Research on a Shared Bicycle Deposit Management System Based on Blockchain Technology
Bibliographic record
Abstract
As a green travel mode, bike sharing is developing rapidly across China. At present, charging deposits from users is the common operation mode adopted by shared bicycle enterprises. The large number of shared bicycle enterprises generates fierce market competition, and the eliminated enterprises always refuse to return user deposits. Even regular running enterprises still have trouble with the immediate return of deposits. This situation severely affects the reputation of shared bicycle enterprises, and concerns have been shared widely across the society. Meanwhile, there is a general expectation among users that their deposits could be refunded timely and a broad appeal for technical management to resolve this problem. This article uses blockchain technology to reform the current management mode for shared bicycle deposits and constructs a decentralized, user information and deposit visualized, and multidimensional supervised management system. The proposed management system makes the real-time flow direction supervision of user deposits to be realized. Furthermore, a smart contract of shared bicycle deposits with punishment mechanism is also designed. Finally, the differences between the proposed deposit management mode and the current deposit management mode are analyzed, and a simulation experiment is conducted. In the simulation experiment, the deposit theft rate of our deposit management system is 0%, which is far better than the two existing bike deposit management systems. The results show that the outstanding advantages of the proposed deposit management mode, which include improving deposit supervision and guaranteeing user deposit security, are also conducted. This article has made effective technical management exploration to reduce deposit management risks and improve deposit management institutions for shared bicycles. It has important practical reference value for accelerating the sustainable development of shared bicycle enterprises.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".