We consulted on the optimal machine configuration for "a system that converts images from industrial cameras into MP4 in real time and records videos."
Since we are expecting long-term recording operations, we would like a configuration that is as durable as possible and can be supplied for a long period of time.
We also hear that there is a desire to keep the housing size as small as possible, and that a slim tower is preferable.
The requests we received are as follows.
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・CPU: Core i7 |
Based on these conditions, we proposed the following configuration.
| CPU | Intel Core i7-12700 (2.10GHz 8 cores + 1.60GHz 4 cores) |
| memory | 16GB |
| Storage 1 | 240GB SSD S-ATA high durability model |
| Storage 2 | 8TB M.2 SSD |
| Video | NVIDIA GeForce GTX1650 4GB |
| network | on board (2.5GbE x2) |
| Housing + power supply | Mini tower type housing + Nipron 500W |
| OS | Microsoft Windows 11 Professional 64bit |
The configuration includes parts that can be supplied for a long period of time in accordance with customer requests.
The supply of core components such as motherboards and CPUs is scheduled to continue until 2029.
The video card uses the specified NVIDIA Geforce GTX1650, but this product is a consumer product.If long-term supply is important, it is also possible to change to a workstation video card such as the NVIDIA RTX series, so please feel free to contact us.
In addition, this configuration is equipped with an Intel Core i7-12700 CPU, which generates a relatively high amount of heat under high load, and is also equipped with a full-height video card.For this reason, it is difficult to use a slim tower-type housing with a small volume.
In this case study, we are introducing a mini-tower type chassis that can accommodate a large CPU cooler and a full-height video card, taking into consideration heat exhaust and cooling inside the chassis. If you change the CPU/video card configuration, we can also accommodate a slim tower type chassis, so please contact us if you wish.
This configuration is intended to be used to detect product errors by having AI learn from captured images.As a related example, the learning configuration (Case No.PC-10311) and product judgment (Case No.PC-10540) We also have examples of machines, so please feel free to contact us.
The configuration of this case study is based on the conditions given by the customer.
We will flexibly propose machines according to your conditions, so please feel free to contact us even if you are considering different conditions than what is listed.
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