ankn
a5f8fb49ab
Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp,
Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit,
Projekt-KIQ-HP, teamlandkarte-mcp
Dhive: Jury-Voting
Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation)
Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more
Shared/references: symphony (read-only)
Bahn repos remain available as independent remotes - this monorepo
pulls them in via subtree, the originals are untouched.
313 lines
12 KiB
Python
313 lines
12 KiB
Python
"""
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Extracts IT-relevant text passages from FuBen PDFs (IWF 2-8) using PyMuPDF.
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Outputs a markdown table with OE, IT-relevant passage, and assessment.
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Version 2: Better sentence extraction and more focused passages.
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"""
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import fitz # PyMuPDF
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import os
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import re
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from pathlib import Path
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# Paths
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DATA_DIR = Path(r"c:\Users\AndreKnie\OneDrive - Deutsche Bahn\Coden\Orchestrator\Analyse-O2C-C2S\data\FuBen")
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OUTPUT_FILE = Path(r"c:\Users\AndreKnie\OneDrive - Deutsche Bahn\Coden\Orchestrator\Analyse-O2C-C2S\analysis\2026-06-18-fuben-it-analyse.md")
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# Keywords to search for (case-insensitive)
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KEYWORDS = [
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r'\bIT[-\s]',
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r'\bIT\b',
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r'\bSoftware\b',
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r'\bAnwendung(?:en)?\b',
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r'\bSystem(?:e|en|s)?\b',
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r'\bDigital(?:e|er|es|en|isierung)?\b',
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r'\bTool(?:s)?\b',
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r'\bPlattform(?:en)?\b',
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r'\bPortal(?:e|s)?\b',
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r'\bDatenbank(?:en)?\b',
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r'\bAutomatisier(?:ung|t|en)?\b',
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r'\bDaten(?:management|pflege|qualität|bereitstellung|verarbeitung|modell)?\b',
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r'\bSchnittstelle(?:n)?\b',
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r'\bSAP\b',
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r'\bERP\b',
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r'\bApplikation(?:en)?\b',
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r'\belektronisch(?:e|er|es|en)?\b',
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]
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# Compile combined pattern
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KEYWORD_PATTERN = re.compile('|'.join(KEYWORDS), re.IGNORECASE)
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# IWF number extraction from filename
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IWF_PATTERN = re.compile(r'V\.IWF\s+(\d+)')
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def get_iwf_number(filename: str) -> str:
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"""Extract IWF number from filename."""
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match = IWF_PATTERN.search(filename)
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if match:
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return match.group(1)
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return ""
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def is_relevant_iwf(iwf_num: str) -> bool:
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"""Check if IWF is in range 2-8 (including sub-units like 21, 221, etc.)."""
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if not iwf_num:
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return False
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first_digit = iwf_num[0]
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return first_digit in '2345678'
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def extract_text_from_pdf(pdf_path: Path) -> str:
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"""Extract full text from a PDF using PyMuPDF."""
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text = ""
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try:
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doc = fitz.open(str(pdf_path))
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for page in doc:
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text += page.get_text()
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doc.close()
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except Exception as e:
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print(f"Error reading {pdf_path.name}: {e}")
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return text
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def split_into_items(text: str) -> list[str]:
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"""Split FuBen text into individual task items (■ bullet points and sentences)."""
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items = []
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# Split by ■ bullet points (common in FuBen) - these are the primary structure
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if '■' in text:
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parts = text.split('■')
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for part in parts:
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cleaned = part.strip()
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cleaned = re.sub(r'\s+', ' ', cleaned)
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if len(cleaned) > 15:
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items.append(cleaned)
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else:
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# Fallback: split by newlines and combine short lines
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lines = text.split('\n')
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current = ""
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for line in lines:
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line = line.strip()
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if not line or len(line) < 3:
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if current and len(current) > 15:
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items.append(re.sub(r'\s+', ' ', current))
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current = ""
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continue
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if current:
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current += " " + line
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else:
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current = line
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if current and len(current) > 15:
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items.append(re.sub(r'\s+', ' ', current))
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return items
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def find_it_passages(text: str) -> list[str]:
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"""Find sentences/bullet points containing IT-relevant keywords."""
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passages = []
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seen = set()
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items = split_into_items(text)
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for item in items:
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if KEYWORD_PATTERN.search(item):
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# Skip header/metadata lines
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if item.startswith('Funktionsbeschreibung V.IWF'):
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continue
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if 'Seite' in item and 'von insgesamt' in item:
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continue
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if item.startswith('ETO2024'):
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continue
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# Truncate to a reasonable length showing the relevant part
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if len(item) > 300:
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# Find where the keyword is and show context around it
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match = KEYWORD_PATTERN.search(item)
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if match:
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start = max(0, match.start() - 80)
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end = min(len(item), match.end() + 220)
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item = ("..." if start > 0 else "") + item[start:end] + ("..." if end < len(item) else "")
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if item not in seen:
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seen.add(item)
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passages.append(item)
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return passages
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def assess_passage(passage: str) -> str:
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"""Assess whether a passage describes a task that should move to IT department."""
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passage_lower = passage.lower()
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# Strong indicators for IT responsibility
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it_strong = [
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'software', 'anwendung', 'programmier', 'datenbank', 'portal',
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'plattform', 'digital', 'it-system', 'it-lösung', 'it-infrastruktur',
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'it-anforder', 'it-anwend', 'it-unterstütz',
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'schnittstelle', 'automatisier', 'tool', 'applikation',
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'datenmanagement', 'datenpflege', 'datenbereitstellung',
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'datenverarbeitung', 'datenmodell', 'datenqualität',
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'elektronisch', 'server', 'cloud', 'api',
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'webseite', 'website', 'netzwerk',
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'berechtig', 'zugriff', 'login', 'passwort',
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'it-sicherheit', 'cybersecurity', 'datenschutz',
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'konfiguration', 'implementierung', 'migration',
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'sap', 'erp', 'crm', 'testing', 'abnahme',
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'eingabeunterstützung', 'nutzerführung',
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]
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# Indicators that suggest operational/business ownership (not pure IT)
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business_indicators = [
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'fachlich', 'prozesssteuerung', 'steuerung der',
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'strateg', 'führung', 'koordin',
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'qualitätssicherung', 'anforderungsmanagement',
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]
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strong_count = sum(1 for term in it_strong if term in passage_lower)
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business_count = sum(1 for term in business_indicators if term in passage_lower)
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if strong_count >= 2:
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return "✅ Ja – klare IT-Aufgabe"
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elif strong_count >= 1 and business_count == 0:
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return "✅ Ja – IT-Aufgabe"
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elif strong_count >= 1 and business_count >= 1:
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return "⚠️ Teilweise – IT-Unterstützung nötig, fachliche Verantwortung bleibt"
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elif 'system' in passage_lower and business_count == 0:
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return "🔍 Prüfen – möglicherweise IT-relevant (Systembezug)"
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elif 'system' in passage_lower and business_count >= 1:
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return "⚠️ Teilweise – Systemnutzung mit fachlichem Kontext"
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else:
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return "🔍 Prüfen – Kontext unklar"
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def get_oe_label(iwf_num: str, text: str) -> str:
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"""Extract OE label from PDF text."""
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# Look for the title line after "Funktionsbeschreibung V.IWF XX"
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pattern = re.compile(rf'Funktionsbeschreibung\s+V\.IWF\s+{re.escape(iwf_num)}\s*\n(.+?)(?:\n|Zielsetzung)', re.DOTALL)
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match = pattern.search(text)
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if match:
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name = match.group(1).strip()
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name = re.sub(r'\s+', ' ', name)
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if len(name) > 5 and len(name) < 100:
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return f"IWF {iwf_num} – {name}"
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# Fallback: look for title after IWF number
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lines = text.split('\n')
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for i, line in enumerate(lines):
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if f'V.IWF {iwf_num}' in line and i + 1 < len(lines):
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next_line = lines[i + 1].strip()
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if next_line and len(next_line) > 3 and not next_line.startswith(('Seite', 'ETO', 'Vorstand')):
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return f"IWF {iwf_num} – {next_line}"
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return f"IWF {iwf_num}"
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def main():
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results = []
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# Process all PDFs
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pdf_files = sorted(DATA_DIR.glob("*.pdf"))
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for pdf_path in pdf_files:
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iwf_num = get_iwf_number(pdf_path.name)
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if not is_relevant_iwf(iwf_num):
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continue
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print(f"Processing: {pdf_path.name} (IWF {iwf_num})")
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# Extract text
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text = extract_text_from_pdf(pdf_path)
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if not text:
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print(f" No text extracted from {pdf_path.name}")
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continue
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# Get OE label
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oe_label = get_oe_label(iwf_num, text)
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# Find IT-relevant passages
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passages = find_it_passages(text)
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if passages:
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print(f" Found {len(passages)} IT-relevant passages")
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for passage in passages:
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assessment = assess_passage(passage)
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# Escape pipe characters for markdown table
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clean_passage = passage.replace('|', '\\|').replace('\n', ' ')
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results.append((oe_label, clean_passage, assessment))
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else:
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print(f" No IT-relevant passages found")
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# Sort results by OE
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results.sort(key=lambda x: x[0])
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# Write output
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OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
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with open(OUTPUT_FILE, 'w', encoding='utf-8') as f:
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f.write("# IT-Relevanz-Analyse der Funktionsbeschreibungen (FuBen)\n\n")
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f.write("**Datum:** 2026-06-18\n\n")
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f.write("**Scope:** IWF 2, 3, 4, 5, 6, 7, 8 und ihre Untereinheiten\n\n")
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f.write("**Ausgeschlossen:** IWF 1 (Strategie/Steuerung) und IWF 9 (Prozesse und IT)\n\n")
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f.write("**Suchbegriffe:** IT, Software, Anwendungen, Systeme, Digital, Tools, Plattform, Portal, Datenbank, Automatisierung, Datenmanagement, Schnittstelle, SAP, ERP, Applikation, elektronisch\n\n")
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f.write("## Legende Bewertung\n\n")
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f.write("| Symbol | Bedeutung |\n")
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f.write("|---|---|\n")
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f.write("| ✅ Ja | Aufgabe sollte in die IT-Abteilung wandern |\n")
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f.write("| ⚠️ Teilweise | IT-Unterstützung nötig, fachliche Verantwortung bleibt in der OE |\n")
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f.write("| 🔍 Prüfen | Kontext unklar, weitere Analyse nötig |\n\n")
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f.write("---\n\n")
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f.write("## Ergebnisse\n\n")
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f.write("| OE | IT-relevante Textstelle | Bewertung |\n")
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f.write("|---|---|---|\n")
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for oe, passage, assessment in results:
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f.write(f"| {oe} | {passage} | {assessment} |\n")
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# Summary section
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f.write(f"\n\n---\n\n")
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f.write("## Zusammenfassung\n\n")
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# Count by assessment type
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ja_count = sum(1 for _, _, a in results if '✅' in a)
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teilweise_count = sum(1 for _, _, a in results if '⚠️' in a)
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prüfen_count = sum(1 for _, _, a in results if '🔍' in a)
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f.write(f"| Kategorie | Anzahl |\n")
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f.write(f"|---|---|\n")
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f.write(f"| ✅ Klare IT-Aufgaben | {ja_count} |\n")
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f.write(f"| ⚠️ Teilweise IT-relevant | {teilweise_count} |\n")
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f.write(f"| 🔍 Zu prüfen | {prüfen_count} |\n")
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f.write(f"| **Gesamt** | **{len(results)}** |\n\n")
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# Count by OE
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oe_counts = {}
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for oe, _, _ in results:
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oe_counts[oe] = oe_counts.get(oe, 0) + 1
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f.write(f"### Verteilung nach Organisationseinheit\n\n")
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f.write(f"| OE | Anzahl IT-Stellen |\n")
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f.write(f"|---|---|\n")
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for oe, count in sorted(oe_counts.items()):
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f.write(f"| {oe} | {count} |\n")
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f.write(f"\n\n---\n\n")
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f.write("## Fazit\n\n")
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f.write("Die Analyse zeigt, dass IT-bezogene Aufgaben über die gesamte Organisation verteilt sind. ")
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f.write("Besonders in den Bereichen Kapazitätsmanagement (IWF 2), Netzfahrplan (IWF 3), ")
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f.write("Kapazitätssteuerung (IWF 5) und Produkt-/Preismanagement (IWF 8) finden sich ")
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f.write("Aufgaben mit starkem IT-Bezug, die potenziell in eine zentrale IT-Abteilung ")
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f.write("wandern könnten.\n\n")
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f.write("**Empfehlung:** Die mit ✅ markierten Aufgaben sollten prioritär geprüft werden, ")
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f.write("ob sie in IWF 9 (Prozesse und IT) zentralisiert werden können.\n")
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print(f"\nDone! Output written to: {OUTPUT_FILE}")
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print(f"Total IT-relevant passages found: {len(results)}")
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print(f" ✅ Klare IT-Aufgaben: {ja_count}")
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print(f" ⚠️ Teilweise: {teilweise_count}")
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print(f" 🔍 Zu prüfen: {prüfen_count}")
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if __name__ == "__main__":
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main()
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