Mission Knowledge System

Offline mission and ground-segment knowledge for space operations.

Industry	Level	Difficulty	Recommended mode
Aerospace	5 of 5	Frontier	flat

Coming soon

A complete, runnable Mission Knowledge System reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Missions generate vast documentation.
• Ground stations may be isolated.
• Anomalies need fast context.

How SrvDB powers Mission Knowledge System

1. Procedures, anomaly reports and telemetry summaries live in SrvDB.
2. Operators retrieve similar past anomalies.
3. Air-gapped and crash-safe.
4. Export-controlled data stays local.

What changes

Today	With Mission Knowledge System on SrvDB
Slow anomaly context	Instant similar cases
Network dependence	Offline
Scattered docs	One search

How it works

1. Ingest procedures and reports.
2. Embed telemetry summaries.
3. Search on anomalies.
4. Decide.
5. Record outcomes.

SrvDB features it uses

• Fully offline: SrvDB runs in-process and never opens a network connection.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.
• Several databases: each database is a directory, so different data types or tenants stay separate.
• Exact search (flat): every query compares against every stored vector, so results are exact and explainable.

Use cases

• Satellite anomalies
• Ground-station procedures
• Launch campaigns
• Payload operations
• Training
• Lessons learned

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./mission-knowledge-system-db", dimension=768)

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def similar_anomalies(anomaly, k=5):
    hits = db.search(embed(anomaly), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Not for flight-critical software.

Caution

SrvDB is not certified software. Use it for decision support and knowledge retrieval, never in flight-critical or certified systems.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Aerospace projects

Level	Project	What it does
1 · Beginner	Maintenance Knowledge Assistant	Cited answers from manuals and service bulletins for technicians.
2 · Intermediate	Safety Report Analyzer	Clusters safety reports and links similar events.
3 · Advanced	Fleet Finding Matcher	Matches new maintenance findings to similar findings across the fleet.
4 · Expert	Borescope Image Search	Find similar borescope images and the findings they led to.
5 · Frontier	Mission Knowledge System (this page)	Offline mission and ground-segment knowledge for space operations.

See also: Use cases · Live examples (coming soon)