56 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 relates to the technical field of real-time verification of bending machines, in particular to a real-time drawing verification method for a machined part pattern of a bending machine numerical control system. The method comprises the following steps: obtaining an original pattern and identifying geometric element features, generating feature region data, carrying out load safety margin calculation to obtain a safety threshold boundary, disassembling a principal stress direction and constructing a continuous stress flow field, carrying out constraint reconstruction on the geometric element features, and identifying variable contour data. The method comprises the following steps: acquiring target bending data, planning a bending track, predicting a pre-machining shape, generating a real-time machining gap through gap comparison with the target data, detecting elastic springback of a bent part, performing compensation parameter mapping based on springback data, simulating secondary machining, and finally performing real-time comparison and dynamic machining parameter fine adjustment. The machining process is optimized, and the machining efficiency and the finished product quality of the numerical control system of the bending machine are integrally improved.

The embodiment of the invention discloses an intelligent production method and equipment of a composite insulation board, a medium, application and an intelligent production technology, which are used for solving the problem of unstable quality of an existing production mode. The method comprises the following steps: determining target product data of a to-be-detected composite insulation board according to workshop order information and historical detection data; detecting the defect connected graph and the boundary contour according to a preset camera and ultrasonic equipment, performing regional grouping on the defect graph in combination with the layer structure data, inputting a classification model to obtain a first detection result, and comparing the boundary contour to determine a second detection result. And positioning an optimizable production link based on the first detection result and the second detection result, performing simulation operation on an optimizable production environment by using a digital twin model, and identifying variable bottleneck parameters. And inputting the variable bottleneck parameters, the production link parameter range and historical production data into a least square model, calculating tuning data, and adjusting the composite insulation board according to the tuning data to realize closed-loop production.

The invention discloses a push-in information processing method and device. The method comprises the following steps: analyzing a target short message point-to-point protocol data packet sent by a calling number, and judging whether short message information in the target short message point-to-point protocol data packet is push-in information or not according to an obtained analysis result; when the short message information in the target short message point-to-point protocol data packet is push-in information, determining a target short message text corresponding to the push-in information in the target short message point-to-point protocol data packet according to the analysis result; analyzing the target short message text by using the abnormal short message classification model to obtain an abnormal probability of the target short message text; and determining a processing operation executed on the push-in information in the target short message point-to-point protocol data packet according to a size relationship between the abnormal probability and a preset threshold value. The technical problem that the false alarm rate is high due to the fact that variable abnormal push-in short messages cannot be accurately recognized by adopting a fixed rule is solved.

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.

A method for identifying at least one variable from a plurality of variables (A,B) that exhibits at least one dependency on another variable from the plurality of variables (A,B), comprising the following steps: Providing (S21) a dataset comprising sensor time-series measurements for the variables (A,B). Training (S23) a plurality of recurrent machine learning systems on the dataset. Determining (S24) statistics on the deviation of the predictions of the recurrent machine learning systems. Comparing (S25) the statistics of each pair of variables (A,B), whereby if the deviation of the prediction (A → B) of a second variable (B) depending on a first variable (A) is smaller than vice versa (B → A), it is identified that the first variable (A) has a causal dependency on B.

A method is for identifying at least one variable from a plurality of variables having a dependence on a predetermined variable from the plurality of variables. The method includes providing a data set comprising data points for a plurality of variables for a plurality of products respectively and selecting the predetermined variable from the plurality of variables. The data set is pre-processed by adding at least one random variable. A machine learning system is trained on the pre-processed data set and a determination of the dependencies of the variables on the predetermined variable based on the trained machine learning system. The dependencies learned by the trained machine learning system are made understandable and thus usable for root cause analysis using methods of “interpretable machine learning”, such as the extraction of variable importance or effects.

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.

The application relates to the field of artificial intelligence, and relates to a cause-effect relationship determination method, which comprises the following steps: acquiring first information indicating a cause-effect relationship between a plurality of variables predicted by a generative flow model, and predicting second information of the plurality of variables by the generative flow model according to the first information; wherein the second information indicates that a first cause-effect relationship exists between a first variable and a second variable in the plurality of variables, and the first information indicates that the first cause-effect relationship does not exist between the first variable and the second variable. The application identifies the cause-effect relationship between variables in a sequence generation mode, and the generative flow model can generate an optimal cause-effect relationship sequence through iterative updating, so that the optimal cause-effect sequence is selected from a plurality of cause-effect sequences without sampling a large number of cause-effect sequences, the calculation power consumption is reduced, and the convergence speed of the model is improved.

A computer implemented method for prediction of geomechanical performance including productivity index decline and completion integrity for a well or a hydrocarbon reservoir using a geomechanics informed machine intelligence (GIMI) algorithm. The method includes running a geomechanical reservoir simulator to generate training datasets for the hydrocarbon reservoir and incorporating physical models and identified variables into the GIMI algorithm. The method further includes training a neural network of the GIMI algorithm by using correlated training datasets that correlate to the physical models to produce a resulting prediction model and performing sensitivity analysis on the resulting prediction model. Additionally, the method includes identifying dominant variables for damage mechanisms through design of experiment statistics and performing history matching and blind test on the resulting prediction model. Lastly, the method includes updating the identified variables and models incorporated into the GIMI algorithm.

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.

To efficiently test programs containing loop parts.SOLUTION: A program verification support system 1 stores a first program 100 having a loop part that repeats predetermined processing, as well as initial values of variables in the first program 100 and variable values after execution of the first program 100, by inputting the initial values of the variables into the first program 100, identifies changed portions in the variable values immediately after completing execution of the loop part from the variable values immediately before execution of the loop part begins, generates stub data for the identified changed portions in the variable values, replaces a loop part in a second program 200 corresponding to the first program 100 with the changed portions indicated by the stub data, then executes the second program 200, and outputs data indicating comparison results between execution results of the second program 200 and the variable values after completing execution of the first program 100.SELECTED DRAWING: Figure 1

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.

The invention relates to a Java code variable naming normalization judgment method and system. The method comprises the following steps of obtaining a business term table and a file path list of a Java file based on obtained project basic information; sequentially scanning the Java files in the file path list, and identifying variable declaration statements in the Java files and code lines of the variable declaration statements; identifying a variable name corresponding to the statement based on the variable declaration statement; identifying annotation information corresponding to the variable declaration statement based on the code line of the variable declaration statement; and determining the normalization of the variable name based on the business term table, the variable name and the annotation information. According to the method, whether the variable naming in the Java code conforms to the basic naming specification or not can be judged, and whether the Java variable naming achieves the meaning of seeing the name or not can also be judged; the name of the code variable is checked in an automatic code scanning mode, and compared with a manual code walkthrough mode, the efficiency is higher.

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.

The invention discloses an income and cost influence factor analysis method based on a cloud model and pLASSO, and relates to the technical field of data analysis, and the method comprises the steps: building an industry product influence factor set based on a first object influence factor, and generating a preset evaluation grade; constructing a first object influence factor preliminary rating model based on a preset evaluation grade to obtain a first object influence factor preliminary rating; according to the method, a prediction value is obtained through preliminary rating and screening, a first object influence factor analysis model is constructed, and parameters are adjusted to obtain a first object influence factor with a coefficient value not equal to 0. According to the method, deep insights of experts on industry product income and cost key influence factors are integrated into the model; the model is helped to identify variables really related to the target variable more accurately; through comprehensive utilization of the cloud model and the pLASSO model, important factors influencing cost and income are screened out more accurately.

The invention discloses a method for acquiring a fast signal by an XCP of a CAN bus. The method comprises the following steps: constructing a high-speed sampling buffer area for storing a specific observation variable; setting a first calibration quantity used for identifying a variable address and a second calibration quantity used for identifying a variable type through the XCP; acquiring a sampling signal according to a high-speed sampling period, performing conversion processing on the acquired sampling signal according to the first calibration quantity and the second calibration quantity, generating a specific observation quantity, and putting the specific observation quantity into the high-speed sampling buffer area for storage; and when the high-speed sampling buffer area is full or not, acquiring a specific observed quantity from the high-voltage sampling buffer area according to a low-speed sampling period of the XCP, putting the acquired specific observed quantity into an ODT linked list of the XCP, and packaging and sending the specific observed quantity to a host through the XCP. According to the method, any observed quantity can be dynamically acquired at a high speed, and problems in practical application can be more effectively analyzed and solved.

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.

The application discloses a page display method and device and a storage medium, and belongs to the technical field of Internet. The method comprises the following steps: displaying a target page according to stored target page data, and sending a first request message to a server, wherein the first request message carries a first identifier and a second identifier, the first identifier is used for identifying variable data in the target page data, and the second identifier is used for identifying non-variable data in the target page data; receiving a first response message returned by the server when the first identifier is different from the identifier of variable data in the latest page data of the target page, and the second identifier is the same as the identifier of non-variable data in the latest page data, wherein the first response message carries the variable data in the latest page data; and refreshing the displayed target page according to the variable data in the latest page data. In the application, only the place with data change in the target page needs to be locally refreshed, so that the page viewing effect can be improved.

The invention provides a small celestial body center gravitational force on-orbit identification method based on multi-source measurement information. The method comprises the following steps: acquiring multi-source information in a free descending process of a detector towards a small celestial body; the multi-source information is position relation data of the detector relative to the small celestial body obtained by relative navigation based on image landmarks, relative navigation based on combination of center sight and distance measurement and relative navigation based on point cloud features; determining the position and speed of the detector relative to the small celestial body at the initial moment and the central gravity coefficient of the small celestial body as variables to be identified, and determining optimization indexes of the variables to be identified according to the variables to be identified, the priori values of the variables and the obtained multi-source information; and performing iterative correction on the to-be-identified variable to obtain a corresponding small celestial body center gravity coefficient when the optimization index of the to-be-identified variable is minimized. According to the method for autonomously identifying the gravitational force of the small celestial body on the satellite based on the multi-source measurement information, the on-orbit identification precision of the central gravitational force of the small celestial body is improved as much as possible by fully utilizing various effective relative navigation measurement information.

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.

The present application relates to the technical field of real-time verification of bending machines, and in particular to a method for real-time drawing and verification of workpiece patterns for bending machine numerical control systems. The method comprises the following steps: obtaining an original pattern and identifying geometric element features, generating feature area data, and calculating load safety margins to obtain safety threshold boundaries, decomposing principal stress directions and constructing a continuous stress flow field, constraining and reconstructing the geometric element features, identifying variable contour data, obtaining target bending data and planning bending trajectories, predicting pre-processing shapes, generating real-time processing gaps by comparing differences with target data, detecting the elastic rebound of the bent part, mapping compensation parameters based on rebound data and simulating secondary processing, and finally performing real-time comparison and dynamic processing parameter fine-tuning. The present application optimizes the processing process and improves the processing efficiency and product quality of the bending machine numerical control system as a whole.

The invention discloses an electric power data matching method, system and device based on STL and change point detection and a storage medium, and relates to the technical field of data processing, the method comprises the following steps: obtaining original electric power data and economic data to construct an initial data set, and carrying out multi-scale decomposition on the initial data set to obtain different types of components; identifying change points of the electric power data and the economic data to obtain decomposition components and change point conditions under different types of component decomposition; constructing a corresponding feature project, establishing a high-dimensional matrix, carrying out clustering analysis on the feature project at a space-time level, and extracting dynamic space-time association of the power data and the economic data; static space-time association of the power data and the economic data is extracted through variable-point space-time alignment; according to the method, the data relevance of the electric power data and the economic data in the aspects of time and space can be effectively analyzed, electric power and economic linkage is achieved, and reliable data support is provided for decision making, predictive analysis and policy planning.

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.

A system and method for developing unified digital platform based virtual power banks is provided. A second data type is derived by analyzing record types. The record types are obtained from the first data type received from multiple sources. Virtual power banks are generated by employing the first and second data types fetched from database. Dynamic actionable items relating to the virtual power banks are generated from the first data type and the second data type. One or more variables are identified that correspond to different types of dynamic actionable items for categorizing the dynamic actionable items based on the identified variables. Lastly, optimization operations are performed on values of each of the identified variables to obtain an optimized final weightage value of the virtual power banks, accessed via a unified digital platform, based on which one or more operational parameters associated with the virtual power banks are determined.

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.