10 results about "Identifying Variable" patented technology

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.

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 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.

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.

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.

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 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 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.