144 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.

A system for assessing the ecological sensitivity and development potential of areas within ecologically sensitive mountain towns, consisting of: a data acquisition module configured to extract various data types, including multi-source geospatial data, environmental data, built environment data, socio-economic data and community participation data, with the extracted data being validated and stored in a database; a central processing unit comprising one or more processors connected to a memory, the processor communicatively connected to the database in which the extracted data is stored, the memory storing instructions that, when executed, configure the processor to implement the following: a data input module configured to receive data corresponding to multiple indicators from different assessment dimensions, including ecology and environment, built environment, economy, heritage and culture, mobility, location, community participation, visual aspects and safety from hazards; an indicator evaluation module configured to evaluate each indicator according to predefined sensitivity categories and assign corresponding sensitivity index values in the range of 0.1 to 1; a dimension weighting module configured to apply dimension-specific weights to calculate weighted scores for each indicator; a development potential calculation module that aggregates the weighted assessments to determine an overall sensitivity assessment and assigns this overall sensitivity assessment to a development potential rating scale; and a visualization module configured to generate visual representations and reports of the assessment results; and an output module having a user interface operatively connected to the visualization module of the central processing unit, the output module configuring the user interface to display the visualized results and the final index value obtained from the central processing unit.

The invention discloses a mountain torrent debris flow early warning method and system based on multi-source data comprehensive analysis. And the monitoring system carries out multi-source monitoring on rainfall, material source and mud level change characteristics of the ditch domain. The method is based on double-threshold rainfall intensity parameters, a quantitative analysis model of channel mud level and flow, and a blocking and collapsing mechanism of object source deformation. The threshold value of each element is determined according to the disaster history, a disaster outbreak probability formula is established, and key factor weights are screened through a random forest algorithm. And data preprocessing is carried out through cross validation and dynamic adjustment. And substituting monitoring data into the analysis model, and comprehensively judging the early warning level of the mountain torrent debris flow. And issuing an early warning signal of a corresponding level to the object in the threatened area through the early warning system. After operation, an incremental random forest method is adopted to update the model through online learning, data redisk is carried out, and model threshold correction and parameter inversion analysis are carried out. According to the pre-warning system, a multi-index combined action comprehensive monitoring and pre-warning evaluation system for quantitative analysis is established.

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 discloses a role playing dimension implementation method and system, and relates to the technical field of data processing. Comprising the steps that a meta-model of a dimension model related to a role playing dimension is created, the meta-model comprises five types of data tables, a role playing dimension name is selected from a role playing dimension name source according to requirements, when the role playing dimension name is different from a physical dimension table name, it is considered that the dimension corresponding to the role playing dimension name is the role playing dimension, and the role playing dimension is selected from a physical dimension table name; when the related dimension of one index is displayed, the role playing dimension name is used for replacing the physical dimension name to display the actual business meaning of the dimension, when the dimension is selected to summarize the indexes, the role playing dimension name is used for constructing a dynamic SQL, and when the composite index is analyzed, the actual business meaning of the dimension is displayed. When multiple indexes are analyzed in parallel, if the facts where the polyatomic indexes forming the composite indexes are located are all associated with the same physical dimension, the composite indexes are summarized through the physical dimension, and when the multiple indexes are analyzed in parallel, if the facts where all the indexes participating in analysis are located are all associated with the same physical dimension, the multiple indexes are summarized through the physical dimension; and the associated dimension list is displayed by using the physical dimension name, so that confusion caused by different role names is avoided.

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 the technical field of data processing, in particular to an ensemble learning-based fraud behavior identification method, which comprises the following steps of: collecting financial data and operation signals of an A stock company, and constructing a multi-source fraud prediction library after standardized cleaning; according to the method, K-means undersampling, AdaBoost weight adjustment and a GOSDT tree are fused, and a CUSBoost-GOSDT model is constructed in combination with multiple indexes; the training model receives multi-dimensional data, outputs a fraud probability and a risk level through weight and voting, and dynamically adjusts a result; and risk levels are divided according to probabilities, an analysis report is automatically generated, difference early warning is triggered, a threshold value is optimized, and a decision basis is provided. According to the method, a multi-dimensional fraud identification feature system is constructed, redundant features are removed through feature selection, and model generalization and detection precision are improved. The algorithm has both expansibility and applicability, the integrated learning algorithm integrates a weak learner into a strong model, and parameter tuning guarantees stable performance in a complex scene. The detection capability is improved through advantage complementation of classifiers, and intelligent tool support is provided for audit supervision.

The invention discloses an industrial automatic control system based on batch production, and relates to the technical field of industrial automatic control, a matrix construction module compares the importance of indexes in pairs to obtain a judgment matrix, the judgment matrix is subjected to normalization processing, and then the weight vector of each index is calculated; the strategy output module generates the grey correlation degree of each alternative control strategy according to the difference coefficient, then substitutes the grey correlation degree of each alternative control strategy and the weight vector of the index into a fusion algorithm so as to calculate the comprehensive score of each alternative control strategy, sorts all the alternative control strategies according to the comprehensive score, and finally outputs the sorted alternative control strategies. And selecting the alternative control strategy with the first sequence to be applied to the current production scene of the production line. The control system can carry out multi-target comprehensive evaluation under an uncertain condition, so that the model can adapt to different production scenes, comprehensive optimization of multiple indexes is realized, and an optimal control strategy is selected for use.

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 relates to the field of medical health, and discloses a neonatal disease screening and health management integrated platform, which comprises a screening management layer used for adopting a push algorithm based on collaborative filtering and deep learning user interest modeling fusion, learning a user behavior sequence by using an LSTM model, capturing user interest characteristics, and performing health management on the user behavior sequence; pushing the propaganda and education data; the collection interpretation layer is used for collecting multi-source data of neonatal disease screening and health management and transmitting and storing the multi-source data; and the dynamic quality control layer is used for preprocessing the multi-source data, constructing a fusion model based on an attention mechanism, and evaluating the performance of the fusion model through multiple indexes. Newborn multi-source data is collected from multiple terminals and preprocessed after encrypted transmission and storage, a fusion model based on an attention mechanism is constructed, potential association among multiple indexes is mined, the AI interpretation technology of multi-index association analysis is applied, disease risks are evaluated and diagnosed in combination with a disease knowledge base, and the accuracy is improved.

The invention discloses a government affair data query method and device, electronic equipment and a storage medium, and is used for solving the problems of relatively high cost, inaccurate query result and relatively slow response of a related government affair data query technology. The method comprises the steps of obtaining a question statement input by a user; performing intention recognition on the question statement based on a pre-trained large language model to obtain a recognition result; under the condition that it is determined that data of government affair indexes need to be queried in a government affair database according to the recognition result, matching corresponding index aperture vectors in a preset government affair index vector library based on the question statements, and determining query index vectors corresponding to the index aperture vectors; wherein the query index vector and a plurality of index aperture vectors corresponding to the query index vector are stored in a government affair index vector library in an associated manner; and through a large language model, according to the query index vector and a preset prompt word project, generating an SQL statement.

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 belongs to the technical field of tunnel construction, and relates to a rapid multi-index comprehensive evaluation method for the rockburst grade of a specific section of a tunnel, and the method comprises the steps: building a rockburst grade division index system, collecting rockburst cases in real time on site, reasonably distributing the weight of each index through the data collected through multiple indexes, and then carrying out the comprehensive evaluation of the rockburst grade of the specific section of the tunnel. Based on critical values of indexes of different rockburst grades, relative evaluation is carried out on each single rockburst case by adopting an approximate ideal solution sorting method, and finally, comprehensive evaluation is carried out on the rockburst grade of the specific section of the tunnel by combining the average combination weight of a plurality of rockburst cases and adopting cosine similarity. The method has the advantages that compared with a traditional single-index judgment mode, the rockburst grade can be evaluated more comprehensively, and the rockburst grade of the construction stage can be divided more quickly in a multi-index mode.

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 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 a dynamic evaluation system which comprises a database and a deep learning model. The database is used for collecting multiple indexes of a user in real time, the deep learning model performs prediction based on the multiple indexes, and the real-time recommended dosage of sodium bicarbonate used by the user is predicted; the database comprises an acquisition baseline index, an information index and an end index; the deep learning model comprises a preprocessing unit, an XGBOOST calculation unit, an SHAP analysis unit, a regression prediction sub-model and an output unit. Based on the indexes of various physiological parameters of the user, especially the important function of the chloride ion level in metabolic acidosis analysis, accurate prediction is performed in the deep learning model, so that the real-time recommended dosage of sodium bicarbonate is accurately output, and the curative effect is effectively improved.

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 discloses an enterprise safety management maturity evaluation method and system and related equipment. The method comprises the following steps: constructing a multi-dimensional quantitative evaluation model of enterprise safety management maturity; the method comprises the following steps: collecting multi-dimensional index data of enterprise safety management, wherein the multi-dimensional index data comprises strategic index data, system index data, cultural index data and performance index data; carrying out weight-based comprehensive index score operation on the multi-dimensional index data through a multi-dimensional quantitative evaluation model to obtain a maturity evaluation score; and obtaining the level grade of the enterprise safety management maturity based on the numerical value interval of the maturity evaluation score in the level grade division table. According to the evaluation method, starting from multiple index dimensions such as enterprise strategy, system, culture and performance, comprehensive scoring is performed based on weights by using the constructed multi-dimensional quantitative evaluation model, the output evaluation result is more representative, and a reference basis can be better provided for subsequent improvement of enterprises.

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.

The invention relates to a self-adaptive calculation method for parameter thresholds in an activated carbon production process. In order to solve the problem of false alarm / missing alarm caused by a traditional fixed threshold value, process parameter data are collected in real time, an initial threshold value range is set by combining expert experience after data screening, and internal and external parameters of the threshold value are weighted and reserved respectively. Historical sub-samples are randomly extracted by adopting an isolated tree algorithm to construct a plurality of isolated trees, the path length of data points is calculated, and an abnormal score is obtained based on a normalization factor and a harmonic number. And dynamically tracking real-time abnormal score distribution by using a sliding window, adaptively adjusting an abnormal threshold in combination with a historical standard deviation, and automatically optimizing the number of isolated trees through an abnormal proportion. According to the method, the nonlinear relation among parameters is captured through multi-dimensional segmentation, the calculation precision and performance are dynamically balanced, the method can adapt to raw material difference and process fluctuation, the false alarm rate is remarkably reduced, manual intervention is reduced, and the real-time performance and accuracy of temperature, pressure and other multi-index anomaly detection in the activated carbon production process are improved.

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.