According to the customer case of Kuanzi's official Jushubao, this e-commerce data platform under Yida Technology uses AI to read fault logs, combines it with knowledge base for preliminary positioning, and integrates occasional retries, manual support tickets, and repeated faults into the operation and maintenance process. The page also introduces the observation and tuning after going online, which is a self-report of the actual application. This article has not independently verified the efficiency changes.
Merchants report errors separately, and you can find a common entrance for troubleshooting.
The following scenario is an illustrative structure of ChinaOPC and is not a real support ticket for Jushubao. Assume that multiple merchants find that the data of a certain platform is not updated at the same time. The text of the support ticket may read "Few orders", "The board has not changed" and "Task failed" respectively. AI can help organize expressions, but whether it belongs to the same fault requires comparison of platforms, processing steps, time and log basis.
Conversely, several support tickets all say "data anomaly", which may also involve different platforms and different links. Putting them together simply because they are similar in wording can obscure independent issues. The small team can first show the correlations suggested by the system to the investigators, and list the basis and areas that are still uncertain to help them decide where to start investigating first.
When communicating with customers, you can also distinguish between observed phenomena and causes that are being investigated. For example, being able to confirm that a certain piece of data has not been updated does not mean that the reason for the platform revision has been found. Historical similar faults provided by the system are suitable for narrowing down the scope of investigation, and the cause conclusion needs to be combined with current operating evidence.
After the merger, the influence of each merchant must still be retained.
The same upstream problem may cause different merchants to lack data for different time periods. Under a common troubleshooting item, you can continue to associate the respective affected tasks, data periods, and communication records. This way, while engineers deal with common causes, customer service staff can still explain which parts are waiting to be restored.
The original case introduces process retry for occasional problems. When looking into this type of automation, also look at where the retries continue and what previous steps have already accomplished. Assuming that the collection has been successful and subsequent writing failed, re-running the entire task may result in duplicate records; if the data has not been obtained before, the processing method is different. This article does not infer that it uses a specific retry algorithm based on the original page.
You can separately explain the situations that require continued observation, are suitable for retrying, and should be handed over to engineers, so that automated suggestions can be mapped to the current task status. When there is insufficient information, there is practical value in retaining manual troubleshooting. In order to reduce the number of support tickets, you cannot mark ununderstood problems as sporadic failures.
After the common cause is repaired, confirm the affected tasks one by one.
After the technical repair is completed, you can continue to check whether each associated task has been re-run, whether the missing data has been completed, and whether the corresponding dashboard has been updated. If a certain test task returns to normal, it can only prove the situation of that operation; whether other customers have recovered, they should still go back to their respective records to check.
When counting the results, you can observe the number of common faults and the number of affected tasks at the same time. After the support tickets are merged, the number of troubleshooting items seen by engineers has been reduced, but the number of affected customers may not have been reduced. Keeping these two levels of records helps customers understand the progress of the process, and also helps the team discover whether similar problems recur.
A small team that provides AI automation services to merchants can first select a frequently occurring collection failure, sort out a small number of logs that are allowed to be used and remove irrelevant information, and discuss with the operation and maintenance personnel whether the correlation is reasonable. The starting goal can be to make the order of investigation clearer and explain the impact of each customer clearly; we cannot promise the same revenue based on the publicity ratio of official cases, nor identify Yida Technology as OPC based on customer cases.
Extended reading: [How website data changes form useful reminders] () and [How to prepare regression samples after AI code changes] (), respectively understand external change clues and specific inspections after repair. Related reading: How website data changes create useful reminders; How to prepare regression examples after AI code changes.