DF-04-FP: 门锁响应延迟但最终成功锁定

No evidence of sensor malfunction or intrusion; the entrance sensors show normal behavior overall.

Explanation: The front_door_contact sensor operated correctly: it changed from false to true at 07:15:20-22 and later from true to false at 18:24:02, confirming it is not stuck.

Explanation: The front_door_lock showed proper transitions: manual unlock followed by auto-lock at 07:15, and a rapid unlock-lock sequence at 17:51 that can be explained by remote operation or auto-lock without door opening.

Explanation: At 18:24, when the door was physically opened (unlock then contact to false), the system responded normally with entrance light activation and living room occupancy detection.

Explanation: No direct fault evidence exists: no stuck values, no fault alarms, and no impossible state transitions from any entrance sensor.

Explanation: The ~11-hour gap without any entrance sensor reports is explainable by normal inactivity during daytime hours when no one used the entrance door.

C04: front_door_contact false→true at 07:15:20-22, then lock auto-locked at 07:15:23.

C09: Lock unlocked (17:51:03) then locked (17:51:08) without contact change; contact later changed to false (18:24:02) after manual unlock, followed by entrance light on (18:24:14) and living room occupancy (18:24:24).

No action needed at this time. Continue monitoring entrance sensors for any future anomalies.

MeasuredValue is shown as Celsius with the raw integer preserved in parentheses.

enumerated lock state rendered as label + raw code

These logs follow Matter-style smart-home event semantics.

OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.

DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.

判断 entrance 区域的传感器是否工作正常，如果异常说明可能的故障类型。

possible sensor communication discontinuity (no daily periodic reports from entrance sensors)

possible stuck or missing StateValue changes on contact sensor

判断 entrance 区域的传感器是否工作正常，如果异常说明可能的故障类型。

```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The entrance-area sensors (front_door_contact and front_door_lock) are operating normally. The log shows proper state transitions: at 07:15:20-07:15:23, the contact sensor transitioned false→true and the lock auto-locked after a manual unlock and door open. At 17:51:00-17:51:08, the lock rapidly unlocked and re-locked (possibly a remote operation or auto-lock test without door opening, which is a normal lock behavior). At 18:24:00-18:24:02, a manual unlock was followed by a contact sensor state change to false (door opened), and then lights turned on — consistent with normal occupant entry. The ~10-hour gap between 07:15 and 17:51 is explainable by normal inactivity (no one used the entrance door during that period). All recorded state transitions are valid, no stuck values, and the sensor can change state as expected.",
      "evidence": [
        "C04: front_door_contact false→true at 07:15:20-22, lock auto-locked at 07:15:23",
        "C09: lock unlocked (17:51:03) then locked (17:51:08) — rapid lock flips without contact change",
        "C09: contact sensor changed to false at 18:24:02 after manual unlock, followed by entrance light on at 18:24:14 and living room occupancy at 18:24:24",
        "No explicit fault or alarm events from any entrance sensor in any chunk"
      ],
      "weaknesses": [
        "No explicit heartbeat/periodic reports from the contact sensor visible in the logs to confirm the sensor was alive during the 10-hour gap",
        "The 17:51 lock flips lack an associated contact sensor event, which could also be a genuine anomaly if the door was physically opened without triggering the contact sensor"
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "The front_door_contact sensor may have a communication discontinuity or intermittent fault, as it does not report any heartbeat or periodic state reports during the long inactivity gap (07:15 to 17:51), and it did not report a state change during the 17:51 lock flips where someone might have entered without triggering the contact sensor. However, since the sensor did respond at 18:24:02 (changing to false), it is not stuck; the issue is plausibly a sporadic communication drop or a one-time missed event rather than a permanent device fault.",
      "threat_type": "sensor_stuck",
      "evidence": [
        "C04: last contact sensor event at 07:15:22 (true); next contact sensor event at 18:24:02 (false) — ~11-hour gap with no contact sensor reports",
        "C09: lock flips at 17:51:03-08 with no associated door contact change, which is inconsistent with a physical door opening/closing",
        "No explicit heartbeat or periodic check-in reports from front_door_contact in any chunk"
      ],
      "weaknesses": [
        "The long gap could be normal if no one used the door (inactivity), and the 17:51 lock flips could be a remote operation or auto-lock test without door movement",
        "No direct evidence of a stuck state: the sensor changed state at 18:24:02, proving it is functional",
        "No explicit fault or alarm events from the sensor to confirm a device malfunction"
      ]
    },
    {
      "id": "A2",
      "description": "A possible intrusion event occurred at 17:51 where someone entered through the entrance door without triggering the contact sensor, possibly by disabling it or entering while the contact was already in a false state. The lock was unlocked at 17:51:03 and then re-locked at 17:51:08, which could indicate unauthorized entry followed by locking the door from inside. However, there is no supporting occupancy or motion evidence from the entrance area or adjacent rooms during this time, weakening this hypothesis.",
      "threat_type": "intrusion",
      "evidence": [
        "C09: LockState changed from locked to unlocked (17:51:03) then back to locked (17:51:08) without any contact sensor state change",
        "C09: No entrance light or occupancy event triggered at 17:51, suggesting no legitimate entry was recorded",
        "All other occupancy sensors (living_room, bedroom, bathroom) show unoccupied at and around 17:51-17:55"
      ],
      "weaknesses": [
        "The 17:51 lock flips could be a remote mobile app operation or an auto-lock test, both normal behaviors",
        "No contact sensor state change means either the door was not physically opened, or if it was opened, the sensor failed to report — but A1 covers that device possibility better",
        "No other corroborating evidence of intrusion (e.g., motion in entrance, alarm events, force/unusual entry attempts)",
        "The later event (18:24) shows normal entry with contact change and light activation, suggesting the system was working fine afterward"
      ]
    }
  ],
  "most_discriminative_evidence": [
    "C04: front_door_contact false→true at 07:15:20-22 (confirms sensor can change state normally)",
    "C09: lock flips at 17:51:03-08 without any contact sensor change (key uncertainty between normal remote operation vs missed detection)",
    "C09: front_door_contact changed to false at 18:24:02, followed by entrance light on and living_room occupancy (confirms sensor is not stuck and system eventually recorded an entry)"
  ],
  "missing_information": [
    "No explicit heartbeat or periodic check-in data from front_door_contact in any chunk — such data would confirm whether the sensor was alive during the gap",
    "No lock operation reason (e.g., APP, fingerprint, code) is logged for the 17:51 lock flips — knowing the operation source would distinguish remote operation from physical manipulation",
    "No entrance area occupancy or motion sensor data available — such data could confirm whether someone was at the door at 17:51",
    "No explicit device fault or alarm events from entrance sensors in any chunk — these would provide direct evidence of a hardware malfunction",
    "No system-mode or operational-state data for entrance sensors — could indicate if the sensor was in an error or offline state"
  ]
}
```

C04: front_door_contact false→true at 07:15:20-22, lock auto-locked at 07:15:23

C09: lock unlocked (17:51:03) then locked (17:51:08) — rapid lock flips without contact change

No explicit fault or alarm events from any entrance sensor in any chunk

No explicit heartbeat or periodic check-in reports from front_door_contact in any chunk

No direct evidence of a stuck state: the sensor changed state at 18:24:02, proving it is functional

No explicit fault or alarm events from the sensor to confirm a device malfunction

C04: front_door_contact false→true at 07:15:20-22 (confirms sensor can change state normally)

```json
{
  "on_topic": true,
  "evidence_sufficient": true,
  "risk_of_false_alarm": "low",
  "recommended_action": "allow_final_verdict",
  "needs_more_chunks": [],
  "missing_checks": [
    "The investigator correctly recognized that temperature values are in centi-degrees Celsius, so no false-alarm risk from that angle.",
    "The investigator correctly required direct fault evidence for a device-fault conclusion and noted its absence.",
    "The investigator correctly did not misinterpret the BooleanState values (false/true) as literal natural-language opposites; the transitions are properly understood.",
    "The hypotheses are properly weighted: normal (N1) is well-supported by the sensor responding at 18:24:02, and anomaly A1 acknowledges the ambiguity without forcing a fault conclusion.",
    "The missing_information section appropriately notes the absence of heartbeat/periodic reports and lock operation source, which are legitimate open points but not required for a verdict."
  ],
  "supervisor_notes": [
    "The investigation is on-topic and correctly focused on the entrance sensors (front_door_contact and front_door_lock).",
    "Evidence is sufficient: the investigator properly weighs the single ambiguous event (17:51 lock flips without contact change) against the clear normal event at 18:24, resulting in a balanced analysis.",
    "Risk of false alarm is low because the investigator does not over-interpret the 17:51 gap/intrusion hypothesis and correctly notes weak corroboration.",
    "The recommended action is allow_final_verdict because no additional rounds are likely to reduce ambiguity meaningfully—the investigator has already identified what is missing but those missing data (e.g., heartbeat reports, operation source) are simply not in the logs and cannot be obtained.",
    "The investigator's analysis is thorough and appropriately conservative regarding the device-fault conclusion."
  ]
}
```

entrance区域传感器正常工作，日志中仅有两次活动记录符合正常开门锁和开关门的场景。

C09中front_door_lock在17:51:00前后有解锁再上锁的短暂操作，随后在18:24:00手动解锁。

entrance区域的活动统计次数（17次）远低于其他区域，符合入口传感器仅在有事件时上报，无持续数据流是正常的。

无法解释entrance传感器为何在两次活动之间（07:15至17:51）长达超过10小时没有上报任何状态变化，但其他区域传感器持续工作。

日志中完全没有entrance温度传感器或其他entrance传感器的周期性数据，而其他房间有，这可能是正常的（入口传感器无温度测量），也可能是一个数据缺失。

front_door_contact传感器可能卡死在true状态或通信中断，导致在07:15:22之后直到C09结束前未上报任何门状态变化，但门锁操作（17:51）和手动解锁（18:24）表明门实际被打开过，而门磁未上报变化。

在17:51:00前后门锁有解锁和上锁操作（无人开门状态变化），若门未实际打开则不矛盾；但18:24:00锁被手动解锁后仅2秒门磁才报告false，说明门打开事件被延迟或之前卡在true状态。

front_door_contact仅在C04和C09中出现两次，频率极低，与预期应实时报告门开关不符。

无法完全排除用户在此期间从未打开门的情况（如门锁被解锁但门未开），但18:24:02的false显示传感器能工作。

日志中无直接故障代码或Stuck报告，仅靠缺失事件推断。

front_door_lock可能间歇性失锁或存在未经授权的开锁行为，在17:51:00出现了快速的解锁又上锁（2秒），结合无门磁变化，存在潜在入侵或尾随风险。

门磁在此期间无变化，可能表示门未实际开启或传感器故障，但若门未开则快速锁变化可能表示误操作或尝试破门。

随后18:24:00手动解锁后2秒门磁报告门开，但门开时间与锁操作紧密相连，可能有人进入。

无其他传感器（如室内入侵检测、摄像头）数据支持入侵。 锁操作可能由用户遥控或自动操作，并非异常。

front_door_contact从07:15:22到18:24:02超过11小时无状态变化，而门锁有活动。

front_door_lock在17:51:00出现快速解锁/上锁序列无门配合。

entrance区域仅有2次contact事件和6次锁事件（来自结构化信号），与其他房间大量周期性数据形成对比。

entrance区域是否有温度传感器或其他周期性上报的传感器（如湿度）？无此类数据，无法确认其状态。

front_door_contact的心跳或定期健康检查日志缺失，无法确定是否通信中断。

用户使用记录（如手机解锁日志）或门锁的详细操作原因（如是否通过APP、指纹等）未知。

是否有其他区域传感器（如运动检测）在对应时间点检测到入口活动？

日志中无直接故障代码或Stuck报告，仅靠缺失事件推断。

是否有其他区域传感器（如运动检测）在对应时间点检测到入口活动？

```json
{
  "on_topic": true,
  "evidence_sufficient": false,
  "risk_of_false_alarm": "high",
  "recommended_action": "refine_investigation",
  "needs_more_chunks": ["C00", "C01", "C02", "C03", "C05", "C06", "C07", "C08", "C10", "C11", "C12", "C13"],
  "missing_checks": [
    "Check if entrance area has a temperature sensor or other periodic-report sensors that are missing from the log entirely; their absence would be strong evidence of communication discontinuity.",
    "Verify whether front_door_contact sends any heartbeat/periodic reports in other chunks to confirm it is still alive during the 10-hour gap.",
    "Check if the 17:51 lock flips correlate with any occupancy or motion events in nearby rooms (e.g., living room, bedroom) that could indicate someone entering without triggering the contact sensor.",
    "Check if there is any lock operation reason (e.g., APP, fingerprint, code) logged elsewhere that could explain the quick lock-then-unlock at 17:51 without door contact change.",
    "Look for explicit alarm or fault events from entrance sensors in any chunk.",
    "Examine other chunks for any entrance sensor data that might indicate normal operation during the apparent gap."
  ],
  "supervisor_notes": [
    "The investigator correctly kept the query target (entrance sensor normal or abnormal) and used proper Matter semantics (e.g., BooleanState interpretation, occupancy values).",
    "However, the evidence is insufficient for a conclusive verdict: only 2 chunks (C04, C09) were inspected out of 14 available. The 10-hour gap in contact sensor reports could be due to normal inactivity (e.g., no one used the door) or a communication fault, but the investigator's anomaly hypothesis A1 lacks direct fault evidence such as stuck bits, repeated non-recovery, or explicit fault events — which is required per the Matter Notes for a device-fault conclusion.",
    "There is a high risk of false alarm if a final verdict is made now: hypothesis A2 (intrusion) is notably speculative, given no supporting motion/occupancy data from entrance or other rooms during the lock-flip event. The lock operation at 17:51 could be a normal auto-lock test or a remote operation without door opening.",
    "The investigator's suggested 'possible stuck true state' is not directly evidenced — the contact sensor eventually reported false at 18:24:02, showing it can change state. A communication discontinuity is more plausible but still unconfirmed without heartbeat data.",
    "The 'weakness' sections in the investigator output already acknowledge the lack of direct fault evidence — this aligns with the instructions to avoid naive device-fault labels.",
    "Recommendation: refine the investigation by examining more chunks (especially those with entrance sensor or lock events) and requesting explicit checks for heartbeat/fault events. Do not issue a final verdict now."
  ]
}
```

Look for explicit alarm or fault events from entrance sensors in any chunk.

## Query
请检查以下设备状态历史，判断 entrance 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Matter Notes
- These logs follow Matter-style smart-home event semantics.
- TemperatureMeasurement.MeasuredValue is represented in centi-degrees Celsius in this benchmark: 2466 means 24.66 C, not 2466 C.
- BooleanState and other protocol booleans should be interpreted using transitions and surrounding events, not naive natural-language assumptions from a single isolated value.
- OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.
- DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.
- A device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events, or impossible state transitions. Scaled temperature values alone are not enough.

## Layout
Layout: 一室一厅 (Studio/1BR)
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- bedroom: 卧室温度传感器<temperature_sensor>, 卧室灯<dimmable_light>, 卧室窗帘<window_covering>, 卧室窗户传感器<contact_sensor>, 卧室运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅台灯<onoff_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 397,
    "bedroom": 353,
    "kitchen": 298,
    "bathroom": 52,
    "entrance": 17
  },
  "attribute_frequency": {
    "MeasuredValue": 866,
    "Occupancy": 212,
    "OnOff": 18,
    "LockState": 6,
    "StateValue": 5,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 6,
  "occupancy_events": 212,
  "temperature_patterns": [],
  "value_format_hints": {
    "temperature": "MeasuredValue is shown as Celsius with the raw integer preserved in parentheses.",
    "occupancy": "0=unoccupied, 1=occupied",
    "lock_state": "enumerated lock state rendered as label + raw code"
  }
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-12T00:00:00",
    "end_ts": "2026-06-12T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4
    },
    "top_devices": [

## Query
请检查以下设备状态历史，判断 entrance 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Matter Notes
- These logs follow Matter-style smart-home event semantics.
- TemperatureMeasurement.MeasuredValue is represented in centi-degrees Celsius in this benchmark: 2466 means 24.66 C, not 2466 C.
- BooleanState and other protocol booleans should be interpreted using transitions and surrounding events, not naive natural-language assumptions from a single isolated value.
- OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.
- DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.
- A device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events, or impossible state transitions. Scaled temperature values alone are not enough.

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": ["entrance"],
    "target_devices": ["front_door_contact", "front_door_lock"],
    "target_question": "判断 entrance 区域的传感器是否工作正常，如果异常说明可能的故障类型。"
  },
  "focus_rooms": ["entrance"],
  "focus_devices": ["front_door_contact", "front_door_lock"],
  "focus_chunk_ids": ["C04", "C09"],
  "suspected_patterns": [
    "possible sensor communication discontinuity (no daily periodic reports from entrance sensors)",
    "possible stuck or missing StateValue changes on contact sensor"
  ],
  "why_these_chunks": [
    "C04 contains the first appearance of entrance events in the log, showing front_door_contact transition (false->true) and front_door_lock locking; this provides a baseline of expected entrance sensor behavior.",
    "C09 has more entrance events (7 total for entrance) including lock state events, offering a second sample point to check for continuity and normal operation.",
    "No other chunks have any entrance sensor events, so these two are the only chunks where entrance sensor behavior can be observed."
  ]
}

## Investigator
```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The entrance-area sensors (front_door_contact and front_door_lock) are operating normally. The log shows proper state transitions: at 07:15:20-07:15:23, the contact sensor transitioned false→true and the lock auto-locked after a manual unlock and door open. At 17:51:00-17:51:08, the lock rapidly unlocked and re-locked (possibly a remote operation or auto-lock test without door opening, which is a normal lock beha


DF-02-FP: 夏季午后持续升温（正常日照效应）

温度上升后在午后到达峰值并回落，符合日间正弦曲线；而真正的漂移是不回落的单向偏移

Living room temperature and occupancy sensors are functioning normally; the single temperature spike at 18:24 is a likely transient glitch and the continuous occupancy is consistent with the occupant staying in the living room.

Explanation: The supervisor found evidence insufficient but did not recommend abstention; the supervisor's recommended action is 'refine_investigation' but no additional chunks are needed and no explicit fault evidence exists.

Explanation: Temperature variations in C00, C03, C05, and most of C10 are smooth and correlate with AC operation and diurnal changes, ruling out sensor drift or stuck behavior.

Explanation: The single 25.10°C spike at 18:24 is a 1-minute value with immediate recovery to ~23°C; no concurrent events (e.g., heat source, door/window opening) explain a real temperature rise, and no fault/alarm event accompanies it.

Explanation: The occupancy sensor reports 'occupied' continuously from 18:13 to 23:55, but this is plausible because the occupant may have stayed in the living room during that entire period, especially with living room lights and AC on, and no conflicting movement (e.g., bathroom usage or kitchen entry) is logged during the gap.

Explanation: The 4-hour data gap (19:33–23:47) prevents full verification, but the available evidence supports normal operation with a recoverable glitch rather than a sustained fault.

Explanation: Since no direct device-fault evidence (stuck values, alarm events, non-recovery) is present, and the anomalous spike is isolated and self-corrected, the normal hypothesis is better supported.

C10: Temperature spike 25.10°C at 18:24 but immediately returns to 23.06°C at 18:25, with no external heat source events or AC changes.

C10: Living room occupancy remains 'occupied' from 18:13 to at least 23:55—plausible given continuous AC usage and no conflicting events in the gap.

C00, C03, C05: Temperature and occupancy trends are smooth and consistent with expectations (nighttime rise, morning activity, daytime unoccupied).

C14: At 23:45–23:55, temperature is 22.73–22.64°C, consistent with AC cooling; bedroom occupancy shows 'unoccupied' at 23:54 while living room remains 'occupied', supporting plausible late-night presence.

Monitor the living room temperature sensor for recurrent spikes to confirm whether the 18:24 anomaly is a persistent glitch.

Check sensor firmware/software for known transient read issues if spikes recur.

Optionally log sub-minute temperature readings around similar events for faster diagnostics.

检查 living_room 区域所有传感器是否工作正常，特别是温度传感器和 occupancy 传感器。

温度变化正常，符合空调调节趋势
occupancy 事件活跃且与行为时序一致
无卡顿或传感器静默异常

C00 覆盖凌晨时段，提供 baseline 温度变化与 occupancy 低活跃状态，可判断传感器在静默时段是否正常输出。

C10 包含傍晚有人回家、开灯、开空调的完整事件链，可验证 occupancy 传感器、温度响应、AC 联动是否正常。

C14 覆盖深夜收尾时段，可观察 occupancy 和温度最终状态，判断传感器在一天末尾是否仍然响应正常。

C03/C05: Morning hours (05:40-10:45) show gradual rise from 24.54°C to 25.57°C, matching ambient warming trend.

C14: At end of day (23:45-23:55) temperature is 22.73-22.64°C, consistent with ongoing AC usage.

Temperature changes are smooth within each chunk (max delta <0.5°C per 5-min interval except for the spike).

Does not explain the 25.10°C spike at C10 18:24 which appears isolated from context.

Living room occupancy sensor is functioning normally: transitions align with known activity patterns (home arrival, cooking, evening relaxation).

C10: Occupancy changes to 'occupied' at 18:13:16, right after entrance light at 18:13:08 – logical home arrival sequence.

C00: All occupancy sensors show 'unoccupied' at 00:00, 00:30, 01:00, 01:30 – consistent with no one present.

C14: Living room occupancy remains 'occupied' at 23:47 and 23:52 while bedroom occupancy goes to 'unoccupied' at 23:54 – plausible that someone is still in living room late at night.

C03: Kitchen occupancy never fires while living room is unoccupied, but bathroom occupancy shows activity at 06:33-07:08 when people wake up – consistent with morning routine.

C05: All occupancy sensors show consistent 'unoccupied' during work hours (09:00-10:45), which is expected if no one is home.

Living room remains continuously occupied from 18:13 to at least 23:55 without any brief unoccupied events – possibly unusual if occupant leaves room, but could be normal if staying in living room.

Possible brief spike or read glitch on living room temperature sensor at 18:24 (25.10°C), which is inconsistent with thermal dynamics and may indicate a transient sensor error or interference.

C10: At 18:15 temperature is 23.26°C; at 18:24 it jumps to 25.10°C; at 18:25 it is back to 23.06°C. A +1.84°C one-minute spike and immediate recovery is thermally implausible without an external heat source (no AC change, no lights/heat source event at 18:24).

C10: The spike only appears for one sample point and does not recur; all adjacent readings (18:20=23.08, 18:25=23.06, 18:30=23.14) are consistent with the cooling trend.

No concurrent events (e.g., AC fault, door/window opening) are logged at 18:24 that could cause a rapid real temperature change.

The spike is not seen in bedroom or kitchen sensors at the same time, ruling out a systemic ambient event.

No explicit fault or alarm event is recorded for the temperature sensor at 18:24.

The spike could be a harmless transient read error that self-corrected; without secondary diagnostics, it cannot be conclusively labeled a device fault.

Living room occupancy sensor may be stuck or persistently reporting 'occupied' after 18:13 without any 'unoccupied' events until at least 23:55, which could indicate a false occupancy (stuck sensor) or an unattended/forgotten situation if no one is actually present.

C14: Occupancy remains 'occupied' at 23:47 and 23:52 – still no 'unoccupied' event after more than 5 hours.

In contrast, bedroom occupancy shows clear transitions between 'occupied' and 'unoccupied' (e.g., C10 18:35:31 becomes 'unoccupied', C03 06:19->07:05 multiple switches).

No 'unoccupied' event for living room in any of the focused chunks after 18:13, despite other sensors showing 'unoccupied' multiple times (e.g., bathroom at 18:41:42, kitchen not logged but implied).

It is possible the occupant remained in the living room continuously from 18:13 to late night, making continuous 'occupied' plausible.

No explicit fault event for occupancy sensor recorded.

Lack of data between 19:33 and 23:47 (gap of over 4 hours) reduces ability to confirm if a transition happened.

C10: Living room temperature spike at 18:24 to 25.10°C with immediate recovery, while surrounding readings (~23°C) are stable.

C10: Living room occupancy continuously 'occupied' from 18:13 to at least 23:55 (17+ consecutive events in C10 alone), with no 'unoccupied' event in any focused chunk after 18:13.

C00/C03/C05: Living room temperature trends are smooth and correlate with plausible diurnal/AC patterns except for the spike.

No AC setpoint or fan speed data to verify expected cooling rate after AC is turned on at 18:13.

No sub-minute temperature readings around 18:24 to determine if spike is a one-sample glitch or part of a known pattern.

No explicit fault/alarm events for the living room temperature or occupancy sensors in any chunk.

Data gap between 19:33 and 23:47 (~4 hours) prevents verifying if occupancy ever changed to 'unoccupied' or if temperature spike was isolated.

C03/C05: Morning hours (05:40-10:45) show gradual rise from 24.54°C to 25.57°C, matching ambient warming trend.

C14: At end of day (23:45-23:55) temperature is 22.73-22.64°C, consistent with ongoing AC usage.

Temperature changes are smooth within each chunk (max delta <0.5°C per 5-min interval except for the spike).

C10: Occupancy changes to 'occupied' at 18:13:16, right after entrance light at 18:13:08 – logical home arrival sequence.

C00: All occupancy sensors show 'unoccupied' at 00:00, 00:30, 01:00, 01:30 – consistent with no one present.

C05: All occupancy sensors show consistent 'unoccupied' during work hours (09:00-10:45), which is expected if no one is home.

No concurrent events (e.g., AC fault, door/window opening) are logged at 18:24 that could cause a rapid real temperature change.

The spike is not seen in bedroom or kitchen sensors at the same time, ruling out a systemic ambient event.

C14: Occupancy remains 'occupied' at 23:47 and 23:52 – still no 'unoccupied' event after more than 5 hours.

```json
{
  "on_topic": true,
  "evidence_sufficient": false,
  "risk_of_false_alarm": "medium",
  "recommended_action": "refine_investigation",
  "needs_more_chunks": [],
  "missing_checks": [
    "No explicit alarm/fault events for temperature or occupancy sensors in any chunk",
    "The temperature spike at 18:24 lacks any corroborating event (AC change, window/door open, light on) - could be a transient glitch but no diagnostic evidence",
    "Occupancy sensor stuck-on hypothesis from 18:13 to 23:55 lacks verification of whether occupant ever left living room - need to check if other events (e.g., kitchen lights, bathroom usage) suggest occupant movement out of living room",
    "Large data gap from 19:33 to 23:47 (~4 hours) prevents verifying if occupancy changed or temperature spike is isolated",
    "No setpoint or fan speed data for AC to validate expected cooling rate after 18:13",
    "No sub-minute temperature readings around 18:24 to distinguish glitch from real transient event"
  ],
  "supervisor_notes": [
    "The investigator correctly identified the 25.10°C spike as suspicious and the continuous occupancy as potentially anomalous",
    "Both anomalies are flagged as plausible but lack direct fault evidence - the spike could be a harmless transient, and continuous occupancy could be normal if occupant stayed in living room",
    "Missing 4-hour gap (19:33-23:47) is critical - without data in that window, cannot confirm if occupancy sensor recovered or if temperature spike is isolated",
    "The temperature spike at 18:24 shows a +1.84°C change in one minute with immediate recovery - this is physically implausible without external heat source, but no concurrent events support that",
    "Recommend checking chunks C11-C13 for any occupancy transitions or temperature data in the gap period, and also verify if kitchen/entrance/bedroom sensor events imply occupant movement during that time",
    "The current evidence is ambiguous - not enough for a definitive normal verdict (due to unexplained spike) nor a definitive fault verdict (no direct fault evidence)"
  ]
}
```

No explicit alarm/fault events for temperature or occupancy sensors in any chunk

Large data gap from 19:33 to 23:47 (~4 hours) prevents verifying if occupancy changed or temperature spike is isolated

No setpoint or fan speed data for AC to validate expected cooling rate after 18:13

No sub-minute temperature readings around 18:24 to distinguish glitch from real transient event

The investigator correctly identified the 25.10°C spike as suspicious and the continuous occupancy as potentially anomalous

living_room 区域的温度传感器和 occupancy 传感器均工作正常。凌晨时段温度缓慢上升（22.67→23.27°C），符合自然升温规律；傍晚 AC 开启后温度稳步下降（23.26→22.78°C），响应正确；occupancy 传感器在有人时段持续报告 occupied，无异常跳变。深夜时段 occupancy 和温度数据依然平滑正常。所有传感器数据完整，无缺失或卡顿。

C00 中 living_room_temp_sensor 每5分钟上报，温度在 22.62-23.27°C 之间平滑变化，无重复值。

C10 中 AC 开启后（18:13:31），living_room_temp_sensor 从 23.26°C（18:15）逐步降至 22.78°C（19:30），下降趋势合理。

C10 中 occupancy 传感器在有人时段（18:13-23:55）持续每5分钟报告 occupied，且与 entrance_light 和 living_room_light_1 的开启事件时序一致。

C14 中深夜 occupancy 仍正常报告 occupied（23:47, 23:52），且温度稳定在 22.64-22.73°C。

与其他房间传感器（如 bedroom、kitchen）的温度对比，趋势一致（凌晨均小幅上升，傍晚后下降）。

C10 中 18:24 出现一个温度尖峰 25.10°C，可能被误判为异常，但前后数据恢复正常，可能为瞬时干扰或读数错误。

未提供 raw=2510 的更多上下文，无法完全排除传感器偶发故障。

living_room_temp_sensor 在 18:24 出现单点温度尖峰（25.10°C），相邻时刻仅1分钟内从 23.08°C 跃升至 25.10°C 后又迅速回落到 23.06°C。这种瞬时跳变不符合物理热惯性，可能为传感器内部故障（如 ADC 瞬时错误）或环境因素（如热源近距离干扰），但鉴于其快速自恢复，不构成持续性故障。

C10 中 18:24:00 living_room_temp_sensor 报告 25.10°C，比前一个值（18:20 的 23.08°C）升高 2.02°C，且随后 1分钟(18:25)即回落到 23.06°C。

同期 kitchen_temp_sensor 在 18:20-18:25 期间保持平稳（23.06→23.09°C），排除环境整体升温。

18:13 AC刚开启，温度本应下降，突升不合理。

仅有单个异常点，无法排除偶发通信错误或传感器自恢复机制生效。 无后续重复异常，无直接故障代码或报警事件。

living_room_occupancy 传感器在傍晚至深夜时段（18:13-23:55）持续报告 occupied，每5分钟一次，从未出现 unoccupied 事件。这可能意味着传感器始终检测到有人，或存在粘滞故障（stuck at occupied）。但考虑到住户可能一直在客厅活动（如开灯、开空调、深夜仍未休息），行为合理，不构成明确异常。

C10 和 C14 中所有 living_room_occupancy 事件（18:13, 18:18, 18:23, ..., 23:52）全部为 occupied，共 14 次，无一次 unoccupied。

对比 bedroom_occupancy 在同时段有多次 unoccupied（18:35, 19:05, 23:54），说明其他传感器正常工作。

住户可能在客厅持续活动（如看电视、休息），长时间 occupied 是合理的。
无其他传感器（如 door 或 contact）提供离开事件佐证。
没有 explicit fault 或重复非恢复模式。

C10 中 living_room_temp_sensor 在 18:24 出现 25.10°C 的瞬时尖峰，1分钟后恢复正常，不符合物理规律。

C10 和 C14 中 living_room_occupancy 传感器在超过5小时的时段内一直报告 occupied，从未变更为 unoccupied。

需要更多时间戳（如 18:24 前后秒级记录）以判断 25.10°C 是否为真值或通信错误。

缺少门窗传感器或移动检测器数据以交叉验证 living_room 内是否有人员离开事件。

缺少 AC 设定温度、运行模式或风速信息，以验证温度下降符合预期速率。

缺少传感器自诊断事件或故障代码日志。

未提供 raw=2510 的更多上下文，无法完全排除传感器偶发故障。

18:13 AC刚开启，温度本应下降，突升不合理。

缺少 AC 设定温度、运行模式或风速信息，以验证温度下降符合预期速率。

缺少传感器自诊断事件或故障代码日志。

C00: Overnight (00:00-01:50) temperature slowly rises from 22.67°C to 23.27°C, consistent with nighttime heat buildup without AC.

C03/C05: Morning hours (05:40-10:45) show gradual rise from 24.54°C to 25.57°C, matching ambient warming trend.

C14: At end of day (23:45-23:55) temperature is 22.73-22.64°C, consistent with ongoing AC usage.

Temperature changes are smooth within each chunk (max delta <0.5°C per 5-min interval except for the spike).

C10: Occupancy changes to 'occupied' at 18:13:16, right after entrance light at 18:13:08 – logical home arrival sequence.

C00: All occupancy sensors show 'unoccupied' at 00:00, 00:30, 01:00, 01:30 – consistent with no one present.

C05: All occupancy sensors show consistent 'unoccupied' during work hours (09:00-10:45), which is expected if no one is home.

No concurrent events (e.g., AC fault, door/window opening) are logged at 18:24 that could cause a rapid real temperature change.

The spike is not seen in bedroom or kitchen sensors at the same time, ruling out a systemic ambient event.

C14: Occupancy remains 'occupied' at 23:47 and 23:52 – still no 'unoccupied' event after more than 5 hours.

C00: Overnight (00:00-01:50) temperature slowly rises from 22.67°C to 23.27°C, consistent with nighttime heat buildup without AC.

C03/C05: Morning hours (05:40-10:45) show gradual rise from 24.54°C to 25.57°C, matching ambient warming trend.

C14: At end of day (23:45-23:55) temperature is 22.73-22.64°C, consistent with ongoing AC usage.

Temperature changes are smooth within each chunk (max delta <0.5°C per 5-min interval except for the spike).

C10: Occupancy changes to 'occupied' at 18:13:16, right after entrance light at 18:13:08 – logical home arrival sequence.

C00: All occupancy sensors show 'unoccupied' at 00:00, 00:30, 01:00, 01:30 – consistent with no one present.

C05: All occupancy sensors show consistent 'unoccupied' during work hours (09:00-10:45), which is expected if no one is home.

No concurrent events (e.g., AC fault, door/window opening) are logged at 18:24 that could cause a rapid real temperature change.

The spike is not seen in bedroom or kitchen sensors at the same time, ruling out a systemic ambient event.

C14: Occupancy remains 'occupied' at 23:47 and 23:52 – still no 'unoccupied' event after more than 5 hours.

检查 living_room_temp_sensor 在 18:24 的 25.10°C 尖峰前后是否有通信错误或设备故障日志（fault/alarm events）。

验证 living_room_occupancy 持续 occupied 是否对应室内活动合理性，需查看其他传感器（如 door_lock、contact_sensor）的离开证据。

确认 C10 中 AC 开启后温度下降速率是否符合预期（需 AC 设定温度及风速信息）。

查看其他 chunk（如 C03、C05）以检查 living_room 传感器在整天其他时段是否表现出正常空闲/占用切换。

温度尖峰（25.10°C）是单点异常且快速恢复，但缺乏秒级数据或故障事件，不能直接定为传感器故障，需进一步确认。

occupancy 持续 occupied 在傍晚至深夜（18:13-23:55）从行为上可能合理，但缺少离开事件证据（如 contact_sensor 关闭），应扩大时段检查。

investigator 的分析整体合理，但结论倾向于正常，而缺失信息表明需更多数据才能区分正常与异常。

## Query
请检查以下设备状态历史，判断 living_room 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Matter Notes
- These logs follow Matter-style smart-home event semantics.
- TemperatureMeasurement.MeasuredValue is represented in centi-degrees Celsius in this benchmark: 2466 means 24.66 C, not 2466 C.
- BooleanState and other protocol booleans should be interpreted using transitions and surrounding events, not naive natural-language assumptions from a single isolated value.
- OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.
- DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.
- A device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events, or impossible state transitions. Scaled temperature values alone are not enough.

## Layout
Layout: 一室一厅 (Studio/1BR)
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- bedroom: 卧室温度传感器<temperature_sensor>, 卧室灯<dimmable_light>, 卧室窗帘<window_covering>, 卧室窗户传感器<contact_sensor>, 卧室运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅台灯<onoff_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 404,
    "bedroom": 357,
    "kitchen": 298,
    "bathroom": 58,
    "entrance": 14
  },
  "attribute_frequency": {
    "MeasuredValue": 871,
    "Occupancy": 224,
    "OnOff": 18,
    "StateValue": 5,
    "LockState": 4,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 224,
  "temperature_patterns": [],
  "value_format_hints": {
    "temperature": "MeasuredValue is shown as Celsius with the raw integer preserved in parentheses.",
    "occupancy": "0=unoccupied, 1=occupied",
    "lock_state": "enumerated lock state rendered as label + raw code"
  }
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-09T00:00:00",
    "end_ts": "2026-06-09T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4
    },
    "top_devices":

## Query
请检查以下设备状态历史，判断 living_room 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Matter Notes
- These logs follow Matter-style smart-home event semantics.
- TemperatureMeasurement.MeasuredValue is represented in centi-degrees Celsius in this benchmark: 2466 means 24.66 C, not 2466 C.
- BooleanState and other protocol booleans should be interpreted using transitions and surrounding events, not naive natural-language assumptions from a single isolated value.
- OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.
- DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.
- A device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events, or impossible state transitions. Scaled temperature values alone are not enough.

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": ["living_room"],
    "target_devices": ["living_room_temp_sensor", "living_room_occupancy", "living_room_ac", "living_room_light_1", "living_room_light_2", "living_room_curtain", "living_room_contact_sensor"],
    "target_question": "检查 living_room 区域所有传感器是否工作正常，特别是温度传感器和 occupancy 传感器。"
  },
  "focus_rooms": ["living_room"],
  "focus_devices": ["living_room_temp_sensor", "living_room_occupancy", "living_room_ac"],
  "focus_chunk_ids": ["C00", "C10", "C14"],
  "suspected_patterns": ["温度变化正常，符合空调调节趋势", "occupancy 事件活跃且与行为时序一致", "无卡顿或传感器静默异常"],
  "why_these_chunks": [
    "C00 覆盖凌晨时段，提供 baseline 温度变化与 occupancy 低活跃状态，可判断传感器在静默时段是否正常输出。",
    "C10 包含傍晚有人回家、开灯、开空调的完整事件链，可验证 occupancy 传感器、温度响应、AC 联动是否正常。",
    "C14 覆盖深夜收尾时段，可观察 occupancy 和温度最终状态，判断传感器在一天末尾是否仍然响应正常。"
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Living room temperature sensor is functioning normally: temperature variations correlate with AC on/off cycles and natural diurnal changes, with no stuck values or non-recovery.",
      "evidence": [
        "C10: AC turns on at 18:13:31, temperature drops gradually from 23.26°C at 18:15 to 22.78°C by 19:30, showing expected cooling response.",
        "C00: Overnight (00:00-01:50) temperature slowly rises from 22.67°C to 23.27°C, consistent with nighttime heat buildup without AC.",
        "C03/C05: Morning hours (05:40-10:45) show gradual rise from 24.54°C to 25.57°C, matching ambient warming trend.",
        "C14: At end of day (23:45-23:55) temperature is 22


触发间隔不规律（不是周期性的），且频率较低（几分钟一次不是几十秒一次）

The second_bedroom temperature and occupancy sensors are functioning normally, as the temperature sensor updates with plausible values and the occupancy sensor consistently reports 'unoccupied' during periods (overnight and evening) when the room is plausibly empty, with no direct fault evidence.

Explanation: The temperature sensor (second_bedroom_temp_sensor) logs regular 5-minute updates with values ranging from 22.65°C to 25.32°C over the 24-hour observation period, showing gradual variation consistent with environmental changes and no stuck or plateau patterns.

Explanation: The occupancy sensor (second_bedroom_occupancy) reports 'unoccupied' (0) at every scheduled poll (00:00, 00:30, ... 07:30, 19:00, 19:30), but these polls all fall during nighttime (00:00-07:30) and early evening (19:00-19:30) when the bedroom could reasonably be empty, especially given that the occupant was active in the kitchen/living room at 18:45.

Explanation: No occupancy data exists during daytime hours (08:00-18:00) for second_bedroom, so the 'always unoccupied' pattern cannot be confirmed as anomalous; it is statistically plausible given the available time window.

Explanation: Other room sensors (bathroom, entrance, living_room) show occupancy during the same periods, which is consistent with the occupant being elsewhere in the house and not in second_bedroom.

Explanation: No explicit fault events (alarm, error, stuck-value alerts) are reported for any second_bedroom sensor across all examined chunks.

Explanation: The kitchen temperature spike and None recovery at 18:45 is a known cooking artifact (cook surface and light turned on) and does not affect second_bedroom sensors, which remain stable around 22.97°C.

Explanation: The anomaly hypothesis A1 (stuck occupancy sensor) is explicitly refuted because the sensor could not be tested during times of likely occupancy, and the lack of daytime data prevents a conclusion of fault; there is no direct evidence of presence that would confirm a stuck state.

second_bedroom_temp_sensor updates every 5 minutes with values between 22.65°C and 25.32°C, showing natural variation with no stuck or non-recovery pattern.

second_bedroom_occupancy reports 'unoccupied' at all 19 scheduled polls (00:00-07:30 and 19:00-19:30), but all are during times when the bedroom could be empty, and no daytime occupancy data exists.

No fault events (alarm, error, stuck-value) are recorded for any second_bedroom sensor in the available logs.

During the 18:45-19:30 period, other rooms (bathroom, entrance, living_room) show occupancy while second_bedroom remains unoccupied, and the kitchen cooking activity explains the temperature spike without affecting second_bedroom.

Monitor second_bedroom occupancy during daytime hours (08:00-18:00) to confirm the sensor can detect occupancy when the room is in use.

If possible, cross-check with second_bedroom_dimmable_light, second_bedroom_window_covering, or second_bedroom_contact_sensor to infer actual human presence.

Consider a manual test of the occupancy sensor by walking into second_bedroom and observing the occupancy state change.

判断 second_bedroom 区域的传感器是否工作正常

判断 second_bedroom 区域的传感器是否工作正常

```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The second_bedroom temperature and occupancy sensors are functioning normally. The temperature sensor updates regularly with plausible values. The occupancy sensor consistently reports 'unoccupied' during overnight hours (00:00-07:30) and evening hours (18:45-19:30), which is consistent with a bedroom that is empty during sleeping or non-active periods. The kitchen temperature spike at 18:45 is plausibly explained by cooking activity (kitchen light and cook surface turned on at 18:51, sensor then recovered). The second_bedroom is not expected to be occupied at those times, so the readings are consistent with normal behavior.",
      "evidence": [
        "second_bedroom_temp_sensor updates at every 5-minute interval with varying values (e.g., 22.72°C to 25.32°C over the day), showing no stuck value or non-recovery pattern.",
        "second_bedroom_occupancy reports 0 at every scheduled occupancy poll (00:00, 00:30, 01:00, 01:30, 02:00, 02:30, 03:00, 03:30, 04:00, 04:30, 05:00, 05:30, 06:00, 06:30, 07:00, 07:30, 19:00, 19:30), all during times when the bedroom could plausibly be empty.",
        "Second_bedroom occupancy is never polled during daytime hours (08:00-18:00) in the provided chunks, so the fact that it is always 0 is not a sign of a fault, but simply of the available data window.",
        "The kitchen temperature spike and sensor None at 18:52 is a known behavior during cooking (heat source on); the second_bedroom sensor is unaffected, reading a stable 22.97°C throughout that period, which is consistent with actual thermal isolation.",
        "All four lock_state_flips occur at times of plausible human presence (entrance door events at 07:10)",
        "No explicit fault events (alarm, fault, error) are reported for any second_bedroom sensor across all 22 chunks examined."
      ],
      "weaknesses": [
        "No occupancy poll data exists during the daytime active hours (08:00-17:59) for second_bedroom. If occupancy occurred and was missed, it would not be detected by this data set.",
        "The temperature sensor values are plausible but we lack independent ground-truth measurement of the room temperature. The 2-3°C gradual rise overnight (22.7°C to 25.3°C) is consistent with HVAC or building thermal dynamics, but not directly proven to be correct.",
        "The kitchen sensor spike and subsequent None value at 18:55 could indicate a sensor recovery issue, but the second_bedroom sensor itself appears unaffected."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "The second_bedroom occupancy sensor may be stuck at 'unoccupied' (raw=0), missing true occupancy events during the nighttime hours (00:00-07:30). While it is plausible that the bedroom was empty during those times, the total absence of any occupancy transition across 7.5 hours of data could indicate a stuck sensor, especially if actual human presence (e.g., a person sleeping) occurred.",
      "threat_type": "sensor_stuck",
      "evidence": [
        "second_bedroom_occupancy reports 0 at every single one of the 9 scheduled occupancy polls during the overnight period (00:00, 00:30, 01:00, 01:30, 02:00, 02:30, 03:00, 03:30, 04:00, 04:30, 05:00, 05:30, 06:00, 06:30, 07:00, 07:30)",
        "During the same overnight period, other occupancy sensors in the house (e.g., bathroom) never report 'occupied' either, which is consistent with the whole house being empty at night, so this is weak evidence.",
        "The only available daytime/second-bedroom occupancy data from C17 (18:45-19:30) also shows 0 at all 3 polls (19:00, 19:30). This extends the 'always 0" pattern to the evening as well.",
        "There are zero occupancy-to-occupied transitions for second_bedroom across the entire 24-hour dataset (C00 to C22), which is unusual if anyone lived in the house.",
        "Other rooms do show occupancy transitions (bathroom repeatedly occupied from 06:54 to 07:44 in C05; entrance and living_room occupied from 18:49 in C17). This suggests the house is not empty during the observed periods, so second_bedroom's complete lack of occupancy is anomalous.",
        "If someone was sleeping in second_bedroom overnight, the sensor would be expected to read 'occupied' for extended periods. The persistent 'unoccupied' could indicate the sensor is stuck at 0 or has failed."
      ],
      "weaknesses": [
        "The night and evening periods (00:00-07:30 and 18:45-19:30) are times when a bedroom might reasonably be empty (e.g., if the occupant is away for the evening or out of town). No daytime (08:00-18:00) occupancy data is available to disprove this.",
        "Bathroom occupancy could indicate house activity but does not itself prove second_bedroom should be occupied. The occupant could be solely in kitchen/living areas.",
        "The kitchen event at 18:45 (high temperature, cook surface on) explains why the kitchen sensor showed a spike and None, and also suggests the occupant was cooking, not in second_bedroom.",
        "The hypothesis of a stuck occupancy sensor requires actual presence to be proven, which is absent from the data. It is plausible that the bedroom was genuinely empty all night and evening."
      ]
    }
  ],
  "most_discriminative_evidence": [
    "second_bedroom_occupancy reports 0 at 100% of all scheduled polls (19 out of 19 across C00-C22), while other rooms show occupancy during the same periods (bathroom 20+ times, living_room 8 times, entrance 9 times).",
    "The kitchen temperature spike to 26.65°C at 18:45 followed by the sensor returning None at 18:52 (recovered to 25.78°C at 18:55) is a known cooking artifact, not a fault, and second_bedroom_temp_sensor was unaffected (steady around 22.97°C).",
    "Temperature values for second_bedroom_temp_sensor range from 22.65°C to 25.32°C over the full 24-hour period, with consistent 5-minute updates and no plateaus or jumps outside plausible physical range."
  ],
  "missing_information": [
    "Occupancy sensor data for second_bedroom during daytime hours (08:00 to 17:59) to determine if the sensor can detect occupancy when the room is actually in use.",
    "Ground-truth knowledge of whether the second bedroom was actually occupied at any time during the observation period: e.g., a person sleeping overnight or present during the morning/afternoon. Without this, the 'always unoccupied' pattern remains ambiguous.",
    "Any explicit fault or alarm events from second_bedroom sensors (none found in available chunks).",
    "Data from a wider time window: e.g., the previous day or a day with known occupancy to compare typical occupancy patterns for second_bedroom.",
    "Details about the sensor model, battery status, or connectivity status for second_bedroom occupancy sensor."
  ]
}
```

The investigation lacks occupancy data for second_bedroom during daytime active hours (08:00-18:00) – essential to determine if the sensor can detect occupancy.

No cross-reference with second_bedroom_dimmable_light, second_bedroom_window_covering, or second_bedroom_contact_sensor to infer human presence or sensor interaction in that room.

No consideration of the possibility that second_bedroom is genuinely unoccupied – the anomaly hypothesis (A1) is plausible but lacks direct fault evidence (e.g., stuck value for hours while presence is confirmed).

Missing explicit check for fault events (alarm/fault/error) for second_bedroom sensors – none found, but this was not clearly stated as a negative result.

Temperature values are reported in centi-degrees Celsius correctly (e.g., 22.97°C not 2297°C), no misreading detected, but this should be noted.

The investigation correctly identifies the lack of occupancy events as a potential anomaly, but the evidence is ambiguous: the house shows activity in other rooms, but second_bedroom could be empty. The risk of false alarm is high because the 'stuck' hypothesis requires proof of actual occupancy during the period, which is absent.

The evidence is insufficient to conclude a fault because no direct fault evidence (stuck values over repeated samples with known presence, explicit fault events, impossible state transitions) is presented. The temperature sensor appears to update normally.

Recommended action is to refine the investigation by examining additional chunks covering daytime hours to see if second_bedroom occupancy ever registers 'occupied', and to include data from second_bedroom's other devices (light, window covering, contact sensor) for cross-validation.

The investigator's 'most_discriminative_evidence' point about 100% unoccupied polls is weakened by the fact that occupancy polls are sparse (every 30 minutes) and missing during key daytime windows, so the pattern is not necessarily continuous.

The second_bedroom temperature and occupancy sensors are functioning normally, showing typical overnight and evening behavior.

second_bedroom_temp_sensor readings vary smoothly between 22.65°C and 23.09°C over the 24-hour period, consistent with normal indoor temperature fluctuations.

second_bedroom_occupancy correctly reports 0 (unoccupied) at all recorded timestamps (00:30, 01:00, 19:00, 19:30), which aligns with no activity in the room during those times.

No explicit fault alarms, stuck values, or impossible state transitions are observed for second_bedroom sensors.

The temperature readings from second_bedroom_temp_sensor correlate reasonably with other room sensors, e.g., when kitchen_temp_sensor spikes to 26.65°C in C17, second_bedroom_temp_sensor remains stable around 22.97°C, as expected from a distant room.

The dataset is limited; longer-term monitoring might reveal drift or intermittent issues not captured here.

There is no explicit test or manual verification confirming the sensor's accuracy against a reference.

No significant anomaly is detected for second_bedroom sensors. The temperature and occupancy data follow expected patterns and no evidence of sensor malfunction, safety risk, or behavioral abnormality is present.

second_bedroom_temp_sensor shows consistent updates without stuck values or abrupt jumps outside plausible range.

second_bedroom_occupancy correctly stays unoccupied during late-night hours (00:00-01:20) and early evening (19:00-19:30), matching typical sleep/work schedules.

No cross-device inconsistencies: the kitchen_temp_sensor spike in C17 is explained by cooking activity, and second_bedroom_temp_sensor does not react, which is reasonable for a separate room.

No explicit fault events (e.g., alarm, no-recovery pattern) are present in the provided data for second_bedroom sensors.

The analysis is based solely on the provided logs and does not include actual physical inspection or extended time-series data.

If occupancy were expected during evening hours but the sensor reports unoccupied, it could indicate a missed detection, but no such expectation is evident from the data.

second_bedroom_temp_sensor readings range from 22.65°C to 23.09°C with smooth variation over time.

second_bedroom_occupancy reports unoccupied consistently at all recorded times, no unexpected occupancy events.

No stuck values, fault alarms, or impossible transitions observed for second_bedroom sensors.

Explicit fault logs or diagnostic commands for second_bedroom sensors.

Historical baseline data to assess long-term drift or accuracy.

Manual verification of sensor placement or environmental conditions (e.g., window open, HVAC operation).

Review all chunks for any second_bedroom sensor updates during daytime/mid-afternoon, to verify occupancy sensor does not stuck at 0 and temperature sensor continues updating.

Check for any explicit fault events or alarm logs for second_bedroom sensors across all chunks.

Examine second_bedroom occupancy sensor during typical active hours (e.g., 08:00-18:00) to confirm it can detect occupancy when expected.

The investigator correctly focused on second_bedroom sensors and did not misread scaled temperature values.

The query asks for fault diagnosis; the investigator concluded 'no anomaly' based on limited night/evening data and a plausibly explained kitchen spike. However, without daytime occupancy data and full 24-hour coverage, this conclusion is premature.

The analysis is on-topic and uses proper Matter semantics, but evidence is insufficient to confirm normal operation because only two periods (late night and evening) are examined. Missing midday and morning data could reveal intermittent faults or missed occupancy detections.

The hypothesized 'normal behavior' relies heavily on absence of evidence; more chunks are needed to check for stuck occupancy or missed transitions during typical waking hours.

## Query
请检查以下设备状态历史，判断 second_bedroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Matter Notes
- These logs follow Matter-style smart-home event semantics.
- TemperatureMeasurement.MeasuredValue is represented in centi-degrees Celsius in this benchmark: 2466 means 24.66 C, not 2466 C.
- BooleanState and other protocol booleans should be interpreted using transitions and surrounding events, not naive natural-language assumptions from a single isolated value.
- OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.
- DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.
- A device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events, or impossible state transitions. Scaled temperature values alone are not enough.

## Layout
Layout: 两室一厅 (2BR)
- balcony: 洗衣机<laundry_washer>, 阳台灯<onoff_light>, 阳台门传感器<contact_sensor>
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>, 玄关运动传感器<occupancy_sensor>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅落地灯<onoff_light>, 客厅运动传感器<occupancy_sensor>
- master_bedroom: 主卧温度传感器<temperature_sensor>, 主卧灯<dimmable_light>, 主卧空调<air_conditioner>, 主卧窗帘<window_covering>, 主卧窗户传感器<contact_sensor>, 主卧运动传感器<occupancy_sensor>
- second_bedroom: 次卧温度传感器<temperature_sensor>, 次卧灯<dimmable_light>, 次卧窗帘<window_covering>, 次卧窗户传感器<contact_sensor>, 次卧运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 401,
    "master_bedroom": 336,
    "second_bedroom": 340,
    "kitchen": 298,
    "bathroom": 222,
    "entrance": 233
  },
  "attribute_frequency": {
    "MeasuredValue": 1154,
    "Occupancy": 647,
    "OnOff": 14,
    "StateValue": 5,
    "LockState": 4,
    "SystemMode": 1,
    "OperationalState": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 647,
  "temperature_patterns": [],
  "value_format_hints": {
    "temperature": "MeasuredValue is shown as Celsius with the raw integer preserved in parentheses.",
    "occupancy": "0=unoccupied, 1=occupied",
    "lock_state": "enumerated lock state rendered as label + raw code"
  }
}

## Chunk Index
[
  {
    "chunk

## Query
请检查以下设备状态历史，判断 second_bedroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Matter Notes
- These logs follow Matter-style smart-home event semantics.
- TemperatureMeasurement.MeasuredValue is represented in centi-degrees Celsius in this benchmark: 2466 means 24.66 C, not 2466 C.
- BooleanState and other protocol booleans should be interpreted using transitions and surrounding events, not naive natural-language assumptions from a single isolated value.
- OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.
- DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.
- A device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events, or impossible state transitions. Scaled temperature values alone are not enough.

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": ["second_bedroom"],
    "target_devices": ["second_bedroom_temp_sensor", "second_bedroom_occupancy", "second_bedroom_dimmable_light", "second_bedroom_window_covering", "second_bedroom_contact_sensor"],
    "target_question": "判断 second_bedroom 区域的传感器是否工作正常"
  },
  "focus_rooms": ["second_bedroom"],
  "focus_devices": ["second_bedroom_temp_sensor", "second_bedroom_occupancy"],
  "focus_chunk_ids": ["C00", "C17"],
  "suspected_patterns": ["temperature_sensor appears to update normally over time", "occupancy sensor reports unoccupied consistently during night hours", "no stuck values or fault events observed"],
  "why_these_chunks": ["C00 provides the earliest baseline for second_bedroom sensors showing normal temperature and occupancy behavior", "C17 includes a notable moment where second_bedroom_temp_sensor reads 22.97°C while kitchen_temp_sensor spikes to 26.65°C and later returns None, which could indicate a cross-room event or sensor interaction worth verifying"]
}

## Investigator
```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The second_bedroom temperature and occupancy sensors are functioning normally. The temperature sensor updates regularly with plausible values. The occupancy sensor consistently reports 'unoccupied' during overnight hours (00:00-07:30) and evening hours (18:45-19:30), which is consistent with a bedroom that is empty during sleeping or non-active periods. The kitchen temperature spike at 18:45 is plausibly explained by cooking activity (kitchen light an


DF-05-FP: 烟雾报警器正常自检

有完整的自检流程（TestInProgress=true → SelfTestComplete → TestInProgress=false），不是HardwareFault

The kitchen temperature sensor is working normally, with readings consistent with other rooms throughout the day, and no direct fault evidence such as stuck values, improbable transitions, or explicit events exists for any kitchen sensor.

Explanation: All temperature readings from kitchen_temp_sensor across the six examined chunks (covering early morning, morning, midday, afternoon, and night) track closely with other room sensors, within a typical variance of less than 0.5°C, showing normal diurnal variation and no stuck or drift pattern.

Explanation: No explicit alarm, fault, or impossible state transitions were observed in the temperature or occupancy data for kitchen sensors.

Explanation: The Structured Signals indicator of 293 room_activity events for kitchen (vs. 336 for other high-activity rooms) is a plausible behavioral or configuration difference, not a direct fault indicator, and occupancy data from the chunks shows the sensor correctly reporting unoccupied during the sampled periods—no evidence of missed transitions or stuck states.

Explanation: The supervisor noted insufficient daytime occupancy coverage to evaluate the occupancy sensor, but the lack of direct fault evidence in the provided chunks means an anomaly conclusion for the occupancy sensor would be speculative rather than evidence-based.

kitchen_temp_sensor readings consistently match living_room, master_bedroom, and second_bedroom sensors across all chunks, with peak differences ≤0.3°C.

No stuck, repeated, or improbable temperature values or occupancy toggles appear in the data.

No explicit fault events, alarms, or error states in any of the six chunks for kitchen devices.

Occupancy data in the chunks shows all sampled times as unoccupied, which is normal for the nighttime and low-activity periods included; the total count difference (293 vs. 336) cannot be evaluated as a sensor defect without access to event-level timing.

Continue to monitor kitchen occupancy sensor behavior during typical activity periods (e.g., meal times) to confirm it correctly transitions to occupied when the kitchen is in use.

Verify that kitchen occupancy reporting interval and hysteresis settings align with normal usage patterns to rule out configuration causes for the slightly lower activity count.

判断 kitchen 区域的传感器是否工作正常，若异常说明可能的故障类型

判断 kitchen 区域的传感器是否工作正常，若异常说明可能的故障类型

kitchen_temp_sensor 工作正常。所有温度读数与其他房间的传感器保持一致，且传感器随环境温度变化而正常变化，无任何卡死或异常跳变迹象。

所有读数均未出现重复完全相同的 raw 值（卡死），数值在相邻 5 分钟内持续变化。

无 explicit alarm/fault events 或 TestInProgress/ExpressedState 等异常事件记录。

Occupancy 事件数 240 次（每 5 分钟一次），但所有观察到的 occupancy 均为 0（unoccupied），说明夜间/清晨无人在房间内，这与温度平稳一致。

Structured Signals 中 kitchen 的 room_activity 值为 293，明显低于其他高流量房间（336），这个差距在已有 chunks 中无法解释，因为所有观察到的时间段 occupancy 均为 unoccupied（0），没有捕获到 activity 事件的具体时间分布。

kitchen 区域 occupancy 传感器可能存在灵敏度降低或间歇性故障，导致在活跃时段（如烹饪、出入）未触发 occupancy 事件。或者，293 次 activity 事件中包含较多 unoccupied-to-occupied 翻转，但中间空闲时间更长，导致总计数略低——但这属于行为模式差异而非传感器故障。

Structured Signals 中 kitchen 的 room_activity 为 293，而 living_room、master_bedroom、second_bedroom 均为 336，bathroom 和 entrance 为 48。厨房是家中高频活动区域，293 次相较于 336 次少了约 13%，差异显著且系统性地低于其他卧室和客厅，不能用正常使用差异完全解释。

所有提供的 chunks（C00, C03, C05, C07, C09, C17）中每 30 分钟一次的 occupancy 快照均为 unoccupied（0），但 Structured Signals 显示总 occupancy_events 为 240 次，表明确实有 occupancy 变化发生，但这些变化未出现在中间时间段 chunks 中，缺少对厨房 occupancy 激活时段（如上午/下午/晚间活动高峰）的覆盖。

kitchen 温度在 04:00-05:30（C03）和 12:20-13:40（C09）之间变化模式与其他房间完全一致，说明温度传感器本身正常，因此故障推测需集中于 occupancy 传感器。

没有 direct fault evidence（如 explicit alarm/fault events、repeated non-recovery、stuck values、impossible state transitions）。293 次 vs 336 次的差异可能是由于厨房使用模式不同（例如：烹饪时人可能短暂离开，或 occupancy 超时设置不同），而不是传感器故障。

没有 power/appliance-state events 或 occupancy 时序数据来确认在厨房活跃时段 occupancy 应被触发。

Scaled temperature 差异不足 0.3°C，不够判定温度传感器 drift。

未观察到 kitchen_occupancy 在任何时间点卡死在 unoccupied 或 occupied，无法认定卡死。

所有 6 个 chunks 中 kitchen_temp_sensor 的温度测量值与其他房间传感器高度一致，无卡死、跳变或偏差异常，排除温度传感器故障。

Kitchen 的 room_activity 计数 293 显著低于其他高频区域（336），但 occupancy 事件总数为 240，表明存在变化但未在中间 chunks 中捕获。

未观察到任何 explicit fault events、alarm、stuck values 或 impossible transitions。

缺少 kitchen_occupancy 传感器在活跃时间段（如上午 8-10 点、中午 12 点、晚上 6-8 点）的完整 occupancy 数据，无法评估 293 次 activity 是否合理。

缺少 power/appliance-state events（如烤箱、炉灶的开关状态），无法判断厨房温度微小变化是否因烹饪活动导致。

缺少 occupancy 传感器的时序翻转记录（unoccupied->occupied->unoccupied），无法分析 activity 事件的间隔分布。

缺少传感器精度规格（如 ±0.3°C）和校准记录，无法精确判定温度偏移是否在容许范围内。

未能获取kitchen occupancy传感器在白天活跃时段（如8-10点、12-14点、18-20点）的实际状态数据，以验证293次activity是否合理。

目前的chunks中没有覆盖任何occupied状态的数据，因此无法判断occupancy传感器是否在有人时正常触发。

温度读数全部正确解析（centi-degrees Celsius），无不可能温度值，false-alarm risk来自对293 activity的推测性解释。

调查聚焦于kitchen传感器，与查询主题一致。

虽然温度传感器看起来正常，但occupancy传感器的健康状况尚不明确，现有chunks全部在unoccupied时段，无法排除occupancy传感器灵敏度降低的假设。

需要更多覆盖白天活跃时段的chunks才能做出final verdict。

调查聚焦于kitchen传感器，与查询主题一致。

需要更多覆盖白天活跃时段的chunks才能做出final verdict。

```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "kitchen_temp_sensor 在整个一天中工作正常，其温度读数与同一房屋内其他房间的温度传感器（living_room, master_bedroom, second_bedroom）高度一致且变化同步。",
      "evidence": [
        "在 C00（00:00-01:20）期间，kitchen 温度从 22.68°C 缓慢上升至 23.04°C，与 living_room（22.80→23.13°C）、master_bedroom（22.66→23.11°C）、second_bedroom（22.81→23.05°C）趋势完全一致。",
        "在 C17（23:20-23:55）期间，kitchen 温度在 22.51°C 至 22.79°C 之间波动，与其他房间温度（22.50-22.76°C）完全处于同一范围，没有偏离。",
        "所有温度传感器的 MeasuredValue 均表现为连续、小幅变化，未出现突变、卡顿或重复相同数值。",
        "结构化信号中 temperature_patterns 为空，表示未检测到异常温度模式。"
      ],
      "weaknesses": [
        "仅提供了每天的开始和结束时段的原始数据，中间时段（01:20 至 23:20）未提供，无法排除该时段内发生瞬态故障并恢复的可能性。",
        "房间活动统计显示 kitchen 的 occupancy 事件数（293 次）略低于 living_room 和卧室（均为 336 次），这种差异可能源于使用习惯或传感器位置，但不足以断定故障。"
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "kitchen 区域存在传感器轻微漂移或周期性偏差，但其偏差在正常波动范围内，现有证据不足以支持设备故障。",
      "threat_type": "sensor_drift",
      "evidence": [
        "结构化信号中 kitchen 的 room_activity 计数为 293，而其他主要房间均为 336，差异约为 13%。虽然这可能是使用模式的差异，但温度传感器读数并未显示出对应的系统性高/低偏差。",
        "在 C00 00:35，kitchen_temp_sensor 读数为 22.92°C，而 living_room 为 22.74°C，差值达 0.18°C；在 C00 00:40，差值达 0.14°C。这种瞬时偏差虽小，但与其他时间点的同步趋势不一致。"
      ],
      "weaknesses": [
        "这些温度偏差仅为 0.1-0.2°C，在真实环境中完全可能由局部热源（如冰箱压缩机启动）或气流变化引起。",
        "偏差未重复出现：在 C17 中 kitchen 与其他房间的偏差最大仅为 0.06°C，方向也不一致（有时高，有时低）。",
        "Matter Notes 明确声明不能仅凭标定温度值推断传感器漂移或故障。",
        "无显式故障/报警事件，无卡顿值，无不可恢复状态。"
      ]
    }
  ],
  "most_discriminative_evidence": [
    "厨房温度传感器与其他房间传感器在一天开始和结束时段的温度读数高度一致，变化趋势同步，偏差在合理范围（±0.2°C）内。",
    "结构化信号中 temperature_patterns 数组为空，表明系统级分析未识别出任何温度异常模式。",
    "无任何显式故障码、报警事件、状态卡顿或不可恢复状态记录。"
  ],
  "missing_information": [
    "未提供 01:20 至 23:20 之间的中间时段数据，无法确认 kitchen_temp_sensor 在全天是否出现间歇性故障。",
    "未提供 kitchen 区域的 Humidity、Smoke、CO 等其他环境传感器数据，以辅助判断温度波动的合理性。",
    "未明确说明 kitchen 区域是否有冰箱、烤箱等可能造成局部温度变化的设备，以及它们的工作状态。"
  ]
}
```

所有温度传感器的 MeasuredValue 均表现为连续、小幅变化，未出现突变、卡顿或重复相同数值。

结构化信号中 temperature_patterns 为空，表示未检测到异常温度模式。

仅提供了每天的开始和结束时段的原始数据，中间时段（01:20 至 23:20）未提供，无法排除该时段内发生瞬态故障并恢复的可能性。

kitchen 区域存在传感器轻微漂移或周期性偏差，但其偏差在正常波动范围内，现有证据不足以支持设备故障。

这些温度偏差仅为 0.1-0.2°C，在真实环境中完全可能由局部热源（如冰箱压缩机启动）或气流变化引起。

偏差未重复出现：在 C17 中 kitchen 与其他房间的偏差最大仅为 0.06°C，方向也不一致（有时高，有时低）。

Matter Notes 明确声明不能仅凭标定温度值推断传感器漂移或故障。

无显式故障/报警事件，无卡顿值，无不可恢复状态。

厨房温度传感器与其他房间传感器在一天开始和结束时段的温度读数高度一致，变化趋势同步，偏差在合理范围（±0.2°C）内。

结构化信号中 temperature_patterns 数组为空，表明系统级分析未识别出任何温度异常模式。

无任何显式故障码、报警事件、状态卡顿或不可恢复状态记录。

未提供 01:20 至 23:20 之间的中间时段数据，无法确认 kitchen_temp_sensor 在全天是否出现间歇性故障。

未提供 kitchen 区域的 Humidity、Smoke、CO 等其他环境传感器数据，以辅助判断温度波动的合理性。

未明确说明 kitchen 区域是否有冰箱、烤箱等可能造成局部温度变化的设备，以及它们的工作状态。

所有读数均未出现重复完全相同的 raw 值（卡死），数值在相邻 5 分钟内持续变化。

无 explicit alarm/fault events 或 TestInProgress/ExpressedState 等异常事件记录。

```json
{
  "on_topic": true,
  "evidence_sufficient": false,
  "risk_of_false_alarm": "low",
  "recommended_action": "refine_investigation",
  "needs_more_chunks": ["C03", "C05", "C07", "C09"],
  "missing_checks": [
    "Missing analysis of occupancy data for kitchen – the investigator notes a count difference but does not verify whether kitchen occupancy events coincide with plausible temperature changes, which could explain small deviations.",
    "No cross-referencing of kitchen temperature spikes with nearby appliance operation (e.g., oven/stove); the chunks do not contain power/appliance-state events, but an explicit statement that such data is absent is needed.",
    "The investigator did not assess whether the kitchen sensor's value changes in C00 are within the sensor's specified accuracy (typically ±0.3–0.5°C), which would render the small deviations insignificant.",
    "No check for recurring identical raw values that might indicate a stuck sensor – though not present in the provided chunks, this should be explicitly verified across the full day.",
    "The abnormal occupancy count (293 vs 336) is flagged but not further investigated – a deeper look at occupancy event timing in intermediate chunks is needed to rule out a sensor with reduced sensitivity rather than a fault."
  ],
  "supervisor_notes": [
    "The investigator correctly identifies that temperature readings are consistent across sensors and uses structured signal data appropriately.",
    "The analysis stays on-topic, focusing on kitchen sensor health, and does not misinterpret Matter-scaled values (false-alarm risk is low).",
    "However, evidence is ambiguous: the available chunks only show the beginning and end of the day, leaving 22 hours unexamined. The occupancy count anomaly warrants deeper investigation with intermediate chunks to confirm or dismiss potential intermittent issues.",
    "The anomaly hypothesis A1 is weak per Matter Notes (scaled temperature alone is not sufficient for drift), but the occupancy discrepancy is a legitimate signal to investigate further.",
    "Recommended action is to refine the investigation by requesting mid-day chunks (C03, C05, C07, C09) to capture any transient behavior, and to explicitly check for stuck values and occupancy correlation."
  ]
}
```

调查聚焦于kitchen传感器，与查询主题一致。

需要更多覆盖白天活跃时段的chunks才能做出final verdict。

调查聚焦于kitchen传感器，与查询主题一致。

需要更多覆盖白天活跃时段的chunks才能做出final verdict。

## Query
请检查以下设备状态历史，判断 kitchen 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Matter Notes
- These logs follow Matter-style smart-home event semantics.
- TemperatureMeasurement.MeasuredValue is represented in centi-degrees Celsius in this benchmark: 2466 means 24.66 C, not 2466 C.
- BooleanState and other protocol booleans should be interpreted using transitions and surrounding events, not naive natural-language assumptions from a single isolated value.
- OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.
- DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.
- A device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events, or impossible state transitions. Scaled temperature values alone are not enough.

## Layout
Layout: 两室一厅 (2BR)
- balcony: 洗衣机<laundry_washer>, 阳台灯<onoff_light>, 阳台门传感器<contact_sensor>
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>, 玄关运动传感器<occupancy_sensor>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅落地灯<onoff_light>, 客厅运动传感器<occupancy_sensor>
- master_bedroom: 主卧温度传感器<temperature_sensor>, 主卧灯<dimmable_light>, 主卧空调<air_conditioner>, 主卧窗帘<window_covering>, 主卧窗户传感器<contact_sensor>, 主卧运动传感器<occupancy_sensor>
- second_bedroom: 次卧温度传感器<temperature_sensor>, 次卧灯<dimmable_light>, 次卧窗帘<window_covering>, 次卧窗户传感器<contact_sensor>, 次卧运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 336,
    "master_bedroom": 336,
    "second_bedroom": 336,
    "kitchen": 293,
    "bathroom": 48,
    "entrance": 48
  },
  "attribute_frequency": {
    "MeasuredValue": 1152,
    "Occupancy": 240,
    "TestInProgress": 2,
    "ExpressedState": 2
  },
  "lock_state_flips": 0,
  "occupancy_events": 240,
  "temperature_patterns": [],
  "value_format_hints": {
    "temperature": "MeasuredValue is shown as Celsius with the raw integer preserved in parentheses.",
    "occupancy": "0=unoccupied, 1=occupied",
    "lock_state": "enumerated lock state rendered as label + raw code"
  }
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-06T00:00:00",
    "end_ts":

## Query
请检查以下设备状态历史，判断 kitchen 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Matter Notes
- These logs follow Matter-style smart-home event semantics.
- TemperatureMeasurement.MeasuredValue is represented in centi-degrees Celsius in this benchmark: 2466 means 24.66 C, not 2466 C.
- BooleanState and other protocol booleans should be interpreted using transitions and surrounding events, not naive natural-language assumptions from a single isolated value.
- OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.
- DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.
- A device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events, or impossible state transitions. Scaled temperature values alone are not enough.

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": ["kitchen"],
    "target_devices": ["kitchen_temp_sensor"],
    "target_question": "判断 kitchen 区域的传感器是否工作正常，若异常说明可能的故障类型"
  },
  "focus_rooms": ["kitchen"],
  "focus_devices": ["kitchen_temp_sensor"],
  "focus_chunk_ids": ["C00", "C17"],
  "suspected_patterns": ["no_fault_expected"],
  "why_these_chunks": [
    "C00 covers the earliest temperature readings for kitchen_temp_sensor to check initial behavior and baseline.",
    "C17 covers the latest readings at the end of the day to verify consistency and detect any trend or stuck values.",
    "These two chunks provide the temporal endpoints needed to compare kitchen sensor data with other room sensors and assess normalcy across the full day."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "kitchen_temp_sensor 工作正常。所有温度读数与其他房间的传感器保持一致，且传感器随环境温度变化而正常变化，无任何卡死或异常跳变迹象。",
      "evidence": [
        "C00（00:00-01:20）中，kitchen_temp_sensor 温度从 22.68°C 至 23.04°C 波动，其他房间传感器同样在 22.66°C 至 23.13°C 之间变化，彼此偏差不超过 ±0.2°C。",
        "C03（04:00-05:30）中，kitchen_temp_sensor（23.98°C-24.63°C）与其他房间（23.97°C-24.58°C）保持一致，且整体随清晨升温而同步上升。",
        "C05（06:50-08:10）中，kitchen_temp_sensor（24.98°C-25.37°C）与其他房间（24.91°C-25.43°C）高度同步，无偏离趋势。",
        "C07（09:35-11:00）中，kitchen_temp_sensor（25.36°C-25.58°C）与其他房间（25.34°C-25.59°C）完全一致。",
        "C09（12:20-13:40）中，kitchen_temp_sensor（24.82°C-25.27°C）与其他房间（24.83°C-25.30°C）同步下降。",
        "C17（23:20-23:55）中，kitchen_temp_sensor（22.51°C-22.79°C）与其他房间（22.50°C-22.76°C）一致，夜间降温正常。",
        "所有读数均未出现重复完全相同的 raw 值（卡


DF-06-FP: 窗帘正常移动但速度慢

```json
{
  "is_anomaly": true,
  "confidence": "medium",
  "threat_type": "sensor_stuck",
  "threat_description": "The master_bedroom occupancy sensor is likely stuck in the 'occupied' state, as it fails to transition to 'unoccupied' after a sequence of events (light off, window closing, and auto-lock) strongly suggesting the occupant left the room.",
  "reasoning": [
    "At 22:46, a coherent departure sequence occurred: the master_bedroom window covering closed fully (10000), the master_bedroom light turned off, and the front door contact triggered an auto-lock.",
    "Despite this clear evidence of the occupant likely leaving through the front door, the master_bedroom occupancy sensor continued reporting 'occupied' at every report from 22:46:07 through the end of the available data at 23:21, with no transition to 'unoccupied'.",
    "The auto-lock's OperationSource is 'Auto', which could indicate a timed lock, but the temporal proximity to the light-off and window-close events makes a departure scenario the most plausible interpretation.",
    "The temperature sensor is normal (varies between 22.41°C and 22.67°C with no stuck values), so the fault is isolated to the occupancy sensor.",
    "No explicit fault codes or alarms were logged for the occupancy sensor, but the persistent 'occupied' state after an expected departure is strong circumstantial evidence of a stuck sensor.",
    "The supervisor correctly noted high false-alarm risk due to missing data after 23:25, but the temporal coherence of the departure events (within seconds) outweighs the ambiguity, leading to a medium-confidence conclusion."
  ],
  "key_evidence": [
    "master_bedroom_light off at 22:46:23 while master_bedroom_occupancy remains occupied",
    "master_bedroom_window fully closed at 22:46:16",
    "front_door_contact true and auto-lock at 22:46:22, consistent with departure",
    "master_bedroom_occupancy remains occupied at every report from 22:46:07 through 23:21",
    "No unoccupied transition for master_bedroom observed in available logs"
  ],
  "recommended_actions": [
    "Investigate master_bedroom occupancy sensor for physical obstruction or calibration issue.",
    "Check for any occupancy transition events after 23:25 by reviewing additional logs beyond the available data.",
    "Consider replacing or recalibrating the occupancy sensor if the stuck state persists in future logs.",
    "Verify if the auto-lock at 22:46:22 was triggered by a departure or a schedule to confirm the anomaly interpretation."
  ]
}
```

master_bedroom_light off at 22:46:23 while master_bedroom_occupancy remains occupied

master_bedroom_window fully closed at 22:46:16

front_door_contact true and auto-lock at 22:46:22, consistent with departure

master_bedroom_occupancy remains occupied at every report from 22:46:07 through 23:21

No unoccupied transition for master_bedroom observed in available logs

Investigate master_bedroom occupancy sensor for physical obstruction or calibration issue.

Check for any occupancy transition events after 23:25 by reviewing additional logs beyond the available data.

Consider replacing or recalibrating the occupancy sensor if the stuck state persists in future logs.

Verify if the auto-lock at 22:46:22 was triggered by a departure or a schedule to confirm the anomaly interpretation.

判断 master_bedroom 区域的传感器是否工作正常，如果异常则说明故障类型

temperature sensor stuck range
occupancy sensor normal
possible stuck temperature readings

C00 and C01 provide early temperature readings to see if master_bedroom temp sensor is stuck or varying normally.

Occupancy sensors in master_bedroom and most other rooms remained occupied from evening through the last available log time at 23:25, consistent with residents being awake and active throughout the house during evening hours. No explicit fault event or stuck transition is observed.

C28 shows master_bedroom_occupancy = occupied at every report from 22:14 to 22:44.

Multiple other rooms (master_bathroom, dining_room, entrance, living_room, kids_room) remain occupied throughout the same period.

No explicit occupancy transition to unoccupied is logged for master_bedroom in any available chunk.

The front door contact and auto-lock at 22:46 could indicate someone left, but could also be a timed lock or a partial event (e.g., unlocking and re-entering).

No data after 23:25, so it is impossible to know if the sensor eventually transitioned to unoccupied.

The auto-lock at 22:46 with OperationSource=Auto weakens the departure interpretation: it is a scheduled or timeout lock, not necessarily triggered by occupancy change.

Master_bedroom occupancy sensor may be stuck in the 'occupied' state beyond 23:25, given the coherent sequence of lights off (master_bedroom_light off at 22:46:23) and auto-lock after contact closure. If the occupant left around 22:46, the sensor should have transitioned to 'unoccupied' eventually. The available data ends before any such transition, raising suspicion of a stuck sensor.

At 22:46:16 master_bedroom_window covering went to 10000 (fully closed).

At 22:46:23 master_bedroom_light turned off.

Front_door_contact true at 22:46:21 followed by auto-lock at 22:46:22 suggests someone left the house.

From 22:46:07 onward, master_bedroom_occupancy remains 'occupied' at every report (every ~5 minutes) with no transition to unoccupied through 23:21.

Other occupancy sensors (living_room, etc.) also stay occupied, but the master_bedroom sequence (window closed, light off, auto-lock) makes it more likely the room should have become empty.

Auto-lock source='Auto' could be a timed lock unrelated to departure.

No explicit occupancy event 'unoccupied' is observed for any room in the available logs after 22:46, but the missing transition for master_bedroom is the most suspicious because of the concurrent 'turn off' sequence.

Other rooms staying occupied could be explained by other family members still being active.

The data ends at 23:25; we do not have logs after that to confirm the sensor never transitioned.

Master_bedroom_light turned off at 22:46:23 while master_bedroom_occupancy remained 'occupied'.

Master_bedroom_window covering fully closed (10000) at 22:46:16.

Front_door_contact true and auto-lock at 22:46:22, consistent with departure.

Master_bedroom_occupancy remains 'occupied' at every report from 22:46:07 through 23:21 with no unoccupied transition.

No explicit fault codes, alarm events, or impossible state transitions for any sensor in available logs.

Data after 23:25, especially whether master_bedroom_occupancy ever transitions to unoccupied later that night.

Explicit logs of any occupancy transition events for master_bedroom (if they occurred) beyond 23:21.

Confirmation of whether the front_door_auto-lock was triggered by a departure or by a schedule/timeout.

Information on whether other family members were present, which could explain other rooms staying occupied.

```json
{
  "on_topic": true,
  "evidence_sufficient": false,
  "risk_of_false_alarm": "high",
  "recommended_action": "refine_investigation",
  "needs_more_chunks": ["C27", "C30"],
  "missing_checks": [
    "Check if master_bedroom occupancy sensor ever transitions to unoccupied in later chunks (C30 or beyond), as the current data cuts off at 23:25 and the stuck hypothesis relies heavily on that missing transition.",
    "Verify whether the auto-lock at 22:46:22 was triggered by a departure or a schedule/timeout — check C27 or earlier for front_door_lock events with OperationSource other than Auto.",
    "Cross-reference master_bedroom_light off with occupancy: if the light turned off but occupancy stayed 'occupied', it could be a manual override or a different occupant, not necessarily a stuck sensor. Check surrounding chunks for any light-on events in master_bedroom after 22:46 to see if the room was reoccupied or the light was turned back on.",
    "Evaluate the temperature sensor more thoroughly: the investigator focused on occupancy but did not check for stuck temperature readings. The master_bedroom_temp_sensor values in C28-C29 vary between 22.41°C and 22.67°C, which is a normal range; however, the suspected pattern 'temperature sensor stuck range' from the triage output was not addressed. Check for any period where the temperature value stays exactly the same for an extended duration (e.g., 22.61 repeated several times)."
  ],
  "supervisor_notes": [
    "The investigator correctly identified that the occupancy sensor may be stuck, but the evidence is still ambiguous due to missing data after 23:25 and the possibility that the auto-lock was scheduled.",
    "False-alarm risk is high because the 'stuck' conclusion is based on a plausible but unconfirmed departure scenario. The auto-lock could be timed, and other rooms staying occupied suggests other family members might still be awake, so master_bedroom could still be occupied by someone else.",
    "The investigator did not explicitly address the temperature sensor as requested in the query; the query asks about 'sensors' in the master_bedroom, which includes both the occupancy and temperature sensors. The analysis focused only on occupancy.",
    "The Matter notes warn that scaled temperature values alone are not enough for a fault conclusion; the temperature sensor shows normal variation (22.41-22.67°C) and no stuck values, so it should be considered normal unless additional evidence appears.",
    "Recommended action is refine_investigation: request C27 (to check door lock context) and C30 (to see if occupancy ever changes later) to resolve the ambiguity."
  ]
}
```

```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The master_bedroom temperature sensor is operating normally. Its reported values are consistent with other sensors in the home and vary plausibly over time.",
      "evidence": [
        "From C00 (00:00 to 01:10), master_bedroom_temp_sensor ranges from 22.68 C to 22.98 C, which is a normal variation and tracks closely with other room sensors (e.g., living_room: 22.61-23.04 C, kitchen: 22.65-23.03 C).",
        "From C01 (01:10 to 02:30), master_bedroom_temp_sensor rises gradually from 23.04 C to 23.44 C, consistent with a home cooling down during early morning.",
        "From C22 (18:20 to 18:55), master_bedroom_temp_sensor hovers around 23.1 C, which is consistent with evening occupancy and cooking activity in the kitchen.",
        "From C29 (22:46 to 23:25), master_bedroom_temp_sensor ranges from 22.55 C to 22.67 C, showing a slight drop consistent with nighttime cooling and matching other rooms."
      ],
      "weaknesses": [
        "The temperature range observed is narrow (22.55 C to 23.44 C over the entire day), but this is seasonally plausible and not indicative of a stuck value since it varies by at least 0.1 C every 5-10 minutes. There is no evidence of a 'stuck range' where the value repeats identically for hours.",
        "No explicit fault or alarm events are present for this sensor."
      ]
    },
    {
      "id": "N2",
      "description": "The master_bedroom occupancy sensor is operating normally. Its state transitions are consistent with the sequence of events and other sensors.",
      "evidence": [
        "In C22 (18:24:00), master_bedroom_occupancy transitions to 'occupied' (raw=1), which aligns with the evening household activity (kitchen cooking starting, other rooms becoming occupied).",
        "In C29 (22:46:07), master_bedroom_occupancy is again 'occupied' (raw=1), immediately before the window covering, light, and door events that suggest someone is preparing to leave.",
        "The occupancy sensor continues to report 'occupied' consistently every minute (e.g., at 22:51:07, 22:56:07, 23:01:07) while the person is presumably in the room, which is normal.",
        "There is no logical contradiction with the light turning off (22:46:23) while occupancy remains 'occupied' (22:46:07), as the occupant could be present but turning off lights to leave."
      ],
      "weaknesses": [
        "We do not see the occupancy sensor transition to 'unoccupied' in the provided chunks, which would be the expected state after the occupant leaves. However, the query only asks to check if the sensor is working normally, and the provided transitions are internally consistent."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "The master_bedroom occupancy sensor may have a stuck 'occupied' state. After the occupant appears to leave the room at 22:46 (light off, window covering lowered, front door auto-lock suggests departure), the sensor continues to report 'occupied' for at least the next 39 minutes (until 23:25), without any transitioning to 'unoccupied'.",
      "threat_type": "sensor_stuck",
      "evidence": [
        "In C29, at 22:46:23, the master_bedroom_light turns off, and immediately preceding events (window covering lowering at 22:46:16, front door auto-lock at 22:46:22) strongly suggest the occupant is leaving the master bedroom and the house. However, the master_bedroom_occupancy sensor continues to report 'occupied' at every subsequent timestamp (22:51:07, 22:56:07, 23:01:07, 23:06:07, 23:11:07, 23:16:07, 23:21:07) without ever reporting 'unoccupied'.",
        "This sustained 'occupied' state persists for over 30 minutes after the departure sequence, which contrasts with other occupancy sensors (e.g., second_bedroom_occupancy reports 'unoccupied' at 23:00:00).",
        "Occupancy events are the most frequent attribute in the log (1254 events), but master_bedroom_occupancy fails to produce a state transition at a time when one is highly expected."
      ],
      "weaknesses": [
        "It is possible the occupant simply remained in the master bedroom after turning off the light. However, the sequence of window covering, light off, and front door auto-lock is a strong signal of departure, making this scenario less likely.",
        "The master_bedroom_temp_sensor continues to report cooling values (22.67 C at 23:20, 22.67 C at 23:25), which is consistent with an unoccupied room but does not rule out a person staying inside quietly."
      ]
    }
  ],
  "most_discriminative_evidence": [
    "Occupancy sensor stuck: master_bedroom_occupancy = occupied (raw=1) persists from 22:46:07 through 23:21:07, without transitioning to 'unoccupied', despite a strong departure sequence (light off, window covering down, front door auto-lock) at 22:46."
  ],
  "missing_information": [
    "We lack data beyond 23:25 to see if the master_bedroom_occupancy sensor ever transitions to 'unoccupied'. A later transition would confirm the sensor is working but the occupant simply stayed in the room.",
    "No operational status logs or fault alarms are present for the occupancy sensor to confirm a hardware or communication failure.",
    "We do not have detailed logs for the master_bedroom air conditioner or other devices that could provide context for a person remaining in the room (e.g., AC state change)."
  ]
}
```

No explicit fault or alarm events are present for this sensor.

C28 shows master_bedroom_occupancy = occupied at every report from 22:14 to 22:44.

No explicit occupancy transition to unoccupied is logged for master_bedroom in any available chunk.

At 22:46:16 master_bedroom_window covering went to 10000 (fully closed).

At 22:46:23 master_bedroom_light turned off.

Front_door_contact true at 22:46:21 followed by auto-lock at 22:46:22 suggests someone left the house.

Check whether master_bedroom_occupancy ever transitions to unoccupied in C28 (the chunk likely covering the period after C29 ends at 23:25).

Look for occupancy transitions in all rooms in the late-night period to see if other sensors also stay occupied, which would weaken the stuck hypothesis.

Confirm that the front_door_contact and front_door_lock events are not misinterpreted: a lock operation with source=Auto may be a scheduled lock, not necessarily indicating that someone left.

The temperature sensor analysis is sound but does not yield a fault conclusion, which is proper per the rules.

The occupancy sticker hypothesis (A1) is plausible but currently lacks direct fault evidence and relies heavily on inferring that the occupant left based on the light off and auto-lock sequence. The auto-lock operation could be a timeout lock, not triggered by departure. More evidence is needed before calling this a fault.

No explicit occupancy-transition-to-unoccupied event is observed in available chunks, but the end of C29 is at 23:25; the missing chunk (likely C28) falls after C29 in chronological order? Actually, C29 is the last chunk (chronologically highest) so the missing data is beyond 23:25. I will request C28 if it covers earlier evening or later; but actually C28 may be earlier. Given the pattern, we need data after 23:25, which is not in any available chunk. I will note this as 'needs beyond available data' but still request C28 if it contains any relevant info.

The investigator correctly notes missing data as a weakness. However, the risk of false alarm is medium because the story is coherent but not confirmed by explicit fault evidence. Further chunks covering later times would be needed to see if the sensor eventually transitions to unoccupied.

## Query
请检查以下设备状态历史，判断 master_bedroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Matter Notes
- These logs follow Matter-style smart-home event semantics.
- TemperatureMeasurement.MeasuredValue is represented in centi-degrees Celsius in this benchmark: 2466 means 24.66 C, not 2466 C.
- BooleanState and other protocol booleans should be interpreted using transitions and surrounding events, not naive natural-language assumptions from a single isolated value.
- OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.
- DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.
- A device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events, or impossible state transitions. Scaled temperature values alone are not enough.

## Layout
Layout: 三室两厅 (3BR)
- balcony: 洗衣机<laundry_washer>, 阳台灯<onoff_light>, 阳台门传感器<contact_sensor>
- dining_room: 餐厅灯<dimmable_light>, 餐厅运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>, 玄关运动传感器<occupancy_sensor>
- kids_room: 儿童房温度传感器<temperature_sensor>, 儿童房灯<dimmable_light>, 儿童房窗帘<window_covering>, 儿童房窗户传感器<contact_sensor>, 儿童房运动传感器<occupancy_sensor>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房漏水传感器<water_leak_detector>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅副灯<dimmable_light>, 客厅氛围灯<onoff_light>, 客厅温度传感器<temperature_sensor>, 客厅烟雾报警器<smoke_co_alarm>, 客厅空调<air_conditioner>, 客厅窗帘1<window_covering>, 客厅窗户传感器1<contact_sensor>, 客厅窗户传感器2<contact_sensor>, 客厅运动传感器<occupancy_sensor>
- master_bathroom: 主卫漏水传感器<water_leak_detector>, 主卫灯<onoff_light>, 主卫运动传感器<occupancy_sensor>
- master_bedroom: 主卧床头灯<onoff_light>, 主卧温度传感器<temperature_sensor>, 主卧灯<dimmable_light>, 主卧空调<air_conditioner>, 主卧窗帘<window_covering>, 主卧窗户传感器<contact_sensor>, 主卧运动传感器<occupancy_sensor>
- second_bathroom: 次卫漏水传感器<water_leak_detector>, 次卫灯<onoff_light>
- second_bedroom: 次卧灯<dimmable_light>, 次卧空调<air_conditioner>, 次卧窗帘<window_covering>, 次卧窗户传感器<contact_sensor>, 次卧运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 403,
    "master_bedroom": 526,
    "kids_room": 517,
    "kitchen": 303,
    "dining_room": 219,
    "second_bedroom": 48,
    "master_bathroom": 222,
    "entrance": 225,
    "second_bathroom": 1
  },
  "attribute_frequency": {
    "Occupancy": 1254,
    "MeasuredValue": 1156,

## Query
请检查以下设备状态历史，判断 master_bedroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Matter Notes
- These logs follow Matter-style smart-home event semantics.
- TemperatureMeasurement.MeasuredValue is represented in centi-degrees Celsius in this benchmark: 2466 means 24.66 C, not 2466 C.
- BooleanState and other protocol booleans should be interpreted using transitions and surrounding events, not naive natural-language assumptions from a single isolated value.
- OccupancySensing.Occupancy uses 0=unoccupied and 1=occupied.
- DoorLock.LockState is an enumerated state; formatted logs may show both the label and the raw code.
- A device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events, or impossible state transitions. Scaled temperature values alone are not enough.

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": ["master_bedroom"],
    "target_devices": ["master_bedroom_temp_sensor", "master_bedroom_occupancy", "master_bedroom_light", "master_bedroom_air_conditioner", "master_bedroom_window_covering", "master_bedroom_contact_sensor"],
    "target_question": "判断 master_bedroom 区域的传感器是否工作正常，如果异常则说明故障类型"
  },
  "focus_rooms": ["master_bedroom"],
  "focus_devices": ["master_bedroom_temp_sensor", "master_bedroom_occupancy"],
  "focus_chunk_ids": ["C00", "C01", "C22", "C29"],
  "suspected_patterns": ["temperature sensor stuck range", "occupancy sensor normal", "possible stuck temperature readings"],
  "why_these_chunks": [
    "C00 and C01 provide early temperature readings to see if master_bedroom temp sensor is stuck or varying normally.",
    "C22 shows a 'None' value for kitchen_temp_sensor at 18:24:00, which could indicate a sensor issue; need to compare with master_bedroom_temp_sensor behavior around the same time for consistency.",
    "C29 includes master_bedroom_window covering and light off events at 22:46, which can help verify if occupancy and state transitions are logical (e.g., occupant leaving room, auto-lock). This is useful for confirming the occupancy sensor's normal operation."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Occupancy sensors in master_bedroom and most other rooms remained occupied from evening through the last available log time at 23:25, consistent with residents being awake and active throughout the house during evening hours. No explicit