21 results about "Identifying Variable" patented technology

The systems and methods described herein can identify meaningful relationships between variables, such as particular investments or general asset classes. Unlike conventional correlation analysis, these systems and methods provide an improved technique of co-movement analysis that implements a threshold to eliminate data “noise” and then discretizes the remaining observations to normalize any outliers. Such co-movement analysis has numerous advantages over known techniques for characterizing relationships between variables.

The invention discloses an actuarial calculation engine code compiler, an operation method and a related product. The actuarial calculation engine code compiler comprises an editing layer, an analysis layer and a mapping layer. The editing layer is configured to compile an actuarial process about the target actuarial business by adopting actuarial business terms and actuarial logic in response to the operation of the client; the analysis layer is configured to analyze the actuarial process and identify variables, formulas and time sequences in the actuarial process; generating a main syntax tree comprising target actuarial business semantics and a sub syntax tree comprising operation symbols and functions; and a mapping layer configured to parse the main syntax tree and the sub-syntax tree into machine executable code. In the embodiment of the invention, the actuarial process is edited through the actuarial business terms and the actuarial logic, and the actuarial process is converted into the machine executable code, so that the actuarial process adapts to high professionality and high suitability of professional evaluation of actuarial professionals.

A computer-implemented method for allocating registers for multi-core programs. The method includes generating core-specific programs for execution units of cores, wherein each program contains core-specific configurations. The method includes analyzing the core-specific programs to identify core-specific differences. The method includes creating a uniformized program that consolidates semantics of all programs for the execution units while retaining operations to account for core-specific differences. The method includes performing live range analysis of the uniformized program to identify active intervals for variables and creating segmented live ranges to partition the intervals into global and core-specific segments. The method includes allocating registers to the execution units using the segmented live ranges and multi-casting the uniformized program to the execution units of the cores.

In an illustrative embodiment, systems and methods are provided for combining disparate data sets gathered from a variety of external resources to produce safety metrics related to healthcare facilities, correlating data elements derived from the data sets to identify variables that impact safety incident risk in a medical facility environment, and normalizing patient outcomes with underlying population wellness data to allow for benchmarking across facilities and / or geographic regions.

This application relates to the field of wireless communication technology, providing an antenna robustness design method and apparatus based on hybrid deep learning. This invention simulates manufacturing process errors by combining sampling methods within the range of process errors, obtaining design variables and... S Sensitivity analysis was performed on the Gaussian distribution curves of the statistical mapping relationship between parameter responses to identify variables more sensitive to manufacturing errors, thereby reducing the dimensionality of variables and decreasing the complexity of subsequent antenna robustness optimization. A hybrid deep learning model was used to construct an antenna response substitution model, replacing traditional electromagnetic simulation, significantly shortening the optimization cycle while ensuring the accuracy of response prediction and avoiding distortion of optimization results due to model errors. By constructing a robustness objective function and employing a genetic algorithm to optimize antenna parameter robustness, a highly robust optimal solution was found, avoiding premature convergence of the gradient method in multi-peaked environments caused by random errors.

Aspects of the present disclosure relate to condition tree optimization techniques. In examples, a condition tree is comprised of rules and associated logical operators. The condition tree is used to process a set of variables, thereby generating an evaluation result. In some instances, multiple evaluations are performed, for example to identify variables that affect the evaluation result of the condition tree. As compared to dynamic variables that may change across various iterations, certain variables may be static. Subparts of the condition tree associated with such static variables may be identified, such that they may be evaluated and replaced with a resulting processing result, thereby generating an “optimized” condition tree. Thus, the optimized condition tree may comprise dynamic subparts and processing results in place of static subparts. The condition tree may be processed to identify variables that negatively affect an evaluation result, which may be identified as candidate variables for change.

The application relates to a causal DAG discovery method for an online service system based on fusion of soft priori. The method comprises the following steps: obtaining observation data and text meta-knowledge of the service system, preprocessing to form a standardized sample set, identifying variable types and semantics and outputting; generating a natural language description according to the variable semantics, querying a large language model for an ordered variable pair, analyzing to obtain three types of causal probability vectors, and calibrating to obtain an edge-level priori probability. An appropriate conditional independence test method is selected, high-confidence independent / dependent sentences are divided, and weights are assigned. A candidate directed acyclic graph is selected as an initial structure, parameters are estimated by linear regression, and data fitting scores are calculated, language priori scores, conditional independence penalty terms and counterfactual self-consistency penalty terms are calculated. Fusion is carried out into a hybrid score function, discrete optimization is carried out under the constraint of a directed acyclic graph, and a causal graph structure with the optimal score is output. The method can improve the efficiency and accuracy of a smart operation and maintenance system.

A method includes obtaining (802) observation information related to an artificial intelligence / machine learning (AI / ML) model (116) to be trained and identifying (804) multiple variables associated with the observation information. The method also includes analyzing (806) at least a portion of the observation information associated with the identified variables to determine whether the identified variables are redundant and determining (808) that two or more of the identified variables are redundant with one another based on the analysis. The method further includes obtaining (814) a set of training data (114) for training the AI / ML model, where the set of training data includes observations over a range of values for at least one of the two or more variables determined to be redundant and lacks observations over a range of values for at least one other of the two or more variables determined to be redundant.

This reduces the workload involved in generating form definitions and prevents a decline in quality. [Solution] The processing unit 12 obtains first report layout sheet information 11a, which includes fixed items and variable items, and report item definition information 11b, which defines the positions in which the variable items are written. Based on the positions, the processing unit 12 identifies the variable items from the first report layout sheet information 11a. The processing unit 12 then deletes the identified variable items from the first report layout sheet information 11a to generate second report layout sheet information 15, which retains the fixed items. Furthermore, the processing unit 12 generates a report definition 16 using the second report layout sheet information 15 and the report item definition information 11b, and outputs the report definition 16.

The invention discloses electronic equipment, an executable file generation method and a computer readable storage medium. The electronic device includes: a processor; the processor is configured to: obtain a first computational graph; determining an input tensor and an input mark tensor corresponding to a first operator in the first calculation graph; the input mark tensor is used for identifying a variable region and / or a constant region in the input tensor; based on the input tensor, the input mark tensor and the operator type of the first operator, determining an output mark tensor corresponding to the first operator; the output mark tensor is used for identifying a variable region and / or a constant region in an output tensor after the input tensor is calculated by the first operator; carrying out operator folding on the first calculation graph based on the output mark tensor to obtain a second calculation graph; and generating an executable file based on the second computational graph. The method and the device are beneficial to improving the running efficiency of the executable file.

Historical HTTP transactions are analyzed to determine whether dynamic content can be cached. The analysis yields statistics and key templates of APIs represented in the HTTP transactions. To generate the key templates and statistics, API requests are organized by common request method, domain and path and then by common response content. The common request method, domain and path are used as an API fingerprint. Each set of API requests resulting from the organizing is analyzed to identify variable components and common components among the API requests in the set. The variable components in each set of API requests are incorporated into a key template and statistics are determined through analysis of the API requests. If the statistics satisfy a dynamic content caching criterion, then a cache key is created based on the key template and provided along with the common response content for updating a cache of an edge server.

This invention relates to the field of industrial process control technology, and in particular to an intelligent analysis method and system for industrial chemical processes. By integrating various types of data and unifying the time base, a high-precision process image that more closely resembles actual working conditions is constructed, enabling the system to more accurately reflect the current operating status. Simultaneously, it can identify causal relationships between variables and their intensity changes in real time, thereby providing early warnings of potential risks. Based on this, the process state is combined with causal information, providing a more comprehensive basis for decision-making. This achieves synergistic optimization among multiple objectives such as product yield, energy consumption, and safety margin, effectively improving production efficiency and economic benefits while reducing safety risks. All optimization strategies are verified in a virtual environment before being implemented, further ensuring the reliability and stability of operation, truly realizing efficient closed-loop management from data perception to intelligent control.

The systems and methods described herein can identify meaningful relationships between variables, such as particular investments or general asset classes. Unlike conventional correlation analysis, these systems and methods provide an improved technique of co-movement analysis that implements a threshold to eliminate data “noise” and then discretizes the remaining observations to normalize any outliers. Such co-movement analysis has numerous advantages over known techniques for characterizing relationships between variables.

Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can, identify a plurality of data variables within a multivariate event dataset. Embodiments of the present invention can then formalize a causal inference between at least two identified data variables within the multivariate event dataset and generate a structural framework of an average effect value for the multivariate event dataset based on the formalization of the causal inference of the identified data variables. Embodiments of the present invention can then calculate an inverse propensity score for the generated structural framework of the average effect based on a type of identified variable, a predetermined time associated with the identified variable, and a causal connection strength between the identified variables.

The application provides a kind of based on intelligent operation and maintenance's causal analysis method, device, equipment and medium, receive the original telemetry data of each cloud platform, the directed causal dependence relationship between variables is identified based on the original telemetry data, generate the graph structure information of local causal structure, and the graph structure information is differentially private protection processing, to output the local causal structure information after adding noise;Based on the aggregation weight of each cloud platform, the local causal structure information is aggregated, and the consensus causal graph is obtained, and the causal strength parameter is determined based on the consensus causal graph;When detecting system exception, based on the consensus causal graph and the causal strength parameter, the root cause score of each entity in consensus causal graph is calculated, the main root cause entity is identified based on the root cause score of each entity ordering, and a causal analysis report is generated according to the main root cause entity. Through the method and device, the accuracy of abnormal attribution in intelligent operation and maintenance is significantly improved.

The invention discloses a normalized stream missing data restoration method based on causal guidance, and the method comprises the following steps: data preprocessing and initial restoration: carrying out the standardization and coding of original missing data, and obtaining initial complete data for structure learning through a pre-restoration function; causal structure learning: utilizing a micro-optimizable causal discovery algorithm NOTEARS to automatically identify a directed dependency structure between variables from the pre-restored data, and providing causal constraints for subsequent generation modeling so as to ensure that the data restoration process accords with a real data generation mechanism; and performing normalized flow repair modeling and submerged space optimization based on a causal structure: embedding causal dependence in a flow model and introducing causal consistency constraint in a submerged space to realize high-precision data repair which has reversibility and conforms to a causal law.

A method includes obtaining observation information related to an artificial intelligence / machine learning (AI / ML) model to be trained and identifying multiple variables associated with the observation information. The method also includes analyzing at least a portion of the observation information associated with the identified variables to determine whether the identified variables are redundant and determining that two or more of the identified variables are redundant with one another based on the analysis. The method further includes obtaining a set of training data for training the AI / ML model, where the set of training data includes observations over a range of values for at least one of the two or more variables determined to be redundant and lacks observations over a range of values for at least one other of the two or more variables determined to be redundant.

The invention relates to a method for identifying variables relevant to a dataset, said variables being derived from a plurality of variables involved in processing the dataset, said method comprising:a step of generating a subset of variables from the plurality of variables,a step of assigning a quantization value to each variable of the generated subset of variables,a step of selecting a relevant variable,a further step of generating a new subset of variables when the quantitative value of the selected variable is below a predetermined threshold value.

This makes it easier to identify variables that influence the error or goodness-of-fit indicator between the predicted value from the predictive model and the correct value. [Solution] The information processing device includes a processing unit. The processing unit receives multiple first time series data for each of multiple input variables in a first period and constructs a prediction model that predicts one or more output variables at times after the first period. The processing unit calculates third time series data of an index that represents the error or goodness of fit between one or more second time series data representing one or more output variables predicted by the prediction model for each of the one or more time points included in the first period, and ground truth time series data representing the correct answers for one or more output variables at one or more time points included in the first period. The processing unit uses the multiple first time series data and the third time series data to construct a time series causal graph that represents the causal relationship between multiple input variables and the index.

This invention relates to the field of remote sensing data processing technology, and in particular to a satellite remote sensing big data processing and analysis method and system based on artificial intelligence. The method involves acquiring and preprocessing satellite big data to obtain a set of candidate variables, constructing a dynamic causal inference model to identify directed causal relationships between variables and generating a multidimensional causal association map. Based on this map, a counterfactual reasoning framework is constructed to simulate the causal effects of different band schemes on target identification accuracy. A decision support scheme is generated by combining resource constraints, and the model is corrected online using the deviation between the actual execution effect and the expected causal effect, thereby improving the accuracy and adaptability of remote sensing data analysis.

The invention discloses a building material price dynamic prediction system based on multi-dimensional data fusion, and relates to the technical field of data processing and price prediction, and the system comprises a data integration module which is in butt joint with a multi-industry data interface, collects multi-dimensional data, verifies and summarizes the multi-dimensional data, a data preprocessing module reads original data, outputs a standardized sequence through abnormal value processing and the like, and stores the standardized sequence. The causal mining module adopts a time sequence algorithm to identify a variable relationship and construct a causal graph, the coefficient construction module calculates and optimizes an initial coefficient to form a conduction matrix, and the prediction module collects data in real time, calculates a predicted price and outputs the predicted price; according to the method, building and associated industry data are integrated, after the quality is improved through preprocessing, a variable relation is identified through a time sequence causal algorithm, and a causal graph is constructed; a causal conduction coefficient matrix is constructed through an optimization technology, a dynamic prediction algorithm and adaptive and correction factors are combined, accurate dynamic prediction of building material prices is realized, related subjects are assisted to pre-judge trends, configuration is optimized, and risks are reduced.