Only userId 1105261382487539712 and 1116631120763437056 can see the
scheduling tab. Other users see only assets + mileage modules.
- Add userId to frontend AuthState.user type
- App.tsx conditionally includes scheduling module based on user ID
- Backend already returns userId in auth exchange response
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Remove type badge, reason section — too verbose
- Two clean white cards connected by arrow (swap diagram)
- Result section: predicted mileage, target, conclusion badge
- Tighter spacing, no redundant labels
- Professional tone, no childish wording
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
For rescue_hopeless (换走) scenario, completely rethought candidate logic:
Before: showed biggest-gap candidates (0 mileage) → pointless, customer can't
drive them to target
After: prioritize candidates where customer's remaining driving can push them
over the target line (canQualifyAfterSwap), sorted by smallest gap first
Example: customer drives 178km/day × 57 days = ~1万km remaining.
- 粤AGR6869 (缺口 1990km) → 换后 3.8万, 可达标 ✅ (shown first)
- 浙FF58720 (缺口 6万km) → 换后 1万, 远不达标 (no longer shown first)
Also updated reason text to explain the math:
"该客户剩余57天还能跑约1万km，足以帮缺口小的车冲线"
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
For rescue_hopeless (换走) scenario, filter out inventory candidates
where totalMileage/yearTarget >= 80%. These are already near target
and swapping them in adds no value.
Instead, prioritize candidates with biggest mileage gaps — they benefit
most from accumulating any mileage, even at a low-mileage customer.
Before: showed 粤AGR6869 (93% done, 缺口 1990) as "可达标" — pointless
After: shows 浙FF58720 (0% done, 缺口 60000) — genuinely needs mileage
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Full-page skeleton on initial load: card placeholders + list row placeholders
- List skeleton on refresh: 6 rows with pulse animation
- Skeleton blocks match actual layout (color bar, plate, badges, info line)
- Uses Tailwind animate-pulse for smooth loading effect
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Customer name in list items: truncate with max-w-[40%]
- Daily km and completion rate: flex-shrink-0 to stay on same line
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add department and manager fields to backend types and suggestions API
- Add department/manager to advanced filter panel
- Refine card colors: orange (换下) / blue (换走) / dark slate (全部)
- Selected card uses solid bg color, inactive uses gradient
- Batch pills use dark slate, confirm button uses dark slate
- Background changed to #F0F4F8 for subtle cool tone
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Filter icon in list header with active count badge
- Expandable filter panel: plate search, region select, vehicle type select, customer select
- FilterSelect component with search for long option lists
- Active filter tags shown as removable pills below header
- Temp/confirmed filter pattern (edit → confirm/cancel)
- Result count displayed when filters active
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Modal header: unified dark slate-800 with directional icon (↓ rescue, ↑ release)
- Modal click-outside to close
- Candidate metrics: table-style with bg-slate-50 + dividers, more scannable
- Send button: dark slate instead of blue (avoids color overload)
- Reason section: warm amber accent
- Consistent font sizing and spacing throughout
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Cards filter suggestions by type (已达标/无望达标/全部)
- Toggle: click active card again to reset to all
- Default: white bg + gray border; active: colored bg + ring
- Batch selector: dark pills instead of blue
- Refresh button moved into list header
- Reset type filter when switching batch
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add currentYearMileage to SchedulingVehicleInfo (backend + frontend)
- Compute completionRate as currentYearMileage/yearTarget (year-based)
instead of using overall completion_rate from DB
- Display "本年已跑" instead of "累计" in detail modal
- Fix reason text to show year completion rate
Before: 累计 4.6万, 考核 3.0万, 完成率 12.1% (mismatched periods)
After: 本年已跑 8.3万, 考核 3.0万, 完成率 275% (consistent year-based)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Summary cards: white bg + color border, remove icons, more compact
- SuggestionList: replace badge stacking with compact 2-line layout,
use color bars for priority, fix completion rate format (0.16 → 16.4%)
- SuggestionDetail: bottom-sheet on mobile, compact inline metrics
instead of grid cards, reduce vertical space per candidate
- Follows ui-ux-pro-max Data-Dense Dashboard guidelines
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Filter enriched vehicles to only include rent_status = '租赁' or '自营'.
Inventory candidates already filtered by truck_rent_status = 0 (在库).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
411 of 451 assessment vehicles had is_deleted=1 in tab_truck, causing type
classification to fall back to "其他" and miss all inventory matches. Fix:
- Remove is_deleted=0 filter from truck type query (assessment vehicles need type info regardless)
- Add inferTypeFromTargetName() fallback deriving type from target name when truck record is missing
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- classifyVehicleType now parses dic_type.dic_name (e.g. "4.5吨冷链车") instead of raw model code
- Remove overly strict completionRate >= 0.8 filter for hopeless candidates
- Use vehicle's yearTarget as fallback when inventory has no assessment target
- Filter out suggestions with no candidates (not actionable)
- estimatedGain counts rescue_hopeless suggestions as potential gains
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
AssetsModule 等引用 src/components/Blur 但该文件未纳入版本库导致 CI
rollup 报 Could not resolve "../../components/Blur"。
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
