Research on the Optimization Model of Digital Resource Allocation in Cultural and Tourism Industry Based on Reinforcement Learning
Bibliographic record
Abstract
This paper intends to introduce the multi-intelligence of digital resources in cultural and tourism industry in reinforcement learning.In order to scientifically evaluate digital resource allocation, the index system characterizing resource allocation is constructed using hierarchical analysis.From there, a multi-objective collaborative optimization allocation model of digital resources in cultural and tourism industry based on reinforcement learning and multi-intelligent body system is established.Through empirical analysis, it can be seen that referring to the observation of the development of the comprehensive level of digital resource allocation, there is an imbalance in the development level of N province.The indicator system is refined to consist of 4 guideline level indicators and 26 indicator level indicators.Before and after the multi-objective synergistic optimization, the total amount of digital resource procurement for the cultural and tourism industry in province N was reduced by 460,742 yuan.After optimization, the comprehensive efficiency of resource allocation in area a improves by 0.03136, area b improves by 0.03275, and area h improves by 0.02799.Moreover, all of them tend to be in equilibrium.Therefore, the multi-objective synergistic optimization allocation model in this paper can improve the efficiency of digital resources in cultural tourism industry and reduce the differences between districts and counties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".