| Literature DB >> 35069799 |
Yang Sun1, Changjun Hu2.
Abstract
This article is aimed at studying the design and implementation of a football player training management system based on smart images. Based on the analysis of the importance of informatization for scientific football training, system performance requirements and intelligent image detection technology, the football player training management is designed. The overall architecture of the system, and the detailed design of each functional module of the system. It mainly includes football player information management module, football player training plan viewing module, training goal formulation module and training information feedback module. The realization of the training management system relies on intelligent image technology to detect and track athletes. Finally, the performance of the system was tested. The test results show that the expected response time of each module of the system when different numbers of users are accessed is within 3 seconds. The longest actual time is 2.64 s, and the actual shortest time is 1.18 s. It can be seen that the response time of the system meets the demand. At the same time, the system throughput rate meets the requirements of this article, and the user pass rate is also above 95%, indicating that the performance of the football player training management system designed in this article is better.Entities:
Year: 2022 PMID: 35069799 PMCID: PMC8767373 DOI: 10.1155/2022/6091557
Source DB: PubMed Journal: Appl Bionics Biomech ISSN: 1176-2322 Impact factor: 1.781
Figure 1The overall architecture of the system.
Server hardware environment configuration table.
| Hardware environment | Parameter |
|---|---|
| Operating system | Windows Server 20112 R2 Datacenter Edition 64-bit Chinese version |
| CPU | Single core |
| RAM | 2 GB |
| System disk | 50 GB |
| Data disk | 30 GB |
| Public network bandwidth | 1 Mbps |
Performance test case.
| Serial number | Test object | System access user | Test pass rate | Estimated response time | Actual response time |
|---|---|---|---|---|---|
| 1 | Information management function | 100 | 99% | 3 | 1.43 |
| 2 | 300 | 100% | 3 | 2.24 | |
| 3 | 500 | 97.7% | 3 | 2.45 | |
| 4 | Plan view function | 100 | 99.8% | 3 | 1.36 |
| 5 | 300 | 100% | 3 | 1.79 | |
| 6 | 500 | 98.3% | 3 | 2.17 | |
| 7 | Training goal setting function | 100 | 99.4% | 3 | 1.18 |
| 8 | 300 | 96.2% | 3 | 2.28 | |
| 9 | 500 | 98.3% | 3 | 2.64 |
Figure 2Test pass rate.
Figure 3Response time.
Design of test cases with large data volume.
| Serial number | Test requirements | Enter the maximum amount of data | User pass rate | Throughput expectation | Can it be achieved |
|---|---|---|---|---|---|
| 1 | Information management function | 1500 | 99.2% | >100000 | Satisfy |
| 2 | 3000 | 99.4% | >300000 | Satisfy | |
| 3 | 5000 | 99.1% | >500000 | Satisfy | |
| 4 | Plan view function | 1500 | 100% | >100000 | Satisfy |
| 5 | 3000 | 99.3% | >300000 | Satisfy | |
| 6 | 5000 | 98.7% | >500000 | Satisfy | |
| 7 | Training goal setting function | 1500 | 100% | >100000 | Satisfy |
| 8 | 3000 | 99.2% | >300000 | Satisfy | |
| 9 | 5000 | 95.8% | >500000 | Satisfy |
Figure 4User pass rate.