Aurora AI by 極光有序有限公司
Before matching drawing numbers normalize formats and expose exceptions
DWG filenames, title blocks, and order columns often format the same identifier differently. Learn how to define normalization, separate exact and rules-based matches, and route uncertain items for review.
Key takeaways
- Identify the authoritative drawing-number source and revision rule
- Separate exact matches from normalized matches
- Send uncertain records to an explicit unmatched queue
One part can appear as several strings
A DWG filename may include an extension or revision suffix, its title block may use periods or hyphens, and the order spreadsheet may introduce whitespace or leading zeros through cell formatting. Values that look equivalent to a person may not be equivalent to software.
Start by identifying the authoritative field and reviewing representative, de-identified samples. Increasing fuzzy matching without this step only hides a data-quality problem.
Normalization rules must be conservative and explainable
Common rules remove a fixed file extension, normalize letter case, trim whitespace, or standardize separators that have been confirmed to carry no meaning. Revision codes, engineering-change codes, and leading zeros may be meaningful and must not be removed by default.
Every rule needs positive and negative examples reviewed by people who understand the drawing and order data. When a rule changes, the team should also know which prior matches may be affected.
Separate exact, normalized, and unmatched results
At minimum, show records whose raw values match exactly, records that match after an approved rule, and records that remain uncertain. This directs reviewer attention to genuine decisions instead of forcing a full manual recheck of successful records.
Aurora's engineering-document workflow uses drawing numbers as the basis for relating DWG and order data. In a deployment, normalization rules and unmatched presentation are confirmed against the organization's conventions and samples.
Unmatched is a clear work queue, not a hidden failure
A system should not guess an unsupported pair merely to raise its success rate. An unmatched queue should retain the raw value, source location, and likely reason so an engineering or order-data owner can add information, correct formatting, or confirm an exception.
Acceptance should not measure match rate alone. It should also test whether false matches are avoided, exceptions are quick to locate, and corrected inputs can be processed again under the same rules.
Frequently asked questions
Can drawing-number normalization remove every symbol?
No. Remove only formatting differences confirmed to have no meaning. Revision codes, engineering-change codes, leading zeros, or separators may distinguish drawings and require data-owner review.
Is more fuzzy matching always better?
No. Engineering data prioritizes avoiding false matches. If a record cannot be resolved by an explicit rule, keep it unmatched for human review instead of guessing to improve a metric.
How does Aurora begin a drawing-number matching validation?
Begin with representative, de-identified DWG and order data. Confirm the authoritative field, formatting rules, exceptions, and acceptance owner, then review matched, unmatched, and merged delivery results.
Related reading
Continue through adjacent enterprise-AI questions instead of treating each article as an isolated definition.
How to find the right engineering drawing by number, part, revision, and candidate search
Engineering drawing search cannot rely on filename keywords alone. Learn how drawing numbers, part numbers, revisions, title blocks, and historical records become explainable search conditions and reviewable candidates.
An order arrives: how do you find its drawings and verify revisions?
Which drawings does an order need, and are any missing or the wrong revision? Aurora’s DWG and order-spreadsheet example explains prerequisites, number checks and delivery review.
Start manufacturing AI with work the team can verify
Choose a pilot by work frequency, data availability, human review and verifiable results. Engineering documents are the Aurora workflow example used in this article.
Connect the question to Aurora
The article provides a decision framework. Aurora's product, solutions, and workflow review put that framework into a real piece of work.