What the data for this category looks like
The core data for this category comes from formula systems, raw material batch records, internal mixers, Mooney viscometers, vulcanizing machines, and laboratory testing equipment. Daily documents include formula sheets, weighing ledgers, mixing curve charts, rubber compound quick test reports, vulcanization process cards, mold usage records, and finished product performance test sheets. Data is updated synchronously once per vehicle of raw material, per batch of rubber compound, and per mold run of products. Document fields include exclusive industrial parameters such as rubber type proportion (unit: phr), mixing temperature range (unit: ℃), vulcanization duration (unit: minutes), batch number, and mold run identifier.
What constraints these characteristics impose on the deployment and upgrade phase
High-frequency, small-batch data updates require deploying low-latency synchronization logic to avoid blocking business processes with batch tasks. Mixed import requirements for multiple types of documents require configuring unified parsing rules that support structured tables, charts, and plain text. Native data formats from industrial equipment are inconsistent, so configuration space for custom field mapping must be reserved. At the same time, process documents and exception reports relate to production compliance, so data access permission control must be strengthened during deployment, and compatible migration capabilities for historical data must be retained during upgrades.
How to set the configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
MONGODB_URI | Fill in the MongoDB connection string dedicated to industrial data synchronization, including authentication information | This category requires high-frequency writing of unstructured data such as mixing curves and weighing records, and MongoDB is suitable for this type of storage scenario |
REDIS_URL | Configure as the address of a local Redis instance, using port 6379 | Frequently updated vulcanization process cards and batch data require caching to reduce database read and write pressure |
STORAGE_S3_ENDPOINT | Fill in the endpoint address of the enterprise's internal object storage | Large-volume mixing curve charts and finished product performance reports require unified storage and support cross-node access |
FILE_TOKEN_KEY | Set a random string of 16 or more characters as the key | Prevent unauthorized access to sensitive production documents such as formula sheets |
DB_MAX_LINK | Configure as 20–30 connections | Adapt to the single-batch data volume of this category, avoid synchronization failures caused by connection pool overflow |
Three common mistakes
- After upgrading to FastGPT v4.17.0, process document sharing links fail to load normally. The cause is that the new version adjusted the file authentication logic, and authentication verification failed because
FILE_TOKEN_KEYwas not reconfigured. - A connection timeout error occurs when batch importing weighing records. The cause is that the
DB_MAX_LINKconfiguration value is too low, which cannot support high-frequency write requests and leads to database connection pool exhaustion. - Mixing curves exported from vulcanizing machines cannot be indexed by the knowledge base after upload. The cause is that
STORAGE_S3_ENDPOINTwas not configured, and the file storage path is abnormal, which prevents the parsing task from reading the source file.
How to confirm that the configuration is complete
- Log in to the FastGPT backend system configuration page, check the connection status of configuration items such as
MONGODB_URIandREDIS_URL, and confirm that there are no error prompts. - Upload a standard vulcanization process card PDF file, verify that the knowledge base can normally parse and extract core parameters such as rubber type proportion and vulcanization temperature.
- Create a test application, ask the question "What are the mixing temperature requirements for the current batch of rubber compound", confirm that the accurate content from the corresponding formula sheet is returned.
- Trigger a batch import of 10 weighing records, check that the backend system logs have no prompts for connection timeouts or storage path errors.
The values provided are common starting points and should be measured against the reader's own samples.