Many companies still rely heavily on manual data entry for various aspects of their operations, which is time-consuming, labor-intensive, and prone to errors. For instance, in warehouse management, tasks such as purchasing, returns, shipping, and inventory are often handled manually. This leads to a heavy workload due to the need to fill out numerous and complex forms, resulting in inefficiency and a high likelihood of mistakes.
To address this issue, many companies have attempted to introduce computerized management systems. However, after implementation, only part of the problem was resolved. While software support can automate some tasks, certain manual processes—such as entering data without automated support—still remain unresolved.
Even when some manual tasks are automated, the bottleneck caused by re-entering large volumes of printed form data at subsequent computer workstations persists. Using devices like the PT923 or LK934 scanner, companies can configure an efficient workflow to track the status of each item in every order in real-time. Bar code scanning with these devices allows for quick registration and modification of item information.
Moreover, item data can be directly uploaded to the computing center via a MODEM. Once the collector device is used, all data recorded at each stage becomes automatically registered, eliminating the need for redundant data entry.
Server performance data can also be collected using Python scripts combined with Linux commands. The script determines whether the data collection is complete based on the number of active TCP connections during testing.
The main functions of the script include three key operations: preliminary data collection, extraction of performance indicators, and packaging. First, the script uses Linux commands like `sar` and `iostat` to collect raw performance data. After collection, another script extracts relevant data from the original file and writes it into a final file, then packages the results.
A configuration file is used to extract data from the original file, depending on the server language type. There are two versions: `abstractConf_ch.xml` for Chinese and `abstractConf_en.xml` for English. These files specify the original file path and use Linux commands like `cat`, `egrep`, and `awk` to extract the required data.
An example of the XML configuration includes:
```xml
res/CPU
CPU
result/cpu_status
Cpu_Status
%user %system
Time(s) Cpu_Percent(%)
cat %s | egrep -v "Linux|^$|%s" | awk 'BEGIN {print "%s%s%s"}{if($2 !~/AM|PM/) print $3,$5 }'
```
To get the number of service connections, a Python class named `GetLinkingNumber` is used. It executes Linux commands like `netstat` to count the number of active TCP connections. The script supports both string and dictionary inputs for server specifications.
In addition, a shell script (`collect.sh`) is used to execute several Linux commands such as `sar`, `iostat`, and others to gather system performance data.
The main data acquisition script initializes the process, calls the shell script, and manages the termination of subprocesses. It also records the number of TCP connections and logs the progress.
Another script is responsible for extracting valid data from raw files and writing it to new files. It reads configuration files based on the system's language settings and uses appropriate commands to parse and format the data.
Finally, the extracted data is packaged into a tarball for easy distribution and analysis. The script also includes error handling and logging to ensure reliability.
Overall, while the current approach meets the basic needs, future improvements could involve using more advanced Python libraries like `psutil` to make the data collection process more efficient and aligned with modern development practices.
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