What data for this category looks like
Aquaculture yield rate-related data comes from four categories: pond IoT monitoring devices, aquaculture cost ledgers, buyer quotation systems, and fishery administration statistical reports. Data update rhythms vary: real-time monitoring data such as dissolved oxygen and water temperature from ponds is reported every 15 minutes. Daily cost data such as daily feeding amount and seed consumption is aggregated in the early morning each day. Weekly out-pond volume and acquisition price ledgers are updated every Monday.
Each structured single data document includes fields such as pond ID, aquaculture species, daily core monitoring parameters, cost breakdowns, acquisition unit price, and out-pond weight. Field units include kilograms, degrees Celsius, milligrams per liter, yuan per kilogram, and others. There is no unified fixed format template.
Constraints imposed by these characteristics on model access and configuration
Multi-source heterogeneous data sources require configuring access adapters for multiple data source types, supporting IoT protocol reports, CSV report uploads, API interface pulling, and other data formats. Differing update rhythms require setting differentiated synchronization trigger rules. Using a unified synchronization interval will lead to data lag or repeated pulling issues.
Inconsistent field units and formats require configuring automated field mapping and unit conversion rules, to avoid result deviations caused by the large language model using mixed units during calculations. Yield rate calculations need to associate multi-dimensional scattered data, so parameters for associative retrieval must be configured to ensure the large language model can simultaneously obtain core fields such as costs, sales volume, and acquisition unit price for calculations.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DATA_SOURCE_TYPE | "Structured Multi-Data Source" | Aquaculture data includes multiple structured formats such as IoT real-time data, daily cost reports, and acquisition price ledgers |
SYNC_CRON | */15 * * * * (real-time pond data), 0 0 * * * (daily statistical data) | Matches the update rhythms of different data sources: pull real-time data every 15 minutes, update daily data in the early morning daily |
FIELD_MAPPING | Configure unit conversion rules (kilograms ↔ jin, yuan/kilogram ↔ yuan/jin) | Field units of different data sources vary; must be unified to standard units for yield rate calculation |
RECALL_COUNT | "Top 7 entries" | Yield rate calculation requires associating core dimensions such as feeding amount, seed cost, acquisition unit price, and out-pond weight; 7 entries can cover all associated fields |
SIMILARITY_THRESHOLD | 0.75 | Filters low-relevance historical data to avoid redundant information interfering with accurate yield rate calculation |
CONTEXT_MAX_TOKENS | 8000 | Adapts to the large language model's context requirements for processing multi-dimensional associated data, avoiding calculation errors caused by content truncation |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: The yield rate calculation results returned by the large language model do not match the actual ledger, with numerical deviations. Cause: No field mapping rules are configured, and units from different data sources are not unified, causing the large language model to use mixed units for calculations.
- Phenomenon: A "Third-party model access failed" error is displayed during deployment, with status code
401 Unauthorized. Cause: The API key and interface address of the third-party large language model are not correctly configured, or the key permissions are insufficient. - Phenomenon: Retrieval results contain a large number of irrelevant non-aquaculture data, and the number of results exceeds expectations. Cause:
SIMILARITY_THRESHOLDis not set, or the threshold is set too low, causing low-relevance data to be recalled.
How to verify successful configuration
- Manually trigger a data synchronization, check the background synchronization log, and confirm that all configured data sources have successfully pulled the latest data.
- Input a test question such as "What is the yield rate of Pond XX last week", verify that the field units in the returned results are unified, and that the calculation logic conforms to preset rules.
- Check the access status of the third-party large language model, confirm that the interface displays "Connection normal" with no error prompts.
- Adjust the value of
SIMILARITY_THRESHOLD, verify whether the relevance of the retrieval results meets expectations, and adjust the threshold as needed.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.