Design and Application of Experimental Data Management System Integrating Remote Monitoring and Historical Data Analysis
Bibliographic record
Abstract
In order to solve the problems of scattered data storage, difficult management and low data utilization in ship impact and explosion test, a test data management system integrating remote monitoring and historical data analysis is designed and implemented in this paper. The system adopts the hybrid architecture mode of B/S (browser/server) and C/S (client/server) to give full play to the advantages of the two architectures. VUE, ExtJS, Java, Python and other advanced technical frameworks and programming languages are applied in the system development process to ensure the high efficiency and flexibility of the system. The core function modules of the system include test task scheduling, data storage and management, data analysis, resource allocation, knowledge management and system maintenance. Through these modules, the system not only realizes the systematic management of the test data, but also supports the flexible expansion of the analysis algorithm to adapt to the ever-changing test requirements. The test results show that the system effectively solves the decentralized problem of data storage and management, and significantly improves the standardization and utilization efficiency of data management. The integration of remote monitoring function makes it possible to collect and process real-time data, and at the same time, to conduct in-depth analysis with historical data, which greatly improves the comprehensive application value of data. The implementation of this system has promoted the improvement of the management level of ship impact and explosion test data, and provided strong support for the research and application in related fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".