What the data for this category looks like
Business data for meat products and slaughter processing is generated around production batches. Data sources include animal quarantine information, supplier batches, production traceability records, cold chain temperature collection data, metal detection results, and laboratory microbial test reports. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature logs, and microbial test reports. Data update frequency is adjusted based on the production process, with updates per batch or per shift. Temperature control data is collected and updated continuously. Data fields include batch number, quarantine identifier, temperature value, test results, and more. Some fields have dedicated units: temperature uses degrees Celsius, and batches are identified by production shift.
What constraints these characteristics impose on deployment and upgrade
Batch-bound data and compliance-sensitive documents for this category require strict isolation of business data from data of other categories during deployment, to prevent cross-mixing. Continuously collected temperature control data and frequently updated production records need storage configurations optimized for high-concurrency reading and real-time synchronization. Access to compliance documents such as quarantine certificates requires additional permission controls to avoid sensitive information leaks. Production data related to raw and cooked product zoning must be stored in separate categories to ensure data traceability accuracy, and prevent data interference from different production links when calling fields for tender document and quotation preparation.
How to set the configurations
The following configurations are formulated for FastGPT v4.17.0:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 10–15 connections | Adapt to scenarios with concurrent reading of multiple batch data, avoid connection pool exhaustion during production data import |
MONGODB_URI | Point to a dedicated collection storing quarantine and production records | Isolate business data for meat product processing from data of other categories, ensuring traceability accuracy |
REDIS_URL | Bind to a locally deployed cache instance | Cache quarantine numbers and temperature data linked to batches, speeding up field calls for tender documents |
STORAGE_S3_ENDPOINT | Use the dedicated storage bucket address for cold chain temperature records | Store large volumes of continuous temperature control log files, meeting the storage needs of frequent updates |
FILE_TOKEN_KEY | Generate a dedicated key per production batch | Restrict unauthorized access to sensitive documents such as quarantine certificates, complying with compliance requirements |
Three common mistakes
- A parsing failure error with status code 400 occurs when uploading PDF files of quarantine certificates. This happens because the chunking rule for multi-page compliance documents is not configured, leading to long text truncation during parsing.
- Some temperature fields are empty when importing Excel files containing batch temperature data. This occurs because the unit matching rule for temperature values is not specified, causing numerical values with Celsius suffixes to fail correct identification.
- Timeouts occur when calling the quotation generation function after deployment or upgrade. This happens because the value of
DB_MAX_LINKis not adjusted, leading to connection pool exhaustion during concurrent reading of multiple batch data.
How to confirm the configurations are correct
- Upload a single quarantine certificate PDF, verify that core fields such as quarantine number and batch number are extracted correctly after parsing, with no truncation or missing content.
- Import an Excel file containing temperature data, check that all temperature fields are correctly identified and stored, with no empty values.
- Initiate more than 10 concurrent requests for tender document generation, verify that response time stays within a reasonable range, with no connection timeout errors.
- Check the uploaded temperature control log files in the storage bucket, confirm that their association tags match production batches correctly.
The values given are common starting points and should be measured against the reader's own samples.