Application of Multi Information Data Fusion in College Public Sports Basketball Teaching
Notice bibliographique
Résumé
Due to the deepening reform of quality education, the requirements for physical education teaching in colleges and universities have become increasingly strict.In this era of rapid renewal and development of multimedia information technology, in order to make the traditional sports basketball teaching keep up with the pace of the trend and to search for the future development direction of college public sports basketball teaching, this paper studied the application of multi information data fusion technology in college public sports basketball teaching.The remote sensing technology and global positioning system in the multi information data fusion technology were used to conduct real-time detection and statistics on the sports effects of students in basketball teaching, and the relevant experimental scheme was designed.The data results recorded by manual recording and multi information data fusion technology were compared.The experimental results showed that when three student representatives and remote sensing technology simultaneously counted the times of passing and touching, the success rate of passing and the scoring rate of throwing for four sports members, the accuracy of remote sensing technology was higher; the Global Positioning System (GPS) system could effectively record the running distance, average speed and heart rate of 4 athletes.The average speed of No. 3 athlete was 9.1 m/s; the passing rate and shooting rate were both 50%, and the average speed of No. 4 athlete was 7.85 m/s.The pass success rate was 50%, and the shooting rate was only 33.3%.These data were conducive to teachers' timely understanding of students' personal conditions and basketball level, which could improve the efficiency of college sports basketball teaching and also increase the quality of students' sports.At the same time, the questionnaire survey method was also used to study the results of the introduction of multi information data fusion technology.The findings shown that multi-information data fusion technology might increase students' passion for learning basketball courses, hence improving the quality of sports, by altering their interest and attitude.In order to provide guidance for the future development of college public sports basketball instruction, this study offered a reference value for the application of multi-information data fusion.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».