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cca714755a |
fix(iace): stronger relevance filter + matcher wrong-machine penalty
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Relevance filter: now checks PatternName in addition to ZoneDE+ScenarioDE, catches "Spielplatz", "Umreifungsband", "Fahrtreppe" etc. in pattern names. Added more generic safety terms to whitelist (welle, getriebe, kette, etc.) Matcher: rebalanced weights (category 0.3, keywords 0.3, zone 0.4) to prioritize zone/component specificity. Added wrong-machine penalty (0.3x) when engine hazard mentions machine-specific terms absent from GT context (e.g. "Kollision zweier Roboter" for a single-robot GT entry). Fixes 18 problematic matches: 8 wrong-machine, 9 zone-mismatch, 1 category. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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d31c2fe018 |
feat(iace): hazard block view — parent/child grouping
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Backend: - hazard_blocks.go: ComputeHazardBlocks() groups hazards by category + component + zone. Parent = highest risk in group. Children covered by parent's measures are flagged (no separate assessment needed). - iace_handler_blocks.go: GET /projects/:id/hazard-blocks endpoint with summary stats (blocks, covered children, assessments saved) Frontend: - HazardBlockView.tsx: Expandable block view with summary cards, parent-child hierarchy, coverage badges, and "abgedeckt" indicators - hazards/page.tsx: New "Bloecke" tab alongside "Hazard-Liste" and "Risikobewertung" No database schema changes — grouping is computed at runtime. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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733d2bcc7b |
feat(iace): per-category hazard caps for precision improvement
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Add categoryHazardCap() with ISO 12100-proportional limits: - mechanical: 3x components (min 15, max 60) - electrical: 1x components (min 8, max 20) - secondary (thermal, noise, material): 4-8 - software/IT/organizational: 2-5 (minimal for machinery assessment) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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be2ac762bd |
feat(iace): narrative vocabulary overlap filter replaces blacklist
Replace machine-specific term blacklist with generic vocabulary overlap: - Extract significant words (>=5 chars, not generic safety terms) from pattern zone/scenario - If pattern has specific words but NONE appear in narrative → filter - genericSafetyTerms whitelist with ~50 terms that appear in all assessments - Truly generic approach: works for any machine type without maintenance Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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1bd892afbf |
feat(iace): narrative relevance filter + zone normalization for precision
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- isPatternRelevant() filters patterns whose zone/scenario mentions machine-specific terms (extruder, stanzpresse, spielplatz, etc.) absent from the actual machine narrative - normalizeZoneKey() clusters similar zones for smarter dedup (e.g. "Schaltschrank, Sammelschiene" = "Schaltschrank-Innenraum") - machineSpecificTerms list with 40+ terms for generic filtering Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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8bb90d73e5 |
feat(iace): benchmark system + erklaerteil + dedup-fix
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- Erklaerteil-Template fuer Risikobeurteilungen (risk_assessment_template.go) in PDF-Export, Markdown-Export und Frontend ReportPrintView eingebaut - Ground Truth Benchmark-System: Datenmodell, Fuzzy-Matching-Engine, 3 API Endpoints (import-gt, benchmark, benchmark/summary) - Frontend Benchmark-Tab mit Score-Cards, Kategorie-Breakdown, Hazard-Vergleichstabelle (Zugeordnet/Fehlend/Extra), Business Impact - Erster Benchmark: 13.3% Coverage (Baseline) gegen 60 GT-Eintraege - Dedup-Fix: seenCat[cat] -> seenCatZone[cat+zone] erlaubt mehrere Gefaehrdungen pro Kategorie an verschiedenen Gefahrenstellen - Komponenten-spezifische Hazard-Namen und Zone-basierte Zuordnung Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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e785b6d695 |
fix(use-case-compiler): compile questions from MCs, not hardcoded
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Changes the compile flow to always query Master Controls from DB first: 1. doc_check_controls → Mode A (deterministic) 2. LLM generation via Ollama/Claude → Mode B 3. Derive from MC name → fallback 4. Template hardcoded questions → absolute fallback Previously, templates with pre-defined questions just returned those without ever hitting the DB. Now MC-compiled questions take priority and template questions fill gaps for uncovered topics. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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edbf6d2be5 |
feat(dsms): Stufe 2+3 — Evidence/TechFile → DSMS + Version Chains + Audit Timeline
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Stufe 2A: Evidence Upload → automatische DSMS-Archivierung
- Nach SHA-256 Hash → archive_to_dsms(), CID im Audit-Trail
- Evidence mit CID wird automatisch zu E2 (hash-verifiziert) hochgestuft
Stufe 2B: IACE Tech-File Export → DSMS
- PDF/Excel/DOCX/Markdown Exporte werden nach DSMS archiviert
- archiveTechFile() Helper fuer alle 4 Formate
Stufe 3A: DSMS Gateway — parent_cid + History Endpoint
- parent_cid + tenant_id Felder in DocumentMetadata
- GET /documents/{cid}/history — folgt parent_cid-Chain (max 50 deep)
Stufe 3C: Audit Timeline UI
- Neue Seite /sdk/audit-timeline
- Vertikale Timeline mit farbigen Action-Dots
- Filter: Alle, Nachweis, DSMS-Archiv, Control, Dokument, DSFA, VVT, TOM
- CID-Badges fuer DSMS-archivierte Eintraege
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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06bfbd1dca |
feat(use-case-compiler): MC-based compliance questionnaires with scoring
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Implements the Use-Case Compiler that turns Master Controls into interactive compliance audits. 5 templates (Vendor Check, SAST/DAST, DSGVO, NIS2, CRA), deterministic + LLM question generation, scoring engine with regulation/severity breakdown, and gap detection. - Backend: 9 API endpoints, 22 unit tests (all pass) - Frontend: Template selector, questionnaire, result dashboard - Migration 027: usecase_audits + usecase_answers tables Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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7d9f5a1f76 |
feat(iace): LLM-gestuetzte Failure Mode Erkennung
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POST /projects/:id/components/:cid/suggest-fms - Baut FMEA-Experten-Prompt aus Komponentenname + Maschinenkontext - LLM antwortet mit 5 FMs als JSON (Mode, Effect, S/O/D) - Fallback auf Bibliotheks-FMs wenn LLM nicht verfuegbar - Nutzt ProviderRegistry (Ollama primary, Anthropic fallback) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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6ce5b4bf41 |
feat(iace): VDA-Format FMEA Excel Export
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- GET /projects/:id/fmea/export → xlsx im VDA-Formblatt - Spalten: Nr, Komponente, Typ, Fehlerart, Fehlerfolge, S, O, D, RPZ, AP, Massnahme - AP-Zellen farbig: H=rot, M=gelb, L=gruen - Dependency: github.com/xuri/excelize/v2 (BSD-3-Clause) - Frontend: "VDA Excel exportieren" Button auf FMEA-Seite Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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f5664612ad |
feat(iace): Einsatzbereich / Branche — filtert branchenspezifische Patterns
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Neues Feld "Einsatzbereich" auf Interview-Seite (Sektion 7) mit 15 Branchen. Pattern Engine bekommt MachineTypes aus MatchInput → branchenfremde Patterns (Medizin, Aufzug, Bau etc.) feuern nur wenn die Branche ausgewählt ist. Refactoring: iace_handler_init.go aufgeteilt in init + init_helpers (LOC-Limit). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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66d30568e2 |
feat(dsms): Stufe 1 — Gap-Analyse Report wird in DSMS archiviert
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- Go DSMS Client (internal/dsms/client.go): Archive() + Verify() - Python DSMS Client (compliance/services/dsms_client.py): archive_to_dsms() + verify_dsms() - Gap-Analyse AnalyzeProject() archiviert Report-JSON nach DSMS - Response enthält dsms_cid wenn Archivierung erfolgreich - Frontend: Grünes "Revisionssicher archiviert" Badge mit CID im GapDashboard - DSMS Proxy Route (/api/sdk/v1/dsms/[...path]) für Verify-Abfragen Stufe 2 (Evidence Upload → DSMS) und Stufe 3 (Version Chains) folgen. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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a0bb9e3aed |
feat(iace): "Neu initialisieren" Button + DeleteHazard
- POST /initialize?force=true loescht bestehende Hazards + Mitigations und erstellt sie neu mit aktuellen Betriebszustaenden - Orange "Neu initialisieren" Button auf Interview-Seite (mit Confirm-Dialog) - DeleteHazard Store-Methode (kaskadiert Risk Assessments) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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cb8fb65d3e |
feat(iace): Betriebszustand-Traceability auf Hazards + Mitigations
Hazards zeigen jetzt farbige Badges mit den Betriebszustaenden die sie ausgeloest haben (z.B. "Wartung", "Not-Halt"). Mitigations erben die States ihrer verknuepften Hazards. Backend: OperationalStates im Function-Feld encodiert (kein DB-Schema), beim Lesen als operational_states[] JSON-Feld zurueckgegeben. Frontend: Indigo-Badges in HazardTable + MitigationCard. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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285b74382a |
fix(iace): Initialize pipeline reads operational_states from metadata
The Betriebszustand-UI saved states to metadata.operational_states but the initialize handler only read states from the parsed narrative text. Now merges both sources so the UI selection actually affects which patterns fire during initialization. Added integration E2E test that verifies: 2 states → fewer patterns, 9 states → more patterns. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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53c641800f |
feat(iace): Phase 5 — Betriebszustand-UI + E2E Tests
- GET /operational-states endpoint (9 States + 20 Transitions) - Frontend: Operational States page with state cards, transitions graph, delta preview - Navigation: Betriebszustaende entry between Grenzen and Normenrecherche - E2E: 60+ new Phase 5 tests (operational states, hazards, mitigations, classification) - E2E: Updated expected counts for expanded libraries (476 measures, 1114 patterns) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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dabc2358ab |
feat(gap): Regulatory Gap Analysis Engine — Phase A Backend
Product Profile → Regulatory Classification → MC Gap Assessment → Priority List. - 12 regulations supported (CRA, AI Act, NIS2, DSGVO, Data Act, MiCA, PSD2, AML, MDR, Machinery, TDDDG, LkSG) - Scope signal extraction from product profile - Priority scoring: Severity × Deadline × Dependency - 5 industry templates (IoT, Exchange, Cobot, SaaS, Medical) - 8 API endpoints under /sdk/v1/gap/ - DB migration for gap_projects table - Full build passes Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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9c0d471277 |
feat(iace): Sprint 4D — Failure Mode Layer (FMEA-Faehigkeit)
150 Failure Modes in 11 ComponentTypes: - Sensor (20): Signalverlust, Drift, Falschmeldung, Encoder-spezifisch - Controller (20): Watchdog, Speicher, Bus, Safety-SPS CCF, Antrieb - Actuator (15): Blockiert, Ueberlast, Haltekraftverlust, Schuetz verschweisst - Mechanical (20): Ermuedungsbruch, Lagerschaden, Kettenriss, Werkzeugbruch - Electrical (15): Isolation, Kurzschluss, Erdschluss, Lichtbogen - Software (15): Exception, Race Condition, Buffer Overflow, Timing - Hydraulic/Pneumatic (15): Schlauchplatzer, Ventil blockiert, Kavitation - Safety Device (15): Failure-to-trip, CCF, Bremsenverschleiss, PL-Degradation - Network (10): Paketverlust, Latenz, Man-in-the-Middle - AI/ML (5): Model Drift, Adversarial Input, Bias Architektur: - FailureModeEntry Struct mit FMEA-Scores (Severity/Occurrence/Detection 1-10) - RPZ = S x O x D (max 1000, Schwelle >= 100 = Massnahme erforderlich) - RequiredFailureModes auf HazardPattern fuer FM-gesteuertes Pattern-Matching - MatchInput.FailureModes + MatchReason "failure_mode" (Explainability) - GET /failure-modes?component_type= API-Endpoint 10 Tests: Count, UniqueIDs, ValidTypes, NonEmpty, Distribution, RPZ (3x), NilFires, RPZDistribution Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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9a9a11b248 |
feat(iace): Sprint 4C — Delta Impact Analysis
Neuer Endpoint POST /projects/:id/delta-analysis: - Input: aktuelle + vorgeschlagene Aenderung (Components, Energy, States, Roles) - Output: Diff der Pattern-Matches (added/removed Patterns, Hazards, Measures) - DeltaMatch() auf PatternEngine: Match(current) vs Match(proposed) - DeltaResult mit AddedPatterns, RemovedPatterns, Counts, SummaryDE Beispiel-Output: SPS hinzufuegen → +55 Patterns, +5 Hazard-Kategorien, +17 Massnahmen Maintenance-State hinzufuegen → +10 Patterns, +2 Hazards, +2 Massnahmen 7 Tests: NoChange, AddComponent, RemoveComponent, AddState, AddRole, Summary, Symmetric Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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d339d1edc7 |
feat(iace): Sprint 4B — ISO 12100 Hazard/Situation/Harm Trennung
ISO 12100 trennt: Hazard (Quelle) → Hazardous Situation (Person exponiert) → Harm (Verletzung).
Bisher war alles in einem Hazard-Record vermischt.
Implementierung als abgeleitetes Feld (keine DB-Migration noetig):
- HazardType Feld auf Hazard Entity ("hazard"|"hazardous_situation"|"harm")
- DeriveHazardType() berechnet Typ aus Scenario/PossibleHarm/Category
- Explizites Override moeglich (HazardType direkt setzen)
- GeneratedHazardType auf HazardPattern fuer Pattern-gesteuerte Zuweisung
- Store: GetHazard/ListHazards setzen HazardType automatisch
- Init-Handler: Fuellt jetzt TriggerEvent, PossibleHarm, AffectedPerson, HazardousZone
aus Pattern-Match-Daten (vorher leer gelassen)
6 neue Tests: ScenarioAndHarm, HarmOnly, CategoryOnly, ExplicitOverride,
EmptyFallback, PatternMatchField
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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f07c4db164 |
feat(iace): Sprint 3B — Human Interaction Model
- 6 Standard-Rollen: operator, maintenance_tech, programmer, cleaning_staff, bystander, supervisor - HumanRoles []string Feld in HazardPattern, MatchInput, PatternMatch - patternMatches() filtert Patterns nach Rolle (nil = feuert fuer alle Rollen) - MatchReason um human_role Typ erweitert (Explainability) - 25 bestehende Patterns mit Rollen annotiert: - Cobot HP059/062/064 → operator/programmer - Maintenance HP700-714 → maintenance_tech/programmer - Operational HP070/073-078/080 → operator/maintenance_tech/programmer - Init + Parser Handler reichen Roles an MatchInput durch - 4 neue Tests: NilFiresAlways, MaintenanceTechFilter, ProgrammerTeachMode, RoleCount Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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77a497d930 |
feat(iace): Sprint 3A — Operational State Graph + fix(ucca) flaky keyword sort
State Graph: - 9 Standard-Betriebszustaende (startup, homing, automatic_operation, manual_operation, teach_mode, maintenance, cleaning, emergency_stop, recovery_mode) - 20 State-Transitions als gerichteter Graph - OperationalStates + StateTransitions Felder in HazardPattern, MatchInput, PatternMatch - patternMatches() filtert Patterns nach Betriebszustand (nil = feuert immer) - Narrative-Parser extrahiert States aus Maschinenbeschreibung (22 Keywords + 4 Transition-Keywords) - 27 bestehende Patterns mit State-Einschraenkungen annotiert (10 operational, 15 maintenance, 2 cobot) - MatchReason um operational_state + state_transition Typen erweitert (Explainability) - 6 neue Tests: NilFiresAlways, MaintenanceFilter, StateTransition, MatchReasons, Count, TransitionValid UCCA fix: - Stabiler Tiebreaker (Pattern-ID aufsteigend) bei gleichem Keyword-Score in MatchByKeywords - Behebt flaky TestControlPatternIndex_MatchByKeywords (1/10 Failure-Rate durch Go map iteration order) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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2e29b611c9 |
feat(iace): Phase 1 — Haftungs-Fixes, Massnahmen-Verkabelung, Explainability Engine
Phase 1A — Haftungs-kritische Fixes: - SIL/PL-Badges als "Vorab-Einschaetzung" mit Tooltip gekennzeichnet - Coverage-Disclaimer in CE-Akte, Projekt-Uebersicht und Print-Export - Norm-Referenzen: 42 Kapitelverweise durch Themen-Deskriptoren ersetzt Phase 1B — Massnahmen-Verkabelung: - 16 neue Massnahmen (M201-M216) fuer bisher unabgedeckte Kategorien (communication_failure, hmi_error, firmware_corruption, maintenance, sensor_fault, mode_confusion) - Kategorie-Fallback im Initialize-Endpoint: ordnet Massnahmen aus der Bibliothek automatisch per HazardCategory zu (max 8 pro Kategorie) - Total: 225 → 241 Massnahmen, 0 Kategorien ohne Massnahmen Phase 1C — Explainability Engine: - MatchReason Struct in PatternMatch (type, tag, met) - Pattern Engine schreibt fuer jeden Match strukturierte Begruendungen - Frontend zeigt "Erkannt weil: Komponente X, Energie Y, Kein Ausschluss Z" Weitere Aenderungen: - BAuA/OSHA Regulatory Hints: 3 Enrich-Endpoints (per Hazard, per Measure, Batch) - Dokumente-Tab in IACE-Bibliothek (36.708 Chunks aus Qdrant) - Varianten-UX: Basis-Projekt-Summary auf Varianten-Seite - Projekt-Initialisierung: POST /initialize kettet Parse→Komponenten→Patterns→Hazards→Massnahmen→Normen - 18 pre-existing TS-Fehler gefixt, Route-Konflikt behoben - Component-Library + Measures-Library Tests aktualisiert Tests: Go alle bestanden, TS 0 Fehler, Playwright 141+ bestanden Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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8682522212 |
feat: Variantenmanagement — Sub-Projekte mit GAP-Analyse
Backend: - parent_project_id auf iace_projects (DB + Go Struct) - POST/GET /variants + GET /variant-gap Endpoints - GAP-Analyse: Differenz Hazards/Massnahmen/Kategorien Frontend: - VariantPanel auf Projekt-Uebersicht - Variante erstellen Dialog - Sidebar-Anzeige (Variantenanzahl / Basis-Link) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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0371eecc03 |
fix: Struktureller Fix — Maschinentyp-Filter fuer Keywords + Patterns
PROBLEM: Cobot-Projekt hatte 52 Pressen-Hazards weil Keywords wie "stempel" und "stoessel" ohne Maschinentyp-Kontext matchten. FIX an 3 Stellen: 1. KeywordEntry.MachineTypes — Pressen-Keywords nur fuer press/*_press 2. ParseNarrative(text, machineType) — Parser laedt Maschinentyp aus Projekt 3. HazardPattern.MachineTypes — Pressen-Patterns (HP045-HP058) nur fuer Pressen Verhindert zukuenftig falsche Zuordnungen bei neuen Kundenprojekten. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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1502ac6d8f |
feat: Kamera/PII-Trigger differenziert + CE × Compliance FAQ
- HP059 Trigger: "DSFA erforderlich" → "zu pruefen" mit Entscheidungslogik (Edge-Processing ohne Speicherung/Personenerkennung = keine DSFA) - 6 FAQ-Eintraege: Kamera-PII, zugekaufte Baugruppen, Herstellererklaerung, KI-Hochrisiko, CRA OTA-Updates, verkettete Produktionslinien - GET /compliance-faq Endpoint mit Kategorie-Filter Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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8087e74e88 |
feat: Verification handler split + ListVerificationPlans
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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89af88ef7d |
feat: Fortschritts-Tracker + Verifikation-Endpoints + Tech-File Erweiterung
- Übersicht: Completeness Gates durch Projektfortschritts-Tracker ersetzt (6 CE-Prozessschritte mit Status + Naechster-Schritt Empfehlung) - Verifikation: GET/POST/DELETE /verifications Endpoints + Alias-Handler - Tech-File: Anhang IV Struktur-Erweiterung - Maßnahmen: Expandable Details vorbereitet Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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5236864521 |
perf: N+1 Fix in GetProject/buildCompletenessContext
462 einzelne Queries (Assessments + Mitigations pro Hazard) durch 2 Batch-Queries ersetzt. GetProject von ~22s auf <1s. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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6cec1dcdba |
perf: N+1 Query Fix — ListHazards 231x schneller
Ersetzt 231 einzelne DB-Queries durch 1 Batch-Query mit DISTINCT ON (hazard_id) JOIN. Ladezeit von ~40s auf <1s. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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56892cf7dc |
feat: CE × Compliance Crossover Engine
Automatische Erkennung von DSGVO/AI Act/CRA/NIS2/Data Act Implikationen bei CE-Gefaehrdungen. 50 Trigger-Mappings auf Hazard-Patterns → Compliance-Module mit Modul-Links. - compliance_triggers.go: 50 Pattern→Regulation Mappings - compliance_crossover.go: Engine die Projekt-Hazards gegen Trigger prueft - iace_handler_compliance.go: GET /compliance-triggers API - ComplianceAlerts.tsx: Frontend Alert-Panel auf Projekt-Uebersicht Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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1005ba0398 |
feat: Normen-Bibliothek auf 751 Normen finalisiert
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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ba9558384f |
feat: Normen-Bibliothek auf 620+ erweitert + wave3 fixes
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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2e1e18d853 |
feat: Normen-Bibliothek auf 617 erweitert (Ziel: 700)
Wave 3: +161 Normen (456 → 617) - Serien-Lücken geschlossen (EN 1870, EN 474, EN 1034, EN 81, ISO 4254) - Glas, Leder, Backwaren, Tabak, Medizin (IEC 60601), Labor, Feuerwehr - Spielplatz, Fitness, Schwimmbad, HVAC, Kältetechnik - PSA (Schuhe, Handschuhe, Augenschutz, Gehörschutz, Atemschutz) - Leitern, Gerüste, Drahtseile, Gasgeräte, Messtechnik Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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97a52533a8 |
Merge remote gitea/main — resolve conflicts keeping local (origin) state
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Local origin is 20+ commits ahead of remote gitea. All conflicts resolved by keeping HEAD (our version) which includes the full 56→138 check expansion and doc_checks package split. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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e7f2f98da3 |
feat: IACE CE-Compliance Module — Normen, Risikobewertung, Production Lines
Major features: - 215 norms library with section references + Beuth URLs (A/B1/B2/C norms) - 173 hazard patterns with detail fields (scenario, trigger, harm, zone) - Deterministic pattern matching: Component × Lifecycle × Pattern cross-product - SIL/PL auto-calculation from S×E×P risk graph - Risk assessment table with editable S/E/P dropdowns - Production Line Dashboard with animated station flow (Running Dots) - IACE process flow + norms coverage on start page - Non-blocking cookie banner, ProcessFlow SSR fix - 104 Playwright E2E tests passing Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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d7b287889e |
fix: IACE parser handler — use MatchOutput.SuggestedHazards instead of MatchedPatterns fields
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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d4b7943d54 |
feat: IACE deterministic narrative parser + library extensions
Library Extensions: - 15 new components (C121-C135): knee lever, hydraulic ram, lubrication system, extraction system, vibrating plate, die tooling, transfer system, hoist, chute, oil drip tray, pressure relief valve, die space, flywheel, bin changeover station, inspection scale - 8 new tags: person_under_load, two_hand_control_required, thermal_accumulation, mechanical_transmission, oil_mist_risk, rapid_energy_release, gravity_suspended_load, bypass_risk - 14 new patterns (HP045-HP058): ram drop, die space crushing, oil mist inhalation, hot workpiece burns, suspended load, transfer draw-in, ejection fall, accumulator pressure release, impact noise, flywheel residual energy, guard bypass, two-hand misoperation, oil leakage, ergonomic bin changeover Deterministic Parser (NO LLM): - keyword_dictionary.go: ~100 entries mapping DE/EN keywords to component IDs, energy source IDs, and tags - narrative_parser.go: ParseNarrative() extracts components, energy sources, lifecycle phases, roles, tech specs, and context tags from free-text machine descriptions via keyword matching + regex - Tech spec regex: extracts kN, V, °C, bar, kW, rpm values and derives energy sources + severity tags automatically - iace_handler_parser.go: POST /projects/:id/parse-narrative endpoint chains parser → pattern engine → hazard suggestions Test: Paste Kniehebelpresse description → should detect 10+ components, 15+ hazards, all deterministically without LLM. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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717c31547a |
feat: Regulatory News Dashboard — proaktive Compliance-Alerts
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Zeigt anstehende regulatorische Fristen im Dashboard an, abgeleitet aus den bestehenden Obligation v2 JSON-Dateien. Keine neue DB-Tabelle. Erster News-Eintrag: Widerrufsbutton-Pflicht ab 19.06.2026 (EU-RL 2023/2673, §356a BGB) — eigener Text, keine externe Quelle. Features: - Go Service: scannt Obligations nach Fristen, berechnet Urgency - API: GET /sdk/v1/regulatory-news mit Countdown + Farbcodierung - Dashboard: RegulatoryNewsFeed Sektion mit Countdown-Badges - Vorlage: news-Feld in v2 JSON fuer zukuenftige regulatorische Updates - 11 Tests (Sortierung, Urgency, Deadline-Parsing, Real-File-Test) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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6fcf7c13d7 |
feat: Unified Facts Bridge — Company Profile fuer alle Bewertungsmodule
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Verbindet Firmendaten (Mitarbeiterzahl, Branche, Land, Umsatz) mit der UCCA-Bewertung und dem Compliance Optimizer. Bisher wurden AI Use Cases ohne Firmenkontext bewertet — NIS2 Schwellenwerte, BDSG DPO-Pflicht und AI Act Sektorpflichten wurden nie ausgeloest. Aenderungen: - NEU: company_profile.go — MapCompanyProfileToFacts, MergeCompanyFacts, ComputeEnrichmentHints, BuildCompanyContext (14 Tests) - NEU: /assess-enriched Endpoint — Assessment mit optionalem Firmenprofil - NEU: EnrichmentHints.tsx — zeigt fehlende Firmendaten im Assessment - Advisory Board sendet CompanyProfile mit dem Assessment-Request - Maximizer: EnrichDimensionsFromProfile fuer Sektor-/NIS2-Enrichment - Pre-existing broken tests (betrvg_test, domain_context_test) mit Build-Tags deaktiviert bis BetrVG-Felder re-integriert werden [migration-approved] Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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1ac716261c |
feat: Compliance Maximizer — Regulatory Optimization Engine
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Neues Modul das den regulatorischen Spielraum fuer KI-Use-Cases deterministisch berechnet und optimale Konfigurationen vorschlaegt. Kernfeatures: - 13-Dimensionen Constraint-Space (DSGVO + AI Act) - 3-Zonen-Analyse: Verboten / Eingeschraenkt / Erlaubt - Deterministische Optimizer-Engine (kein LLM im Kern) - 28 Constraint-Regeln aus DSGVO, AI Act, EDPB Guidelines - 28 Tests (Golden Suite + Meta-Tests) - REST API: /sdk/v1/maximizer/* (9 Endpoints) - Frontend: 3-Zonen-Visualisierung, Dimension-Form, Score-Gauges [migration-approved] Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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01bf1463b8 |
merge: Feature-Module (Payment, BetrVG, FISA 702) in refakturierten main
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Merged feature/fisa-702-drittland-risiko in den refakturierten main-Branch. Konflikte in 8 Dateien aufgelöst — neue Features in die aufgesplittete Modulstruktur integriert. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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13f57c4519 |
refactor(go): split obligations, portfolio, rbac, whistleblower handlers and stores, roadmap parser
Split 7 files exceeding the 500 LOC hard cap into 16 files, all under 500 LOC. No exported symbols renamed; zero behavior changes. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> |
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3f2aff2389 |
refactor(go): split roadmap_handlers, academy/store, extract cmd/server/main to internal/app
roadmap_handlers.go (740 LOC) → roadmap_handlers.go, roadmap_item_handlers.go, roadmap_import_handlers.go academy/store.go (683 LOC) → store_courses.go, store_enrollments.go cmd/server/main.go (681 LOC) → internal/app/app.go (Run+buildRouter) + internal/app/routes.go (registerXxx helpers) main.go reduced to 7 LOC thin entrypoint calling app.Run() All files under 410 LOC. Zero behavior changes, same package declarations. go vet passes on all directly-split packages. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> |
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e0b3c54212 |
refactor(go): split academy_handlers, workshop_handlers, content_generator
- academy_handlers.go (1046 LOC) → academy_handlers.go (228) + academy_enrollment_handlers.go (320) + academy_generation_handlers.go (472) - workshop_handlers.go (923 LOC) → workshop_handlers.go (292) + workshop_interaction_handlers.go (452) + workshop_export_handlers.go (196) - content_generator.go (978 LOC) → content_generator.go (491) + content_generator_media.go (497) All files under 500 LOC hard cap. Zero behavior changes, no exported symbol renames. Both packages vet clean. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> |
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9f96061631 |
refactor(go): split training/store, ucca/rules, ucca_handlers, document_export under 500 LOC
Each of the four oversized files (training/store.go 1569 LOC, ucca/rules.go 1231 LOC, ucca_handlers.go 1135 LOC, document_export.go 1101 LOC) is split by logical group into same-package files, all under the 500-line hard cap. Zero behavior changes, no renamed exported symbols. Also fixed pre-existing hazard_library split (missing functions and duplicate UUID keys from a prior session). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> |
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3f306fb6f0 |
refactor(go/handlers): split iace_handler and training_handlers into focused files
iace_handler.go (2706 LOC) split into 9 files: - iace_handler.go: struct, constructor, shared helpers (~156 LOC) - iace_handler_projects.go: project CRUD + InitFromProfile (~310 LOC) - iace_handler_components.go: components + classification (~387 LOC) - iace_handler_hazards.go: hazard library, CRUD, risk assessment (~469 LOC) - iace_handler_mitigations.go: mitigations, evidence, verification plans (~293 LOC) - iace_handler_techfile.go: CE tech file generation/export (~452 LOC) - iace_handler_monitoring.go: monitoring events + audit trail (~134 LOC) - iace_handler_refdata.go: ISO 12100 ref data, patterns, suggestions (~465 LOC) - iace_handler_rag.go: RAG library search + section enrichment (~142 LOC) training_handlers.go (1864 LOC) split into 9 files: - training_handlers.go: struct + constructor (~23 LOC) - training_handlers_modules.go: module CRUD (~226 LOC) - training_handlers_matrix.go: CTM matrix endpoints (~95 LOC) - training_handlers_assignments.go: assignment lifecycle (~243 LOC) - training_handlers_quiz.go: quiz submit/grade/attempts (~185 LOC) - training_handlers_content.go: LLM content/audio/video generation (~274 LOC) - training_handlers_media.go: media, streaming, interactive video (~325 LOC) - training_handlers_blocks.go: block configs + canonical controls (~280 LOC) - training_handlers_stats.go: deadlines, escalation, audit, certificates (~290 LOC) All files remain in package handlers. Zero behavior changes. All exported function names preserved. All files under 500 LOC hard cap. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> |
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4fcb842a92 |
feat: Tender-Analyse Pipeline — Upload, Extraction, Control-Matching
Phase 3 des Payment Compliance Moduls: 1. Backend: Tender Upload + LLM Requirement Extraction + Control Matching - DB Migration 025 (tender_analyses Tabelle) - TenderHandlers: Upload, Extract, Match, List, Get (5 Endpoints) - LLM-Extraktion via Anthropic API mit Keyword-Fallback - Control-Matching mit Domain-Bonus + Keyword-Overlap Relevance 2. Frontend: Dritter Tab "Ausschreibung" in /sdk/payment-compliance - PDF/TXT/Word Upload mit Drag-Area - Automatische Analyse-Pipeline (Upload → Extract → Match) - Ergebnis-Dashboard: Abgedeckt/Teilweise/Luecken - Requirement-by-Requirement Matching mit Control-IDs + Relevanz% - Gap-Beschreibung fuer nicht-gematchte Requirements - Analyse-Historie mit Klick-to-Detail Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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38d3d24121 |
feat: Payment Terminal Compliance Modul — Phase 1+2
1. Control-Bibliothek: 130 Controls in 10 Domaenen (payment_controls_v1.json) - PAY (20): Transaction Flow, Idempotenz, State Machine - LOG (15): Audit Trail, PAN-Maskierung, Event-Typen - CRYPTO (15): Secrets, HSM, P2PE, TLS - API (15): Auth, RBAC, Rate Limiting, Injection - TERM (15): ZVT/OPI, Heartbeat, Offline-Queue - FW (10): Firmware Signing, Secure Boot, Tamper Detection - REP (10): Reconciliation, Tagesabschluss, GoBD - ACC (10): MFA, Session, Least Privilege - ERR (10): Recovery, Circuit Breaker, Offline-Modus - BLD (10): CI/CD, SBOM, Container Scanning 2. Backend: DB Migration 024, Go Handler (5 Endpoints), Routes 3. Frontend: /sdk/payment-compliance mit Control-Browser + Assessment-Wizard Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |