9 results about "Validation error" patented technology

The present invention relates to the field of large model application technology, and discloses a code error repair method and system based on a large model. The method includes: deploying several large models dedicated to coding and initializing model scoring; continuously monitoring the error log of the target program, obtaining the current error information, and pulling the error code file corresponding to the current error information in the code repository; constructing a code repair prompt word, sending it to the large model with the highest model score, and guiding it to repair the error code; deploying the updated code file in a test environment, and using a request matching the error information to request replay of the updated code to verify whether the error is successfully repaired; if so, updating the target program according to the updated code file, and positively adjusting the model score of the currently used large model; if not, excluding the large model that failed to be repaired. The present invention realizes the automation of the entire process from error monitoring, code acquisition, error repair to deployment verification, reducing manual intervention.

Techniques are described for quickly rerouting traffic to an application hosted on a first Virtual Private Cloud (VPC) location. In the event of an error in routing traffic to the first VPC portion traffic can be rerouted to a second VPC portion. The first and second VPC portions can be different portions of the same VPC or could be different VPSs. The techniques include steps for calculating a predetermined route to the second private virtual cloud location. The techniques further include steps for monitoring data for detecting an error in the first cloud location. The steps further include detecting a potential error based on the monitored data, and also verifying that the potential error is in fact a valid error. In response to verifying that the error is a valid error, further steps include performing a fast reroute of traffic to the second cloud locations along the predetermined route.

The application provides a large-span steel structure closed loop analysis method and system, the analysis method comprises the following steps: constructing a finite element model and a design parameter space, identifying key design parameters and key regions affecting the first N modal strain energy of the structure through initial sensitivity and modal analysis, and establishing a mapping relationship between the two; according to the parameter sensitivity contribution, a non-uniform sampling strategy is formulated, the sampling is encrypted in the high sensitivity parameter subspace, an initial sample set is generated, based on the sample and the finite element response, a hierarchical mixed kernel Kriging surrogate model containing a global long-range kernel and a multi-region local short-range kernel is constructed, the local kernel weight increases as the input point approaches the high sensitivity region, the iteration termination and the performance limit state are set, the composite point adding criterion integrating the prediction variance, the limit state distance and the cross-validation error is used to iteratively update the sample and the model, and after the condition is met, the structure performance analysis is completed by using the surrogate model.

The invention relates to the technical field of data processing, in particular to an accelerometer vibration rectification error test system and method, and the system comprises a self-adaptive signal collection module, a bidirectional cooperative transmission module, a composite modal statistics module and a cross validation error modeling module. The adaptive signal acquisition module generates a feature mark, and the feature mark drives the bidirectional cooperative transmission module to adjust the depth of a buffer area and injects a timestamp verification identifier; a composite modal statistics module selects a wavelet packet time-frequency fusion algorithm based on a timestamp missing proportion, and aligns a physical cycle segmentation window to extract a time-frequency entropy mean statistical magnitude; a cross validation error modeling module fuses statistics and vibration parameters to generate a quantitative model, and a confidence evaluation factor distribution diagram triggers double-path feedback. Invisible window optimization improves data quality in a key period, and frequency domain weight redistribution optimizes a feature extraction strategy. The problems of insufficient dynamic data real-time processing efficiency and transient feature extraction misalignment caused by data acquisition and analysis function separation in the prior art are solved.

Digit validation check control for instruction execution. A process receives an instruction to perform one or more operations using one or more input values. The instruction includes a "No Validation" indicator to check whether the digit validation check control is enabled for instruction execution. The process executes the instruction based on the "No Validation" indicator, including determining whether the digit validation check control is enabled for instruction execution and performing the processing based on this determination.Based on the "No Validation" indicator, which is set to a defined value, the "Digit Validation Check" control is activated, and the processing includes forcing a "Digit Validation Error" indicator, which is output by the execution to indicate "No Digit Validation Error" with respect to the at least one input value.

This invention provides a deep learning-based method for predicting the deformation of foundation pit support piles, belonging to the field of foundation pit engineering safety monitoring technology. The invention establishes a monitoring network to continuously collect multi-dimensional feature data and horizontal deformation values ​​of support piles, recording timestamps; sets the time window length and prediction step size, generates samples by sliding along the time axis, and divides them into training and test sets; constructs an AM-TKAN initial model, determines the type and initial range of hyperparameters to be optimized, and generates several hyperparameter combinations as individuals; trains the model corresponding to each individual on the training set and calculates the error, iteratively updates the hyperparameters according to the Hippo optimization algorithm, and outputs candidate optimal hyperparameter combinations that meet the error requirements; uses a validation set to select the model with the smallest validation error as the final prediction model; inputs real-time data into the final prediction model and outputs the deformation prediction results. This invention achieves high-precision prediction of the horizontal deformation of foundation pit support piles.

This invention discloses an intelligent early warning method, device, and storage medium for offshore wind turbine foundation scour, belonging to the field of structural safety early warning technology. The method includes the following steps: obtaining the foundation structural parameters of the offshore wind turbine and establishing a finite element model; performing foundation scour simulation and collecting time-domain data of pile top displacement and pile nodal stress; establishing several surrogate models, dividing the time-domain data into training and validation sets, and obtaining training and validation results; using two validation indices, validation error and leave-one-out error, to select the best surrogate model; using different scour conditions as input parameters to obtain the wind turbine foundation response, comparing it with a set safety early warning threshold, and issuing a safety alarm if the wind turbine foundation response reaches or exceeds the safety early warning threshold. This invention can solve the problems of traditional experimental methods, such as the difficulty in reproducing initial conditions, the influence of experimental scale on measurement results, and high costs, as well as the shortcomings of finite element simulation in achieving full coverage of scour conditions.

The application relates to the technical field of data processing, in particular to an accelerometer vibration rectification error test system and method, which comprises an adaptive signal acquisition module, a bidirectional collaborative transmission module, a composite modal statistical module and a cross-validation error modeling module. The adaptive signal acquisition module generates a characteristic mark, the characteristic mark drives the bidirectional collaborative transmission module to adjust the buffer depth and inject a timestamp verification mark; the composite modal statistical module selects a wavelet packet time-frequency fusion algorithm based on a timestamp missing ratio, extracts time-frequency entropy mean statistics by aligning a physical cycle division window; the cross-validation error modeling module fuses the statistics and vibration parameters to generate a quantitative model, and a confidence evaluation factor distribution diagram triggers a double-path feedback: a stealth window optimizes and improves the data quality in a key period, and a frequency domain weight re-distribution optimizes a feature extraction strategy. The application solves the problems of insufficient dynamic data real-time processing efficiency and inaccurate transient feature extraction caused by the fragmentation of data acquisition and analysis functions in the prior art.

A system for effort estimation in agile software development using multi-stage evolutionary stacking, consisting of: a data acquisition module configured to retrieve software effort records from one or more data set repositories containing historical data from software development projects with characteristics and actual effort values; a data preprocessing module that is operationally connected to the data acquisition module and is configured to receive the aforementioned software effort data sets from the data acquisition module, cleans the received data by removing inconsistencies with missing target values, and normalizes numerical input characteristics to a common range; a first-level ensemble module connected to the data preprocessing module, wherein the first-level ensemble module comprises a variety of heterogeneous basic learners, including a Random Forest model, a Support Vector Regression model, and an Extreme Gradient Boosting model, which generate predictions from each of the heterogeneous basic learners using the preprocessed data sets received from the data preprocessing module; A genetic algorithm optimization module connected to the first layer's ensemble module, configured to: encode weights as a normalized real-valued vector for each of the heterogeneous base learners; apply a fitness function to minimize the mean squared validation error and derive an optimal weight vector; assign optimized weights to the predictions of each of the heterogeneous base learners; and generate weighted predictions based on the optimized weights. a second-level meta-learning module connected to the optimization module of the genetic algorithm, configured to receive the weighted predictions from the optimization module of the genetic algorithm, processes the weighted predictions using a deep multilayer perceptron neural network to learn complex patterns and nonlinear interactions, and generates a final effort estimate for the software; an output processing module connected to the second-level meta-learning module, configured to receive the final effort estimate for the software and process and visualize the data to improve user understanding; and a user interface connected to the output processing module to receive the processed final effort estimate for the software, wherein the user interface is configured to display the processed and visualized final effort estimate for the software.