What the data for this category looks like
The data for this category covers the full operational workflow of EHS departments and maintenance teams in manufacturing enterprises such as chemical and metallurgical ones. Daily documents include risk identification lists, work tickets for hot work, confined space work, lifting work and other types, environmental monitoring records, hazardous waste ledgers, emission monitoring data, and accident and hidden danger rectification reports. Data sources include industry regulatory standards, online environmental monitoring equipment, gas detectors, operator qualification files, equipment isolation lists, and regulatory platform interfaces. Data update frequencies cover three scenarios: each work submission, real-time monitoring pushes, and regular inspection archiving. Data fields include work type, risk level, personnel qualification number, monitoring value, rectification deadline and more. No unified fixed format exists, and content adjusts dynamically based on work scenarios.
What constraints these characteristics impose during deployment and upgrade
The data for this category is multi-source, heterogeneous, and has large differences in update frequencies. During deployment, adaptation of access and synchronization for multiple data source types is required to avoid data delays or loss. Work documents have no unified format, so support for custom field parsing and storage must be provided. This places flexible configuration requirements on the knowledge base's document processing module. The high veto power attribute across processes requires strict permission control. During deployment, fine-grained team and department permission isolation rules must be configured. Multiple types of data require persistent storage, and some data must be synchronized to multiple nodes in real time. This places constraints on the clustered deployment of databases and caches. During upgrade, compatibility with the format of historical work data must be maintained to prevent historical documents from becoming unreadable due to changes in parsing rules.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Connection string containing 2 replica set nodes | Adapt to concurrent write and high availability requirements for high-frequency work data |
DB_MAX_LINK | 200 connections | Meet the connection upper limit requirement for multiple nodes accessing work data simultaneously |
REDIS_URL | Authenticated address with cluster mode enabled, cache expiration time set to 600 seconds | Implement caching of real-time monitoring data and multi-node session synchronization |
STORAGE_S3_ENDPOINT | Enterprise internal object storage endpoint | Store unstructured documents such as work tickets and monitoring records, support data localization |
FILE_TOKEN_KEY | 32-bit randomly generated key | Encrypt access tokens for work documents, prevent unauthorized access |
FE_DOMAIN | Enterprise internal access domain name | Limit front-end access scope, avoid exposing sensitive work data over the public internet |
Three Common Misconfigurations
- Symptom: Team management functions cannot be enabled in the backend, and administrators cannot create department permission groups. Cause:
ROOT_KEYwas not configured during deployment, administrator initialization was not completed, or the permission module configuration file was not loaded. - Symptom: After multi-node deployment, work data is only stored on a single node, and other nodes cannot access it. Cause:
STORAGE_S3_ENDPOINTwas not configured to point to shared storage, orREDIS_URLdid not enable cluster mode for session synchronization. - Symptom: After a work ticket is uploaded, the knowledge base parsing result lacks personnel qualification or risk level fields. Cause: Parsing rules were not configured for the custom fields of the work ticket, so some business fields were not extracted.
How to Verify Proper Configuration
- Access the internal domain bound to
FE_DOMAIN, log in with an administrator account, verify that entry to the team management page is possible, and that departments and permission groups can be created. - Submit a hot work ticket, verify that data can be written to the database corresponding to
MONGODB_URI, and documents are stored in the storage bucket pointed to bySTORAGE_S3_ENDPOINT. - Simulate the upload of real-time gas monitoring data, verify that
REDIS_URLcan cache data and synchronize it to all deployed nodes. - Generate an access link for a work document, remove the
FILE_TOKEN_KEYparameter, verify that the document cannot be accessed.
The values provided are common starting points and should be evaluated against the reader's own samples.