Staff Mapping Management

Staff Mapping Management

Manage business staff-to-institution mapping table, handle conflicts, and track versions.

When to Activate

Use this skill when the user:

• Says "update the mapping table" or "refresh mapping"
• Mentions "staff conflicts", "name conflicts", or "duplicate names"
• Asks "convert mapping Excel to JSON"
• Wants to "check mapping coverage" or "find unmapped staff"
• Needs to "resolve institution assignment conflicts"

Quick Start Workflow

Step 1: Convert Excel → JSON
  ↓
Step 2: Validate & Detect Conflicts
  ↓
Step 3: Update System Mapping
  ↓
Step 4: Verify Coverage

---

Step 1: Convert Mapping Excel to JSON

1.1 Expected Excel Structure

File: 业务员机构团队对照表YYYYMMDD.xlsx

1.2 Conversion Script

import pandas as pd
import json

def convert_mapping_excel_to_json(excel_path, json_path):
    """Convert staff mapping Excel → JSON"""

    # Load Excel
    df = pd.read_excel(excel_path)

    # Validate columns
    required = ['业务员', '三级机构', '四级机构', '团队简称']
    missing = [c for c in required if c not in df.columns]
    if missing:
        raise ValueError(f"Missing columns: {missing}")

    # Build mapping dict
    mapping = {}
    for _, row in df.iterrows():
        staff_key = str(row['业务员'])
        mapping[staff_key] = {
            '三级机构': str(row['三级机构']),
            '四级机构': str(row['四级机构']),
            '团队简称': str(row['团队简称']) if pd.notna(row['团队简称']) else None
        }

    # Save JSON
    with open(json_path, 'w', encoding='utf-8') as f:
        json.dump(mapping, f, ensure_ascii=False, indent=2)

    print(f"✅ Converted {len(mapping)} records")
    return mapping

---

Step 2: Validate & Detect Conflicts

2.1 Conflict Types

2.2 Conflict Detection

def detect_conflicts(mapping):
    """Find name conflicts and data issues"""
    import re

    # Extract names from "工号+姓名" format
    name_to_records = {}
    for staff_key, info in mapping.items():
        match = re.search(r'[\u4e00-\u9fa5]+', staff_key)
        if not match:
            continue

        name = match.group()
        if name not in name_to_records:
            name_to_records[name] = []
        name_to_records[name].append({
            'key': staff_key,
            'institution': info['三级机构'],
            'team': info['团队简称']
        })

    # Find conflicts (same name, different institution)
    conflicts = []
    for name, records in name_to_records.items():
        if len(records) > 1:
            institutions = set(r['institution'] for r in records)
            if len(institutions) > 1:
                conflicts.append({
                    'name': name,
                    'records': records,
                    'type': 'name_conflict'
                })

    return conflicts

2.3 Missing Data Detection

def detect_missing_data(mapping):
    """Find records with missing institution"""
    missing = []

    for staff_key, info in mapping.items():
        if not info.get('三级机构') or info['三级机构'] == 'nan':
            missing.append({
                'key': staff_key,
                'issue': 'missing_institution'
            })

    return missing

---

Step 3: Update System Mapping

3.1 Backup Current Version

from datetime import datetime
import shutil

def backup_mapping(current_path):
    """Backup current mapping before update"""
    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
    backup_path = f'业务员机构团队归属_backup_{timestamp}.json'

    shutil.copy(current_path, backup_path)
    print(f"✅ Backed up to {backup_path}")
    return backup_path

3.2 Apply Update

def update_mapping(new_mapping_path):
    """Update system mapping file"""

    # 1. Backup current
    current_path = '业务员机构团队归属.json'
    backup_mapping(current_path)

    # 2. Load new mapping
    with open(new_mapping_path, 'r', encoding='utf-8') as f:
        new_mapping = json.load(f)

    # 3. Validate
    conflicts = detect_conflicts(new_mapping)
    missing = detect_missing_data(new_mapping)

    # 4. Report issues
    if conflicts:
        print(f"⚠️  Found {len(conflicts)} name conflicts:")
        for c in conflicts[:5]:
            print(f"  - {c['name']}: {len(c['records'])} records")

    if missing:
        print(f"⚠️  Found {len(missing)} records with missing institution")

    # 5. Copy to system location
    shutil.copy(new_mapping_path, current_path)
    print(f"✅ Updated system mapping: {len(new_mapping)} records")

    return {'conflicts': conflicts, 'missing': missing}

---

Step 4: Verify Mapping Coverage

4.1 Check Against Data

def verify_mapping_coverage(data_df, mapping):
    """Check how many staff in data are covered by mapping"""
    import re

    # Build name lookup
    name_to_info = {}
    for staff_key, info in mapping.items():
        match = re.search(r'[\u4e00-\u9fa5]+', staff_key)
        if match:
            name_to_info[match.group()] = info

    # Get staff from data
    data_staff = data_df['业务员'].unique()

    # Check coverage
    unmapped = [s for s in data_staff if s not in name_to_info]
    coverage_rate = 1.0 - (len(unmapped) / len(data_staff))

    report = {
        'total_staff_in_data': len(data_staff),
        'mapped_staff': len(data_staff) - len(unmapped),
        'unmapped_staff': unmapped[:10],  # First 10
        'unmapped_count': len(unmapped),
        'coverage_rate': coverage_rate
    }

    return report

4.2 Coverage Report

def print_coverage_report(report):
    """Print human-readable coverage report"""

    coverage_pct = report['coverage_rate'] * 100

    print(f"\n📊 Mapping Coverage Report")
    print(f"=" * 50)
    print(f"Total staff in data: {report['total_staff_in_data']}")
    print(f"Mapped staff: {report['mapped_staff']}")
    print(f"Unmapped staff: {report['unmapped_count']}")
    print(f"Coverage rate: {coverage_pct:.1f}%")

    if report['unmapped_count'] > 0:
        print(f"\n⚠️  Unmapped staff (first 10):")
        for staff in report['unmapped_staff']:
            print(f"  - {staff}")
        print(f"\n💡 Action: Update mapping table to include these staff")
    else:
        print(f"\n✅ All staff are mapped!")

---

Version Management

Compare Two Mapping Versions

def compare_mapping_versions(old_json, new_json):
    """Compare two mapping file versions"""

    with open(old_json, 'r', encoding='utf-8') as f:
        old_mapping = json.load(f)

    with open(new_json, 'r', encoding='utf-8') as f:
        new_mapping = json.load(f)

    old_keys = set(old_mapping.keys())
    new_keys = set(new_mapping.keys())

    # Find changes
    added = list(new_keys - old_keys)
    removed = list(old_keys - new_keys)
    changed = []

    for key in old_keys & new_keys:
        if old_mapping[key] != new_mapping[key]:
            changed.append({
                'key': key,
                'old': old_mapping[key],
                'new': new_mapping[key]
            })

    return {
        'added': added,
        'removed': removed,
        'changed': changed,
        'unchanged': len(old_keys & new_keys) - len(changed)
    }

---

Common Use Cases

Case 1: "Update mapping from new Excel file"

# Full update workflow
excel_file = '业务员机构团队对照表20251109.xlsx'
json_file = '业务员机构团队归属_new.json'

# Step 1: Convert
mapping = convert_mapping_excel_to_json(excel_file, json_file)

# Step 2: Detect conflicts
conflicts = detect_conflicts(mapping)
missing = detect_missing_data(mapping)

# Step 3: Update (if acceptable)
if len(conflicts) < 5:  # Acceptable threshold
    result = update_mapping(json_file)
else:
    print(f"❌ Too many conflicts ({len(conflicts)}), manual review needed")

Case 2: "Check mapping coverage"

import pandas as pd
import json

# Load data and mapping
df = pd.read_csv('data.csv', encoding='utf-8-sig')
mapping = json.load(open('业务员机构团队归属.json'))

# Check coverage
report = verify_mapping_coverage(df, mapping)
print_coverage_report(report)

Case 3: "Resolve name conflicts"

# Find conflicts
conflicts = detect_conflicts(mapping)

# Manual resolution approach
for conflict in conflicts:
    print(f"\nConflict: {conflict['name']}")
    for i, record in enumerate(conflict['records']):
        print(f"  {i+1}. {record['key']} → {record['institution']}")

    # User selects correct record or marks both as valid
    # System updates mapping accordingly

---

Troubleshooting

"Many unmapped staff after update"

Cause: New mapping table is incomplete

Solution:

1. Check if Excel file has all staff
2. Verify Excel column names match expected
3. Compare with previous version:

   diff = compare_mapping_versions('old.json', 'new.json')
   print(f"Removed: {len(diff['removed'])} staff")
   

"Name conflicts detected"

Options:

1. Accept conflicts: Use keep='last' strategy (keep last record)
2. Add ID to display: Show "工号+姓名" instead of just name
3. Manual resolution: Update Excel to disambiguate

"Conversion fails"

Check:

• File encoding (should be UTF-8 or GB2312)
• Column names (must match exactly)
• File format (.xlsx vs .xls)

---

Related Files

Current mapping: 业务员机构团队归属.json (229 records as of 2025-11-04)

Data processor: backend/​data_processor.py

• Uses _build_name_to_info() method (lines 23-58)
• See get_policy_mapping() (lines 59-101)

Related Skills:

• field-validation - Check mapping coverage rate
• data-cleaning-standards - Use mapping to fill missing institutions

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Skill Version: v1.0 Created: 2025-11-09 File Size: ~290 lines Focuses On: Mapping management only