation is uncertain and difficult to quantify depending on experience in the past.

This invention provides a parameter-driven, intuitionistic fuzzy logic method for evaluating the suitability of mega-span cavern sites, relating to the field of geotechnical engineering evaluation. This invention, for the first time, constructs a dedicated tunneling design index system for mega-span caverns integrating "rock mass quality, environmental response, shape effect, and support status." It separates evaluation criteria indicators from engineering response indicators, establishing a mapping relationship between the two through numerical simulation, making the evaluation process more consistent with engineering cognitive principles. Intuitionistic fuzzy numbers are used to quantify the merits of evaluation index values, achieving an organic fusion of objective data and fuzzy expression. The final output is a single, quantifiable site suitability index, enabling intuitive comparison of multiple sites and multiple indicators, greatly facilitating engineering decision-making. By replacing expert scoring with a pre-set mapping function, subjective interference is completely eliminated, ensuring that the evaluation results are entirely determined by the input parameters. Different personnel can obtain consistent conclusions, greatly improving the repeatability and engineering applicability of the method.

Systems and methods are provided for assessing cyber attack preparedness associated with organizations. One example computer-implemented method includes accessing data indicative of multiple controls of an organization, where the organization includes an information network, which includes the controls and where the controls are associated with securing one or more information assets. The data is indicative of the controls including multiple indicators. The method also includes aggregating one or more ratings for at least one of the indicators to a criteria, aggregating the aggregate rating for the criteria to a category, and aggregating the aggregate rating for the category to one of the controls as a control maturity score for said one of the controls. The method then includes displaying the control maturity score for said one of the controls to a first user associated with the organization.

The invention discloses a multi-index joint alarm method and device, a medium and equipment. A specific embodiment of the method comprises the following steps: constructing an index association graph comprising a plurality of nodes corresponding to a plurality of indexes and edges among the nodes, and determining the edges according to whether the connected nodes all have alarms in a predetermined historical time interval, the edge weight of the edge represents the joint information entropy determined according to the alarm frequency of the connected node in the historical time interval; determining a first threshold value according to the data distribution of the edge weights, and deleting the edges with the edge weights greater than the first threshold value to obtain a plurality of associated sub-graphs; obtaining an index abnormal degree value of each index in the current time window, and determining a node weight of the node according to the index abnormal degree value; and according to the node weight and the edge weight, determining the current sub-graph abnormal degree value of each associated sub-graph, and determining whether to send out an alarm corresponding to each associated sub-graph according to the current sub-graph abnormal degree value. Through the method, the labor cost of multi-index alarm can be reduced, the alarm accuracy and recall rate are improved, and the discovery time is shortened.

Systems and methods for generating personalized solution recommendations for a user problem. Such a method includes (i) receiving an error indication from a user associated with an account profile including user account data; (ii) retrieving, based on the error indication, an information resource of a plurality of information resources; (iii) inferring, based on the user account data, a problem associated with the information resource and the account profile; (iv) generating, using a trained machine learning model, an information resource summary for the information resource by using the information resource and the inferred problem as inputs to the trained machine learning model; (v) determining whether one or more metrics associated with the information resource summary meet one or more quality criteria; and (vi) training the trained machine learning model based on (a) whether the one or more metrics meet the one or more quality criteria and (b) the information resource summary.

The application discloses a database performance diagnosis method and device, and relates to the technical field of computers. A specific implementation manner of the method comprises the following steps: acquiring a plurality of historical monitoring data and diagnosis results corresponding to each piece of historical monitoring data; each piece of historical monitoring data comprises index data of a plurality of indexes; for each piece of historical monitoring data, constructing time sequence features of each index according to the index data of each index in the historical monitoring data and a preset time window; determining various monitoring features corresponding to the historical monitoring data according to the index data of the plurality of indexes and the time sequence features of each index; and training a database performance diagnosis model according to the various monitoring features corresponding to the historical monitoring data and the diagnosis results. The implementation manner evaluates the database performance through multiple indexes, and improves the accuracy and efficiency of database performance diagnosis.

This invention discloses a method, system, electronic device, and computer-readable storage medium for evaluating the sidetracking potential of different types of reservoirs in complex fault blocks. The method includes: determining the reservoir type; selecting evaluation indicators affecting the sidetracking potential of different reservoir types; establishing the weights of each evaluation indicator; determining the distribution of evaluation levels and the corresponding evaluation classification standards for each evaluation indicator in each evaluation level; establishing an unknown single-indicator measurement function; statistically analyzing the actual values ​​of each evaluation indicator for the target reservoir; determining the quantitative value of a single indicator; classifying the sidetracking potential levels based on the comprehensive evaluation of multiple indicators; and calculating the comprehensive score based on the comprehensive evaluation vector of multiple indicators and the assignment of evaluation space. This invention proposes the concept of evaluating sidetracking potential by reservoir type, accurately characterizing the size of the sidetracking potential of the target area, and compensating for the inaccurate potential evaluation when using the same parameters for reservoirs with different conditions; it also establishes an unknown single-indicator measurement function, realizing the hierarchical and quantitative analysis of geological parameters in the target area.

Visualize key performance indicators (KPIs) from various structural perspectives. [Solution] The Key Performance Indicator Tree Display Device 1 includes: a Key Performance Indicator Tree Database 11 having an organizational tree structure and a task tree structure; an indicator selection unit 12 that displays the organizational tree structure and the task tree structure of the Key Performance Indicator Tree Database 11 and accepts the selection of multiple indicators by the user from among the organization and task; an aggregation unit 13 that aggregates performance information related to the multiple indicators; a graph selection unit 14 that accepts the selection of the type of graph to show the performance information; and a graph visualization unit 15 that visualizes the performance information aggregated by the aggregation unit 13 in a graph of the type accepted by the graph selection unit 14.