Feishu's official Zhiou Home Furnishing customer case introduction, RPA obtains the quality inspection photos uploaded by the factory, AI recognizes the text on the packaging box and compares it with internal data, and the results are entered into the dashboard for quality inspection personnel to view. This article examines the relationship between images, reference records, and differential processing; there is no independent verification of the cost, processing volume, or coverage ratio of the original page, nor does it infer clearance results based on this.
First confirm which item the photo belongs to
The following is ChinaOPC’s independent analysis of the container label verification task, and is not an undisclosed implementation detail by Zhiou. After a photo enters the system, you can first confirm its corresponding product, order or packaging record. If the same product has different packaging, or a single upload contains images of multiple boxes, just identifying the product name is not enough to determine which information should be checked.
You can allow the upload record to retain existing related information on the business, and check whether there are multiple candidates before verification. When the corresponding record cannot be found, the results should allow on-site personnel to see what is missing; the system cannot be allowed to select a product with the closest appearance or name and continue to generate seemingly complete quality inspection conclusions.
You also need to know whether the photo covers the area intended for inspection. If a corner of the box label is blocked, reflected or blurred, a certain field may not be read. At this time, you can be prompted to retake the corresponding location and retain the original image, so that personnel can understand whether the problem comes from the photo or the label itself.
Keep the read text and business fields separately
The recognition results can first retain the original text, and then correspond to the product code, quantity or weight according to the company's own field description. A number needs to be understood in conjunction with the label area in which it is located; if fields with different meanings are compared together, even if the numbers are similar, they may be checking two different things.
When different units, formats or languages appear, they can be processed according to the customer's confirmed requirements while retaining the original expression. The conversion results for easy calculation and the actual content printed on the outer box can be viewed separately. The system should not automatically add text that does not appear in the picture or rewrite reference materials in order to pass the comparison.
The reference materials themselves also need to be able to be located. If personnel are checking a certain batch of goods, they should know which relevant record is being used; if there are missing items or conflicts in the data, they can be listed as situations that need to be confirmed first. Image recognition can reduce manual word picking, but it cannot determine by itself which business information should be used as the basis.
The difference results should help the scene to continue processing
You can explain the results according to whether there are any differences, whether they are read clearly, whether the reference materials are complete, etc., and attach the relevant picture area, read content and reference fields. This way, personnel can quickly determine whether to check printed content, take additional photos, or find the correct business record. The classification here is ChinaOPC design suggestion and does not mean that the original project adopts the same status.
After a certain discrepancy is confirmed on-site, the handling method and whether it needs to be re-photographed for verification can be recorded. Tag modification, data confirmation and abnormal closing are different actual actions; only a change in the status of the kanban board may not mean that the problem with the goods has been resolved. Allowing follow-up personnel to check the processing basis will help continue to complete the quality inspection work.
The original case introduces the division of labor between RPA and AI: the former undertakes automatic operations such as image acquisition, and the latter participates in text recognition and difference verification. When a small team provides related services, it should also describe the scope of delivery based on its true capabilities, and not package connecting interfaces, reading pictures, and understanding business meaning into the same model capabilities.
You can first select a box label and a set of approved photos with the customer, observe how shooting gaps, incorrect associations, and field differences are discovered, and then discuss expanding the scope. Zhiou Home Furnishing is a case of a large enterprise. Its scale, cost and process cannot be directly applied to OPC services; the system results cannot replace the actual inspection and professional confirmation that the enterprise should complete.
Extended reading: [How to determine fields when AI organizes product data] (), [How to check after scanning pictures and converting them into text] () and [How to check fulfillment capabilities after foreign trade is put on shelves] (), and understand the relationship between data, pictures and actual delivery. Related reading: How to determine fields when sorting product information using AI; How to check after scanning pictures and converting them into text; How to check the performance ability after foreign trade is put on the shelves.