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

The living room temperature and occupancy sensors are functioning normally; observed temperature spikes were transient environmental anomalies that recovered immediately without indicating hardware failure.

Explanation: 1. Analyzed the temperature logs for `living_room_temp_sensor`. While two transient spikes occurred (25.50°C at 17:24 and 25.10°C at 18:24), both values immediately returned to baseline (~23.0-23.5°C) in the subsequent 5-minute interval.

Explanation: 2. According to Matter protocol standards and Supervisor notes, a device fault requires evidence such as stuck values, repeated non-recovery, or explicit alarm events. Isolated, self-correcting spikes are indicative of environmental factors (e.g., sunlight, local heat sources) rather than sensor malfunction.

Explanation: 3. Verified the `living_room_occupancy` sensor behavior. It correctly transitioned from 'unoccupied' to 'occupied' at 18:13 upon user entry and remained stable during evening activity, showing no logic errors or stuck states.

Explanation: 4. No explicit fault codes, diagnostic errors, or contradictory state transitions were found in the logs for either sensor.

Temperature readings recovered instantly after spikes (e.g., 25.50°C -> 23.48°C within 1 minute).

Occupancy sensor correctly detected entry (unoccupied -> occupied) at 18:13.

Absence of any explicit fault or alarm events in the logs.

No action required; sensors are operating within normal parameters.

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.

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。

Two transient temperature spikes occurred (17:24 at 25.50°C and 18:24 at 25.10°C), but both recovered immediately to baseline in the subsequent 5-minute interval without any stuck values or error codes.

Occupancy transitions are logical: the sensor correctly reports 'unoccupied' during early morning hours and switches to 'occupied' at 18:13 upon user entry, remaining 'occupied' during evening activity.

No explicit fault events, alarm indicators, or diagnostic errors were logged for either the temperature or occupancy sensors.

The supervisor feedback explicitly notes that a single transient spike with immediate recovery does not constitute a device fault per Matter protocol standards.

Does not fully explain the physical cause of the two specific temperature spikes (e.g., direct sunlight, AC vent proximity), though these are likely benign environmental factors rather than hardware faults.

Potential intermittent sensor glitch or environmental interference causing brief inaccurate high-temperature readings.

At 17:24, the living room temp sensor reported 25.50°C, which is significantly higher than the preceding reading (23.41°C) and surrounding sensors (kitchen ~23.4°C, bedroom ~22.4°C).

At 18:24, another spike to 25.10°C was recorded, again deviating from the baseline (~23.0°C) and other room sensors.

Both spikes were isolated events followed by immediate recovery, suggesting a transient issue rather than permanent drift.

Per Matter Notes and Supervisor Feedback, a 'device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events'.

The values recovered instantly, ruling out 'stuck' or 'drift' faults.

No corroborating evidence (e.g., HVAC failure, fire risk) exists to suggest the high readings were real environmental hazards.

Isolated spikes are common in IoT sensors due to thermal inertia or local heat sources and do not meet the threshold for 'malfunction' without repetition or failure to recover.

The absence of explicit fault/alarm events in the logs despite the presence of anomalous-looking temperature spikes.

The immediate recovery of temperature values to baseline after the spikes (e.g., 25.50°C -> 23.48°C in 1 minute), indicating no persistent hardware failure.

The consistency of occupancy sensor behavior (correct transition from unoccupied to occupied) which validates the general health of the living room sensor suite.

Contextual data regarding HVAC system status (e.g., AC cycling) at 17:24 and 18:24 to rule out localized heating effects.

Historical baseline data to determine if these spikes are recurring patterns or one-off events.

Physical inspection logs to verify if the sensor was exposed to direct sunlight or heat sources during those times.

The investigator correctly identified that the temperature spikes (25.50°C and 25.10°C) were transient and immediately recovered to baseline, which does not constitute a device fault per Matter protocol standards.

The occupancy sensor logic was verified as correct (unoccupied -> occupied transition upon entry).

No explicit fault events or stuck values were found for the living room sensors.

The conclusion that the sensors are working normally is supported by the evidence in chunks C00, C09, C10, and C13.

The distinction between environmental anomalies (spikes) and hardware faults (stuck/failed recovery) was handled appropriately.

The living room temperature sensor and occupancy sensor are functioning normally. The temperature readings show stable, gradual changes consistent with a healthy indoor environment, and the occupancy sensor correctly transitions between unoccupied and occupied states in response to user activity.

C00: Temperature readings for `living_room_temp_sensor` fluctuate gently between 22.67 C and 23.27 C over two hours, showing no stuck values or impossible jumps.

C00: Occupancy is consistently 'unoccupied' (raw=0) during the early morning period when no activity is logged.

C10: At 18:13:16, occupancy transitions from unoccupied to 'occupied' (raw=1), coinciding with lights turning on, indicating responsive motion detection.

C10: Occupancy remains 'occupied' throughout the evening activity block (18:13 - 19:35), with periodic updates confirming sustained presence.

C13: Occupancy remains 'occupied' during the late-night block (22:22 - 23:45), with temperature readings remaining stable around 22.5-22.6 C.

Does not account for the single anomalous temperature spike at 18:24 in C10, though it assumes this was an isolated transient event rather than a fault.

The living room temperature sensor experienced a transient malfunction or environmental interference causing a significant, unrealistic temperature spike at 18:24, which immediately corrected itself. This suggests a potential sensor drift or temporary hardware glitch.

C10: At 18:24:00, `living_room_temp_sensor` reports 25.10 C (raw=2510).

C10: Just one minute later at 18:25:00, the reading drops back to 23.06 C (raw=2306), which is consistent with the surrounding baseline (~23.0-23.1 C).

C10: The magnitude of the jump (+2.04 C) and the immediate recovery within 60 seconds is inconsistent with normal thermal inertia in a living room, suggesting a measurement error or transient fault.

The anomaly is isolated; there is no repetition of this behavior in C00 or C13.

The system recovered immediately without manual intervention, which might indicate a benign transient issue (e.g., brief heat source nearby, sensor noise) rather than a persistent device fault.

No explicit alarm or fault code was generated by the device.

The single data point at 18:24:00 in C10 where `living_room_temp_sensor` reads 25.10 C, followed by a return to ~23.06 C at 18:25:00, while all other sensors and the general trend remain stable.

Contextual logs for the living room at 18:24 to determine if a heat source (e.g., AC unit cycling, sunlight, appliance) was active that could explain the spike physically.

Historical calibration data for the sensor to assess if 25.10 C is within its known error margin for a transient event.

Battery level or connectivity status of the sensor at the time of the event to rule out communication artifacts.

Check for explicit fault/alarm events in the device logs (e.g., DiagnosticEvents, FaultIndication) which are required for a definitive 'device-fault' label per protocol notes.

Verify if the temperature spike at 18:24 correlates with any physical environmental changes (e.g., AC cycling, sunlight exposure) that might explain the reading without hardware failure.

Confirm if the sensor has a history of similar transient spikes to distinguish between a one-off glitch and a recurring malfunction.

The investigator correctly identified a suspicious data point (temp spike) but concluded it was likely benign/transient. However, the query asks to judge if sensors are working normally and identify faults if abnormal.

Per Matter Notes, a 'device-fault conclusion requires direct fault evidence such as stuck values, repeated non-recovery, explicit alarm/fault events'. The current evidence shows only a single transient spike with immediate recovery and no explicit fault codes.

Relying on a single isolated spike to claim potential malfunction is speculative and carries a high risk of false alarm. Conversely, declaring it 'normal' without ruling out intermittent hardware issues based solely on recovery is also weak.

The investigation lacks the necessary depth to definitively classify the sensor health. It needs to look for corroborating fault indicators or rule them out more rigorously than just 'it recovered'.

Since no other chunks are strictly needed to see the full picture of this specific event (C00, C10, C13 cover the timeline), the issue is analytical sufficiency regarding fault definition, not missing data chunks. However, 'refine_investigation' is appropriate to force a stricter adherence to the 'direct fault evidence' standard.

## 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"
    ],
    "target_question": "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。"
  },
  "focus_rooms": [
    "living_room"
  ],
  "focus_devices": [
    "living_room_temp_sensor",
    "living_room_occupancy"
  ],
  "focus_chunk_ids": [
    "C00",
    "C10",
    "C13"
  ],
  "suspected_patterns": [
    "sensor_normal_operation",
    "occupancy_transition_logic"
  ],
  "why_these_chunks": [
    "C00: Baseline check for temperature sensor stability and initial occupancy state (unoccupied) in the early morning.",
    "C10: Critical transition chunk showing the change from 'unoccupied' to 'occupied' upon user entry, verifying the motion sensor's responsiveness and logic.",
    "C13: Confirmation of sustained 'occupied' state during evening activity, ensuring no false negatives or stuck states."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The living room temperature sensor and occupancy sensor are functioning normally. The observed data represents standard environmental fluctuations and correct motion detection logic.",
      "evidence": [
        "Temperature readings for the living room sensor remain within a stable, realistic range (approx. 22.4°C - 23.9°C) throughout the day, showing gradual changes consistent with ambient conditions.",
        "Two transient temperature


DF-04-FP: 门锁响应延迟但最终成功锁定

Insufficient evidence to confirm sensor malfunction; sparse logging is consistent with normal low-usage patterns.

Explanation: The investigation focuses on the `front_door_contact` sensor in the entrance area. The primary anomaly hypothesis (A1) suggests intermittent failure due to sparse telemetry and inconsistent timing relative to lock operations.

Explanation: However, the supervisor correctly identified that the 'anomaly' relies on the assumption that the door was used during the gap between C04 (07:15) and C09 (16:45+). There is no direct evidence of door usage (e.g., motion sensors at the entrance, or other triggered devices) during this period to prove the sensor missed an event.

Explanation: In C04, the contact sensor works correctly (false -> true -> lock). In C09, the lock operates at 17:51 without a contact log, but this could be an internal adjustment or key fob use not involving physical door movement sufficient to trigger the magnetic switch, or simply no door opening occurred. At 18:24, the contact sensor does report `false` after the lock unlocks, showing it is functional when triggered.

Explanation: Without explicit fault codes, stuck values, or corroborating evidence of unreported access events, the sparse data is best explained by low physical usage of the front door rather than hardware failure.

Explanation: Therefore, we cannot conclude a device fault. The status remains 'unknown/normal' with low confidence due to the lack of comprehensive coverage for the entire day.

C04 shows valid contact state transitions (false->true) correlated with lock operation, proving the sensor functions when active.

No explicit fault logs or connectivity drops for `front_door_contact` in any chunk.

Absence of corroborating entrance activity (motion/light) during the gap between C04 and C09 makes it impossible to distinguish between 'no use' and 'sensor failure'.

C09 shows the sensor reporting `false` at 18:24, confirming it is not permanently stuck offline.

Monitor the device over a longer period with higher expected traffic to establish a baseline frequency.

Check battery levels of the contact sensor if accessible, as low power can cause sporadic reporting issues.

Verify if the lock operations at 17:51 involved physical door movement by cross-referencing with any available entrance motion sensors.

判断 entrance 区域的传感器（入户门传感器）是否工作正常。如果异常，请说明可能的故障类型。

potential communication drop or sensor failure

C04 shows a valid state transition sequence: `false` -> `true` -> lock operation, indicating the sensor correctly detected the door opening and triggered the associated lock event.

C09 shows another valid sequence: Lock unlock -> Contact `false` -> Light on, consistent with someone entering the house.

No explicit fault codes, connectivity drops, or stuck values are present for the `front_door_contact` device in any chunk.

Other devices (temp sensors, occupancy) report regularly, suggesting the network and gateway are functional; the lack of contact events is likely due to the door simply not being opened.

Relies on the assumption that no door openings occurred between C04 (07:15) and C09 (18:24). If there were entries without contact logs, this hypothesis fails.

Does not account for potential intermittent communication issues if the door was used but logs were dropped silently.

The entrance contact sensor is experiencing intermittent reporting failures or has a hardware fault causing it to miss state changes. While it works occasionally (C04, C09), it fails to report during other potential access events, creating a security blind spot.

Extreme sparsity: Only 2 distinct contact events recorded over ~15 hours, despite high activity in other rooms (living room 397, bedroom 353).

In C09 at 17:51, the lock operates (unlocked/locked) but no corresponding contact sensor event is logged immediately before or after, unlike the clear correlation seen in C04 and the later 18:24 event. This suggests the sensor may have failed to detect the door movement associated with the lock operation.

In C09 at 18:24, the lock unlocks, then the contact sensor reports `false` (door open) 2 seconds later. In C04, the contact reported `true` (open) *before* the lock operated. The inconsistency in timing relative to the lock action suggests unreliable triggering.

The gap between 07:15 (C04) and 16:45+ (C09) contains zero contact data. If the household entered/exited during this time (suggested by general home activity patterns), the sensor missed these events.

It is possible the door was genuinely not touched between 07:15 and 16:45.

The lock operations in C09 might be internal adjustments or key fob uses that don't physically move the door enough to trigger a faulty magnetic switch, though this is speculative.

Lack of explicit error logs makes it hard to distinguish between 'no use' and 'failure to report' definitively.

The absence of any `front_door_contact` events in C09 around 17:51, coinciding with a `front_door_lock` state change (unlock/lock cycle), whereas C04 showed a tight coupling between contact state and lock operation.

The total count of 17 activity events for 'entrance' in structured signals vs only 2 raw contact log entries, implying significant unrecorded activity or very sparse logging behavior.

The inconsistent temporal relationship between lock state changes and contact sensor readings across C04 (contact first) and C09 (contact after lock).

Logs from chunks covering mid-day periods (e.g., 10:00-14:00) to verify if the door was accessed without contact reporting.

Direct diagnostic status or battery level of the `front_door_contact` sensor.

Confirmation of whether the lock operations in C09 (17:51) corresponded to actual physical door movement (e.g., via motion sensors in the entrance area, if available).

Verification of `front_door_contact` activity in the gap between C04 (09:05) and C09 (16:45), specifically around mid-day or afternoon entries which are suggested by general home occupancy patterns but not explicitly logged for the door.

Confirmation that the lock operations in C09 (17:51) did not involve physical door movement that should have triggered the contact sensor, or if the sensor simply failed to report.

Check for any explicit fault codes or connectivity drops for the `front_door_contact` device in chunks C05-C08.

The investigator correctly identifies the sparsity of data as a potential issue but relies heavily on inference ('if the household entered/exited...') rather than direct evidence.

The hypothesis A1 (sensor malfunction) is speculative because it assumes unrecorded events occurred. Without logs from the intervening period (specifically C07, which covers ~12:50-14:48, and potentially parts of C05/C06 where gaps might exist), we cannot confirm if the sensor missed events or if the door was simply not used.

Marking risk as 'high' because concluding a hardware fault based solely on missing logs without corroborating evidence of attempted access (e.g., motion sensors triggering at the entrance while contact remains static/offline) is prone to false alarms.

Need to check C07 and surrounding chunks to see if there were any other entrance-related activities (lights, locks) that should have been accompanied by contact sensor updates.

The entrance contact sensor is functioning normally. The sparse telemetry (only 2 events in the entire log set) indicates that the door remained closed for the vast majority of the monitoring period, and the two recorded transitions (open/close or close/open) occurred during specific access events where the lock was also active.

C04 shows a valid state transition sequence: `front_door_contact` changes from false to true at 07:15:22, immediately followed by a lock operation at 07:15:23. This suggests the door was opened (contact triggered) and then locked/closed.

C09 shows a valid state transition sequence: `front_door_lock` unlocks at 18:24:00, followed by `front_door_contact` changing to false at 18:24:02. This implies the door was unlocked and likely opened (or the sensor registered the open state as 'false' depending on wiring logic, but critically it reported a change). Note: In Matter, BooleanState often maps to Open/Closed. If False=Closed/True=Open, C04 (False->True) is Opening. C09 (Lock Unlocks -> Contact False) might imply the door was already open or the sensor reads Closed as False. Regardless, the *change* in state correlates with the lock event.

No explicit fault codes, alarm events, or stuck values are present in the logs for the contact sensor.

The lock device reports normal operational states (locked/unlocked) and operations, suggesting the communication channel for the entrance devices is generally functional.

The low activity count (17 for entrance vs hundreds for other rooms) makes it difficult to rule out intermittent reporting failures without more data points showing the door remaining open/closed for extended periods without triggering.

The interpretation of BooleanState (whether False=Open or False=Closed) is ambiguous without explicit schema mapping, though the correlation with lock events supports functionality.

The entrance contact sensor may be experiencing intermittent reporting failure or has a loose connection, resulting in missed state changes. While it reported correctly during the two major lock interactions, the complete absence of any other contact events over a ~11-hour window (despite other sensors being very active) is suspicious if the door was accessed frequently. However, given the lock only flipped 6 times total, this could simply mean the door was rarely used.

Structured signals show `entrance` room activity is only 17, while living_room is 397 and bedroom is 353. This disparity suggests either the entrance is unused or the sensor is failing to report occupancy/contact changes that correlate with other room activities.

In C09, between 17:51 and 18:24, the lock operates twice (unlock at 17:51, manual unlock at 18:24). The contact sensor only reports once at 18:24. If the 17:51 unlock involved opening the door, the contact sensor should have triggered. Its absence here suggests a potential missed report.

The query specifically asks to check for faults. The lack of continuous heartbeat or periodic status updates for the boolean state (unlike temperature which updates every 5 mins) means we cannot verify if the sensor is 'stuck' or just silent. Silence is indistinguishable from 'door always closed' without external verification.

The lock state flips are only 6 times in the entire dataset. It is plausible the door was indeed only opened/closed 6 times, making the 2 contact reports sufficient.

There is no explicit error message or failed command associated with the contact sensor.

Distinguishing between 'sensor failure' and 'low usage' is impossible with this limited scope; the anomaly hypothesis relies on the assumption that the door *should* have been used more often than the lock logs suggest, which is not supported by evidence.

Correlation between `front_door_contact` state changes and `front_door_lock` operations in C04 and C09.

Absence of any `front_door_contact` events between 07:15 and 18:24, despite high activity in other rooms.

Presence of `LockOperation` events without corresponding immediate `BooleanState` changes in some intervals (e.g., 17:51 unlock).

Definition of BooleanState semantics (is False=Open or False=Closed?).

Total number of door openings expected based on household schedule or other motion sensors near the entrance.

Logs for the full 24-hour period to assess if the 11-hour window is representative.

Battery level or connectivity status of the front_door_contact device.

Verify if `front_door_contact` reports are missing in the gap between C04 and C09 (e.g., around 17:51 lock operation) to distinguish between 'door not opened' and 'sensor failure'.

Check for any explicit fault codes or connectivity drops for `front_door_contact` in adjacent chunks.

Confirm the semantic mapping of BooleanState (True/False vs Open/Closed) is consistent across all events.

The investigator correctly identifies that direct fault evidence is lacking but relies on sparse telemetry as a potential indicator of failure. This is speculative without ruling out low usage.

The analysis focuses only on C04 and C09, leaving a large gap (approx. 8 hours) where no contact sensor data exists. If the door was accessed during this time (indicated by other activity or lock operations), the absence of contact logs would be strong evidence of failure. If not, it's normal.

Requesting C03 and C05 allows us to check the immediate context before C04 and after C04/before C09 to see if there were missed events or if the device was offline entirely.

Do not conclude 'normal' based solely on two successful events; do not conclude 'fault' based solely on silence. More context is needed.

## 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（入户门传感器）是否工作正常。如果异常，请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": [
    "sparse telemetry for target device",
    "potential communication drop or sensor failure"
  ],
  "why_these_chunks": [
    "C04 contains the only clear, detailed event sequence for the entrance devices (contact state change and lock operation) in the entire log set, serving as the baseline for 'normal' behavior.",
    "C09 shows the presence of the lock device but absence of the contact sensor events despite significant activity count, helping to determine if the contact sensor is failing to report while the lock remains active.",
    "Comparing these chunks allows distinguishing between a total device offline status and a specific sensor reporting failure."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The entrance contact sensor is functioning normally. The sparse telemetry (only 2 events in the entire log set) reflects low physical usage of the front door during the monitored period, rather than a device failure.",
      "evidence": [
        "C04 shows a valid state transition sequence: `false` -> `true`


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 shows normal operation in the available logs, but a significant data gap prevents a high-confidence assessment of its continuous health.

Explanation: The provided logs for `kitchen_temp_sensor` (Chunks C00-C05 and C17) show valid, physically plausible temperature readings that fluctuate logically with time of day and correlate with other sensors in the house.

Explanation: There are no explicit fault codes, alarm events, or stuck values in the visible segments. The raw integer values correctly map to centi-degrees Celsius without overflow.

Explanation: However, there is a substantial gap in telemetry between 08:10 (end of C05) and 23:20 (start of C17). The supervisor explicitly flagged this missing period (C06-C16) as critical for verifying continuous operation.

Explanation: Without evidence from the missing chunks, we cannot rule out intermittent failures, communication dropouts, or sensor malfunctions that may have occurred during the day. Conversely, there is no positive evidence of failure in the available data.

Explanation: Per the rules, a device-fault conclusion requires direct fault evidence. Since the available evidence supports normal behavior but is incomplete, and the missing evidence could contain either normal or abnormal data, we cannot definitively classify it as anomalous or fully healthy. We default to 'no anomaly' based on the absence of fault indicators, but with low confidence due to the data gap.

Valid temperature readings in C00-C05 (e.g., 22.68°C rising to 25.43°C) and C17 (e.g., 22.57°C stable around 22.6-22.8°C).

Absence of any fault, error, or alarm fields in the structured logs for the kitchen sensor.

Significant gap in telemetry coverage from 08:10 to 23:20, preventing verification of continuous sensor health.

Retrieve and analyze log chunks C06 through C16 to verify if the sensor reported data continuously throughout the day.

Check for any transient communication errors or dropped packets during the gap period.

If the gap is confirmed to be a system logging issue rather than a sensor failure, mark the device as healthy with a note about logging gaps.

The reported temperatures for the kitchen sensor (e.g., 22.68°C at 00:00, rising to ~25.43°C by 08:10, then dropping back to ~22.57°C by 23:20) follow a logical diurnal pattern consistent with other rooms.

No explicit fault events, alarm flags, or connection drops are present in any of the reviewed chunks.

The raw integer values (e.g., 2268, 2543) correctly map to centi-degrees Celsius without overflow or formatting errors.

This hypothesis relies on the assumption that the missing intermediate chunks (C06-C16) did not contain intermittent faults that resolved themselves, which cannot be fully verified without those specific logs.

Potential intermittent communication dropout or data loss occurred during the unobserved period (approx. 08:10 to 23:20), although the device recovered successfully. While the start and end states are normal, the significant gap in provided evidence prevents ruling out transient failures or missed readings during high-activity hours.

The supervisor feedback explicitly notes a ~22-hour gap between the last observed reading in C05 (08:10) and the first in C17 (23:20).

Without C06-C16, there is no evidence of continuous telemetry coverage during this entire window.

If the sensor stopped reporting or reported invalid data during this time, it would constitute a malfunction, even if it resumed later.

There is no direct evidence of failure in the available chunks; the anomaly is inferred solely from the absence of data rather than positive evidence of fault.

The prompt asks to judge based on the provided history; assuming a fault without evidence violates the principle of requiring direct fault indicators (stuck values, alarms, etc.).

Continuous presence of `kitchen_temp_sensor` entries in C00-C05 and C17 with valid, changing temperature values.

Absence of any `fault`, `alarm`, or `error` fields in the structured logs for the kitchen sensor.

Consistency of kitchen temperature trends with other rooms (living room, bedrooms) in both the early morning (C00-C05) and late night (C17) segments.

Logs for chunks C06 through C16 (covering the period from 08:10 to 23:20) to verify continuous telemetry and rule out mid-day dropouts or stuck values.

Verify continuous telemetry for kitchen_temp_sensor during the gap between C05 (ends 08:10) and C17 (starts 23:20).

Check for stuck values or communication dropouts in the missing chunks.

Confirm if the sensor reported valid data throughout the day to rule out intermittent faults.

The investigator correctly identified that C00-C05 and C17 show normal behavior but failed to address the significant data gap (approx. 15 hours) covered by chunks C06-C16.

Concluding 'normal' based solely on start/end points is insufficient for a device-health check over a 24-hour period, as it misses potential mid-day failures.

The 'anomaly hypothesis' regarding dropout is speculative without evidence; however, the lack of evidence prevents a definitive 'normal' verdict.

Must retrieve and analyze chunks C06 through C16 to verify continuous operation before finalizing the health status.

The kitchen temperature sensor is functioning normally. The logs show consistent, periodic reporting of temperature values that fluctuate within a realistic range (approx. 22.5°C to 23.0°C) for both the start and end of the day. There are no stuck values, missing reports, or impossible transitions.

C00 shows kitchen_temp_sensor reporting values at 5-minute intervals from 00:00 to 01:20, ranging from 22.67 C to 22.96 C.

C17 shows kitchen_temp_sensor reporting values at 5-minute intervals from 23:20 to 23:55, ranging from 22.51 C to 22.79 C.

The raw integer values (e.g., 2268, 2296) correctly map to centi-degrees Celsius as per Matter notes.

No explicit fault events, alarms, or 'TestInProgress' flags are associated with the kitchen sensor in the provided chunks.

The analysis is limited to two specific time windows (early morning and late night). A fault occurring during other times (e.g., mid-day cooking) is not visible in these chunks, though the query asks to judge based on the provided history.

Sensor Stuck Value / Data Loss Hypothesis: While the provided chunks show normal operation, the triage output suspected 'sensor_stuck_value' and 'missing_telemetry'. However, the focused chunks C00 and C17 do not contain evidence of this. If the full log contained gaps or static values between these points, it would indicate a fault. Based strictly on the provided evidence, there is no anomaly.

Triage output listed 'suspected_patterns': ['sensor_stuck_value', 'missing_telemetry'].

However, inspection of C00 and C17 reveals dynamic, changing temperature values for the kitchen sensor, contradicting a 'stuck' state in these periods.

This hypothesis is effectively refuted by the direct evidence in C00 and C17. Without access to the intermediate logs where the suspected fault might have occurred, we cannot confirm an anomaly. The current data supports normal operation.

C00: kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 22.68 C (raw=2268) at 00:00:00 progressing to 23.04 C (raw=2304) at 01:15:00, showing continuous variation.

C17: kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 22.57 C (raw=2257) at 23:20:00 progressing to 22.79 C (raw=2279) at 23:55:00, showing continuous variation.

Absence of any 'Fault', 'Alarm', or 'Stuck' status codes in the structured signals or focused chunks for the kitchen device.

Logs from the middle of the day (e.g., 08:00-20:00) to rule out intermittent faults or cooking-related anomalies.

Any explicit error logs or diagnostic outputs from the Matter controller regarding the kitchen sensor.

Check for gaps in telemetry reporting (missing 5-minute intervals) between C00 and C17.

Check for 'stuck' values where the temperature remains constant for extended periods despite expected environmental changes.

Verify if the sensor reports valid data during high-activity periods (e.g., cooking times) which might trigger faults or drift.

Look for explicit fault codes, alarm events, or connection drops associated with kitchen_temp_sensor.

The investigator correctly identified that C00 and C17 show normal operation but failed to account for the significant gap in time (approx. 22 hours) between these two chunks.

Concluding the device is 'normal' based solely on start/end points is insufficient for a full-day health check, as intermittent faults, stuck sensors, or communication dropouts could have occurred in the missing chunks (C01-C16).

The triage output suspected 'sensor_stuck_value' and 'missing_telemetry', patterns that are likely to manifest in the middle of the day logs, not just at the boundaries.

To verify the hypothesis of normal operation, the intermediate chunks must be reviewed to ensure continuous, valid telemetry without anomalies.

## 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。如果异常，请说明可能的故障类型。"
  },
  "focus_rooms": [
    "kitchen"
  ],
  "focus_devices": [
    "kitchen_temp_sensor"
  ],
  "focus_chunk_ids": [
    "C00",
    "C17"
  ],
  "suspected_patterns": [
    "sensor_stuck_value",
    "missing_telemetry"
  ],
  "why_these_chunks": [
    "C00 provides the baseline operational state at the start of the log, showing normal temperature readings and transitions for the kitchen sensor.",
    "C17 provides the end-state verification to check if the sensor continues to report valid, changing data or if it has stopped/stuck."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The kitchen temperature sensor is functioning normally. The logs show consistent, periodic reporting with physically plausible temperature fluctuations that correlate with the general house-wide thermal trends (e.g., gradual warming in the early morning). There are no gaps in telemetry, stuck values, or error codes.",
      "evidence": [
        "C00-C05 and C17 all show `kitchen_temp_sensor` reporting valid `TemperatureMeasurement.MeasuredValue` updates every 5 minutes.",
        "The reported temperatures for the kitchen sensor (e.g., 22.68°C at 00:00, rising to ~25.43°C by 08:10, then dropping back to ~22.57°C by 23:20) follow a logical diurnal pattern consiste


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 sensors appear to be functioning normally based on available data; the large gap in logs is likely due to power-saving configuration or logging aggregation rather than a hardware fault.

Explanation: The investigator identified a significant data gap for the second_bedroom sensors between 01:20 and 18:45, which initially suggested a potential connectivity or power issue (Hypothesis A1).

Explanation: However, the Supervisor correctly noted that this gap could be due to low-frequency polling or power-saving modes common in smart home devices during unoccupied night hours, and requested further investigation into intermediate chunks (C05-C16) to confirm continuity.

Explanation: Since the intermediate chunks were not provided, we cannot definitively rule out a communication dropout. However, per the Matter Notes and Supervisor guidelines, 'Scaled temperature values alone are not enough' to declare a fault, and missing logs require corroboration of an actual failure event.

Explanation: In the provided chunks (C00-C04, C17, C22), the sensor data is coherent: temperatures change gradually within physical limits, and occupancy states are consistent with an unoccupied room. There are no explicit alarm/fault events, stuck values, or impossible transitions.

Explanation: Without evidence of a persistent failure, repeated retries, or explicit fault codes, and given the plausibility of normal power-saving behavior, the most supported conclusion is that the device is healthy but operating with sparse telemetry during the gap period.

Explanation: Therefore, we cannot classify this as a device malfunction or anomaly with high confidence. The lack of evidence for a fault leads to a 'none' classification, albeit with low confidence due to the incomplete view of the 17-hour gap.

Valid, stable temperature readings (e.g., 22.65 C to 24.70 C in early morning) and correct occupancy status (unoccupied) in C00-C04.

Consistent operation resumes in C17 and C22 with reasonable values, showing no signs of drift or stuck states upon reconnection.

Absence of explicit Matter fault/alarm events or 'offline' status reports in the provided logs.

Review the full log stream (specifically chunks C05-C16) to determine if the silence was continuous or intermittent.

Check the device's polling interval configuration to see if long gaps are expected behavior for this sensor type.

Monitor the device over the next 24 hours to ensure regular reporting intervals are maintained.

Comparison with peer devices (kitchen, master_bedroom) to isolate local vs global issues

Verification of occupancy state transitions

C00-C04: Temperature readings for second_bedroom_temp_sensor range from 22.65 C to 24.70 C between 00:00 and 06:48, showing smooth transitions without stuck values or impossible jumps.

C00-C04: OccupancySensing.Occupancy remains 0 (unoccupied) consistently throughout the early morning period, which aligns with typical sleeping patterns.

C17: At 19:00 and 19:30, second_bedroom_occupancy is 0 (unoccupied), while other rooms like living_room and bathroom show occupancy, indicating the sensor is actively reporting state changes relative to other devices.

C22: Late night readings (23:10-23:55) show stable temperatures around 22.5-22.7 C and unoccupied status, consistent with the rest of the house being quiet.

There is a significant data gap between 01:20 (end of C01) and 18:45 (start of C17). While the sampled points before and after are normal, we cannot confirm continuous operation during this ~17-hour window based solely on these chunks.

Potential intermittent communication dropout or network issue affecting the second_bedroom sensors, evidenced by the large gap in logs between 01:20 and 18:45. If the sensors did not report during this time, it indicates a connectivity fault rather than a sensor malfunction.

Supervisor feedback highlights a massive data gap between C01 (ends 01:20) and C17 (starts 18:45).

In C17, at 18:52, kitchen_temp_sensor reports 'None', suggesting potential transient network issues or device errors in the system that could correlate with missing data elsewhere.

The absence of any second_bedroom logs for ~17 hours is abnormal for a health-monitoring scenario where regular polling is expected.

The query asks if the sensors are working *normally*. A long gap might be due to power-saving modes or logging aggregation policies rather than a fault. Without explicit 'offline' or 'fault' events, it's ambiguous.

When logs reappear in C17, the values are reasonable (22.97 C), suggesting the sensor itself wasn't broken, just silent.

No specific safety or behavioral anomaly detected in the second_bedroom. The room remains unoccupied throughout the monitored periods, and temperature fluctuations are within normal physical limits for an unoccupied bedroom.

Occupancy is consistently 0 in all provided chunks for second_bedroom.

Temperature trends are monotonic or slowly varying, matching ambient conditions.

No lock states, actuator commands, or intrusion indicators are present for this room.

Does not address the potential data gap issue raised by the supervisor.

The presence of valid, coherent temperature and occupancy data in C00-C04 and C17-C22 confirms the sensors are capable of reporting correct values.

The complete absence of second_bedroom logs between 01:20 and 18:45 is the primary indicator of potential health issues (connectivity vs. configuration).

The 'None' value in kitchen_temp_sensor at 18:52 serves as a corroborating signal for potential transient system-wide telemetry issues.

Logs for the period between 01:20 and 18:45 to verify if the sensors were silent due to fault, power loss, or policy.

Explicit Matter alarm/fault events to rule out hardware failures.

Confirmation of the polling interval configuration for the second_bedroom sensors.

Verification of sensor continuity between C04 (ends 06:48) and C17 (starts 18:45). The current investigation assumes a gap or silence but lacks evidence to confirm if the sensors were offline, in power-save mode, or simply not logged.

Check for explicit Matter fault/alarm events or 'offline' status reports which are required for a definitive device-health conclusion per protocol notes.

Analysis of the 'None' value in kitchen_temp_sensor at 18:52 to determine if it indicates a systemic network issue affecting all devices or an isolated sensor failure.

The investigator correctly identified that the second_bedroom sensors appear functional when present (valid temps, correct occupancy logic).

However, the conclusion relies heavily on the absence of data between 01:20 and 18:45. Without checking the intermediate chunks (C05-C16), we cannot distinguish between a configuration/policy gap (e.g., low-frequency polling at night) and a genuine connectivity/hardware fault.

Per protocol, 'Scaled temperature values alone are not enough' for fault conclusions, and missing logs require corroboration. The current 'anomaly hypothesis' is speculative regarding the cause of the gap.

Requesting the full set of intermediate chunks to establish the telemetry pattern during the unmonitored period before finalizing the health verdict.

The second bedroom sensors (temperature and occupancy) are functioning normally. The temperature readings show expected minor fluctuations consistent with a stable indoor environment, and the occupancy sensor correctly reports 'unoccupied' during periods of inactivity.

C00: Temperature readings for second_bedroom_temp_sensor range from 22.65 C to 23.09 C between 00:00 and 01:20, showing normal thermal inertia and slight variations without sticking or impossible jumps.

C00: OccupancySensing.Occupancy is consistently 0 (unoccupied) at 00:00, 00:30, and 01:00, which aligns with typical nighttime behavior for a secondary bedroom.

C17: At 18:45, 18:50, 19:00, etc., the temperature sensor continues to report valid values (e.g., 22.97 C, 22.99 C, 22.88 C) while other rooms show activity, indicating the device is still communicating and measuring.

C17: Occupancy remains 0 (unoccupied) throughout the evening period shown in C17, consistent with the room not being used.

C22: Late night readings (23:10-23:55) show stable temperatures around 22.5-22.7 C and continued unoccupied status, confirming no late-night intrusion or sensor drift.

The logs provided are sparse samples (every 5 minutes for temp, intermittent for occupancy). A complete absence of data gaps does not guarantee continuous health, but within the sampled windows, no faults are visible.

Temperature continuity: In C00, C17, and C22, the second_bedroom_temp_sensor provides valid, non-stuck, physically plausible temperature values that fluctuate slightly over time, matching the general trend of other bedrooms (master_bedroom) rather than diverging wildly or staying constant.

Occupancy consistency: The second_bedroom_occupancy sensor consistently reports 'unoccupied' (raw=0) across all three time blocks (early morning, evening, late night), with no contradictory 'occupied' signals that would suggest a stuck-on fault or false positive intrusion detection.

Comparison with peers: The second bedroom's temperature profile tracks closely with the master bedroom (e.g., both ~22.7-23.0 C in C00, ~22.8-22.9 C in C17), suggesting similar environmental conditions and no isolated sensor error.

Continuous log stream: The current view relies on sampled chunks (C00, C17, C22). A full 24-hour log would be needed to definitively rule out intermittent communication dropouts or brief stuck-value episodes that occurred between sample points.

Explicit fault events: There are no explicit Matter alarm or fault events in the provided snippets. While their absence supports normalcy, checking for hidden diagnostic flags would strengthen the conclusion.

Check for data gaps or missing timestamps in second_bedroom sensors between C00 (ends 01:20) and C17 (starts 18:45). A ~17 hour gap is significant for health monitoring.

Verify if the 'None' value in kitchen_temp_sensor at 18:52 indicates a broader network issue or specific device fault that might correlate with other devices.

Check for any explicit Matter alarm/fault events in the full log stream, not just sampled chunks.

Confirm occupancy sensor behavior during potential transition periods (e.g., late night/early morning) to rule out stuck states.

The investigator correctly identified that the sampled chunks show normal behavior. However, relying solely on sparse samples (C00, C17, C22) is insufficient for a definitive 'device-health' conclusion over a 24-hour period.

There is a massive data gap between 01:20 and 18:45. The investigator did not check if the sensors reported anything during this time. If they went silent, that is a fault. If they reported normally but weren't sampled, we need to see it.

The 'None' value in the kitchen sensor at 18:52 is an anomaly worth investigating as a potential systemic issue, even if unrelated to the bedroom sensors.

Without checking the intermediate chunks, we cannot rule out intermittent failures, communication dropouts, or stuck values that occurred outside the sampled windows.

Therefore, evidence is insufficient. Recommend refining investigation by requesting adjacent chunks to fill the gap and verify continuous operation.

## 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_sensor"
    ],
    "target_question": "Check if sensors in the second bedroom are working normally. If abnormal, specify the fault type."
  },
  "focus_rooms": [
    "second_bedroom"
  ],
  "focus_devices": [
    "second_bedroom_temp_sensor",
    "second_bedroom_occupancy_sensor"
  ],
  "focus_chunk_ids": [
    "C00",
    "C17",
    "C22"
  ],
  "suspected_patterns": [
    "Sensor data continuity check",
    "Comparison with peer devices (kitchen, master_bedroom) to isolate local vs global issues",
    "Verification of occupancy state transitions"
  ],
  "why_these_chunks": [
    "C00: Baseline early morning data showing normal temperature readings and unoccupied status for second_bedroom sensors.",
    "C17: Mid-day/afternoon data point to check for any transient anomalies or missing logs during active hours.",
    "C22: Late night data to confirm consistent operation and no drift or failure towards the end of the log period."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The second_bedroom sensors (temperature and occupancy) are functioning normally. The temperature readings show stable, gradual changes consistent with a passive indoor environment, and the occupancy sensor correctly reports 'unoccupied' during night hours when no activity is expected.",


DF-06-FP: 窗帘正常移动但速度慢

The master_bedroom temperature and occupancy sensors are functioning normally with no evidence of faults.

Explanation: Analysis of chunks C00, C22, and C30 confirms that the master_bedroom_temp_sensor consistently reports valid, physically plausible temperature values (ranging approx. 22.5°C to 23.2°C) without any 'None' entries or stuck states.

Explanation: The master_bedroom_occupancy sensor shows logical transitions from 'unoccupied' (raw=0) in the early morning to 'occupied' (raw=1) in the evening, with no flickering or impossible state changes.

Explanation: Although a transient 'None' value was observed in the kitchen_temp_sensor at 18:24:00, the master_bedroom sensors continued to report valid data during this period, indicating the issue was isolated to the kitchen device or a localized network event not affecting the target devices.

Explanation: No direct fault evidence (such as repeated failures, alarm events, or invalid raw codes) exists for the master_bedroom sensors.

master_bedroom_temp_sensor reported valid values (e.g., 23.16 C at 18:25:00) while kitchen_temp_sensor failed.

master_bedroom_occupancy transitioned cleanly from unoccupied (0) to occupied (1) around 18:24:00-18:29:00.

Temperature trends in master_bedroom correlate logically with other rooms and time of day.

No action required for master_bedroom sensors.

Monitor kitchen_temp_sensor for recurrence of the transient dropout if it persists.

Missing telemetry (None value)
Intermittent sensor failure

C00: Temperature readings fluctuate naturally between 22.68 C and 22.98 C with no stuck values or impossible jumps.

C00: Occupancy sensor reports 'unoccupied' (raw=0) consistently during the early morning hours when the room is likely empty.

C22: Temperature readings remain stable around 22.9-23.2 C while the room is occupied, showing normal thermal response.

C22: Occupancy sensor correctly transitions to 'occupied' (raw=1) at 18:24:00 and remains so, correlating with other room activities.

C30: Sensors continue to report valid data late in the day (23:25-23:55), with temperatures settling around 22.5-22.7 C as night progresses.

Does not account for the single transient 'None' value observed in the kitchen sensor at 18:24:00, although this device is distinct from the master_bedroom sensors.

Potential intermittent communication fault or transient sensor dropout affecting the master_bedroom ecosystem, evidenced by a simultaneous 'None' reading in the kitchen sensor at 18:24:00. While the master_bedroom temp sensor recovered immediately, this suggests a potential network instability or power glitch that could affect any device on the same bus/network segment.

At 18:24:00 in C22, `kitchen_temp_sensor` reports `TemperatureMeasurement.MeasuredValue = None`.

This event occurs simultaneously with `master_bedroom_occupancy` transitioning to 'occupied', suggesting a busy network state or localized interference.

Although `master_bedroom_temp_sensor` reported a valid value (23.16 C) just before and after (at 18:25:00), the proximity of the kitchen failure raises suspicion of a systemic issue rather than an isolated device fault.

The master_bedroom temperature sensor itself did NOT report a 'None' value; it reported valid data continuously.

The kitchen sensor recovered instantly at 18:25:00, indicating no persistent failure.

There is no evidence of repeated failures or stuck states for the master_bedroom sensors specifically.

Isolated single-point telemetry drops in one unrelated device (kitchen) are insufficient to diagnose a malfunction in the target device (master_bedroom).

Master_bedroom_temp_sensor never reports 'None' or invalid data in chunks C00, C22, or C30.

Master_bedroom_occupancy sensor shows clean 0->1 transitions without flickering or stuck states.

Temperature values for master_bedroom are physically consistent with adjacent rooms (living_room, kids_room) and change gradually over time.

The only anomaly ('None') appears in the kitchen_temp_sensor at 18:24:00, not in the master_bedroom devices.

Network logs or error codes associated with the 18:24:00 kitchen sensor dropout to determine if it was a general network issue.

Battery status or signal strength metrics for the master_bedroom sensors to rule out low-power warnings.

The investigator correctly identified the target devices (master_bedroom_temp_sensor, master_bedroom_occupancy_sensor) and analyzed the provided chunks (C00, C22, C30).

The analysis correctly distinguishes between the anomaly in the kitchen sensor (transient None value) and the stable operation of the master bedroom sensors.

Evidence shows consistent, physically plausible temperature readings and logical occupancy transitions for the master bedroom sensors across all time periods.

No direct fault evidence (stuck values, impossible transitions, repeated failures) was found for the target devices.

The conclusion that the sensors are working normally is supported by the data. The transient kitchen error is irrelevant to the health of the master bedroom sensors.

No protocol misunderstandings detected (scaled values interpreted correctly).

## 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_sensor"
    ],
    "target_question": "Check if sensors in master_bedroom are working normally; if abnormal, specify fault type."
  },
  "focus_rooms": [
    "master_bedroom"
  ],
  "focus_devices": [
    "master_bedroom_temp_sensor",
    "master_bedroom_occupancy_sensor"
  ],
  "focus_chunk_ids": [
    "C00",
    "C22",
    "C30"
  ],
  "suspected_patterns": [
    "Missing telemetry (None value)",
    "Intermittent sensor failure"
  ],
  "why_these_chunks": [
    "C00: Baseline chunk showing normal temperature and occupancy reporting for master_bedroom at the start of the log period.",
    "C22: Chunk containing a specific anomaly where master_bedroom_temp_sensor reports 'None' while other devices function normally, indicating a potential transient fault or communication drop.",
    "C30: Late-stage chunk to verify if the sensor recovered and resumed normal operation after the incident in C22."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The master_bedroom sensors (temperature and occupancy) are functioning normally. The telemetry shows consistent reporting intervals, physically plausible temperature values within the expected range for a bedroom (~22-23°C), and logical occupancy transitions corresponding to human presence.",
      "evidence": [


DF-01-FP: 空调恒温控制导致温度稳定

有微小波动（±0.2°C），不是完全固定值；且空调处于开启状态

The living_room temperature and occupancy sensors are operating normally with no evidence of faults.

Explanation: The query specifically targets the health of sensors in the 'living_room' area (living_room_temp_sensor and living_room_occupancy).

Explanation: Analysis of living_room_temp_sensor logs from C00 to C06 shows continuous, valid temperature readings ranging from 22.69°C to 25.30°C. The values exhibit gradual, physically plausible drift consistent with ambient environmental changes over time, with no stuck values, impossible spikes, or data dropouts.

Explanation: Analysis of living_room_occupancy logs shows a consistent state of 'unoccupied' (raw=0) throughout the entire observation window (00:00 - 07:43). This is corroborated by the absence of any actuator events (lights, locks) specific to the living room during this period, indicating that the lack of occupancy detection is likely due to actual absence rather than sensor failure.

Explanation: While other devices (kitchen_temp_sensor) showed intermittent `None` values, these are isolated incidents not affecting the living_room sensors. The supervisor confirmed that the investigation correctly focused on the target devices and found sufficient evidence for normal operation.

Explanation: No direct fault evidence (stuck values, repeated non-recovery, explicit alarms) was found for the living_room sensors.

living_room_temp_sensor reports continuous valid readings (22.69 C - 25.30 C) with natural drift.

living_room_occupancy consistently reports 'unoccupied' (raw=0) with no contradictory activity in the room.

Absence of fault codes or communication errors for living_room devices.

No immediate action required; sensors appear healthy.

living_room_temp_sensor reports continuous, plausible temperature values ranging from 22.69 C to 25.30 C over the 7+ hour window.

Temperature trends correlate logically with time of day (cooling at night, warming in early morning).

living_room_occupancy consistently reports 'unoccupied' (raw=0) from 00:00 to 07:43.

No explicit fault codes, communication errors, or stuck-value patterns detected for the living_room sensors.

The occupancy sensor was never tested under load (i.e., no one entered the living room during the log window), so its responsiveness to actual presence is not verified by this data.

Potential intermittent telemetry dropout or sensor malfunction in the kitchen_temp_sensor, which may indicate a broader network or device health issue affecting multiple sensors, though the living_room sensors appear unaffected.

At 06:52:00, kitchen_temp_sensor reports `TemperatureMeasurement.MeasuredValue = None`.

At 07:10:10, kitchen_temp_sensor again reports `TemperatureMeasurement.MeasuredValue = None`.

These dropouts occur while other sensors (including living_room) continue to report valid data, suggesting an isolated device or connectivity issue rather than a system-wide failure.

The query specifically asks about the **living_room** sensors, not the kitchen sensor.

Isolated `None` values can be caused by transient network issues or scheduled maintenance cycles and do not necessarily constitute a permanent fault without further evidence of non-recovery or repeated failures.

Possible false negative in occupancy detection if the living_room was actually occupied but the sensor failed to trigger, although there is no direct evidence of presence in the logs to contradict the 'unoccupied' status.

The supervisor feedback noted a risk that the occupancy sensor might be stuck reporting 'unoccupied' while people are present.

However, cross-referencing with other devices (lights, locks) shows no activity in the living_room area (no lights on, no door lock interactions specific to living room access).

There is zero corroborating evidence (motion, light, sound) to suggest anyone was in the living_room during the logged period.

The 'unoccupied' status is consistent with the rest of the house's behavior until ~06:00 when activity shifts to bedrooms/kitchen.

living_room_temp_sensor continuous valid readings (22.69 C - 25.30 C) vs. kitchen_temp_sensor intermittent `None` values.

living_room_occupancy consistently raw=0 throughout the entire 00:00-07:43 window.

Absence of any living_room-specific actuator events (lights, locks) that would imply presence.

Logs covering periods where the living_room is explicitly occupied to verify occupancy sensor transition (0 -> 1).

Confirmation of whether the kitchen sensor `None` values were resolved or persisted beyond the current window.

Context on whether the living_room is typically used during the early morning hours (00:00-07:00) to assess if the 'unoccupied' status is statistically expected.

No missing checks for the specific query target (living_room sensors). The investigation correctly identified that the living_room sensors are functioning normally based on the provided logs.

The investigator correctly focused on the 'living_room' devices as requested.

The analysis of the living_room_temp_sensor shows consistent, plausible temperature drifts with no stuck values or dropouts, indicating normal operation.

The analysis of the living_room_occupancy sensor shows a consistent 'unoccupied' state. While this is not verified by an occupancy event in the logs, it is consistent with the lack of any other activity (lights, locks) in that room during the observed period. This is a valid conclusion for 'normal operation' given the constraints.

The investigator correctly noted the kitchen sensor issues but rightly excluded them from the final verdict for the living_room, although they were worth noting as context.

The conclusion that living_room sensors are working normally is supported by the evidence. No further chunks are needed to answer the specific question about the living_room sensors.

The living room temperature sensor and occupancy sensor are functioning normally. The temperature readings show typical thermal inertia and minor fluctuations consistent with an unoccupied room during early morning hours (00:00-05:00). The occupancy sensor correctly reports 'unoccupied' throughout the period, which aligns with the lack of activity in the structured signals for this specific timeframe.

Temperature values fluctuate naturally between 22.69 C and 24.44 C without getting stuck at a single value or showing impossible jumps.

OccupancySensing.Occupancy remains consistently at raw=0 (unoccupied) across all timestamps in chunks C00-C03.

No explicit fault events, alarms, or error codes are present in the logs.

The rate of change in temperature is gradual, consistent with passive heating/cooling in an empty room.

The analysis is limited to the first 5 hours; if the query implies a longer duration where anomalies might occur, this data is insufficient.

Sensor Malfunction / Data Integrity Issue: While no obvious 'stuck' value is present, the high frequency of occupancy updates (1098 total in the full dataset, though sparse here) combined with the specific query focus suggests checking for intermittent communication drops or timestamp synchronization issues not visible in this short window. However, based strictly on the provided chunks, there is no direct evidence of malfunction.

None found in the provided chunks. The sensors appear healthy.

This hypothesis is weak because the logs explicitly show valid, changing temperature data and consistent occupancy states. There is no evidence of failure, stuck values, or missing data points that would constitute a malfunction.

Living room temperature sensor reports continuous, varying values (e.g., 22.69 -> 22.71 -> ... -> 24.44) rather than a static stuck value.

Living room occupancy sensor consistently reports 'unoccupied' (raw=0) without any transitions to 'occupied', which is consistent with the time of day and lack of other activity indicators.

Absence of any error flags, retry loops, or invalid state codes in the Matter-style logs.

Logs for the remainder of the day (after 05:00) to verify behavior during occupied periods.

Battery level or connection strength metrics for the sensors, which are not included in the current telemetry.

Verification of sensor behavior during occupied periods (typically after 05:00 or later in the day).

Check for occupancy transitions (unoccupied -> occupied) to validate sensor responsiveness.

Check for any explicit fault codes, retry loops, or communication errors in the full log set.

Cross-reference temperature trends with other sensors to rule out localized environmental anomalies vs. sensor drift.

The investigator correctly identified that the provided data (00:00-05:00) shows normal baseline behavior for an unoccupied room.

However, concluding 'normal operation' based solely on early morning idle time is insufficient for a comprehensive device health check. Sensors must be validated under load (occupancy) and transition states.

The current evidence lacks the critical test case: does the occupancy sensor trigger when people enter? Does the temperature sensor react to heat sources?

There is a medium risk of false alarm if the conclusion is taken as final; a sensor could be stuck reporting 'unoccupied' while people are present, which would not be detected in this specific window.

Request adjacent chunks (C04+) to observe daytime/occupied activity.

## 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: 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": 520,
    "kids_room": 521,
    "kitchen": 303,
    "dining_room": 224,
    "second_bedroom": 48,
    "master_bathroom": 60,
    "entrance": 227,
    "second_bathroom": 1
  },
  "attribute_frequency": {
    "MeasuredValue": 1160,
    "Occupancy": 1098,
    "On

## 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"
    ],
    "target_question": "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。"
  },
  "focus_rooms": [
    "living_room"
  ],
  "focus_devices": [
    "living_room_temp_sensor",
    "living_room_occupancy"
  ],
  "focus_chunk_ids": [
    "C00",
    "C01",
    "C02",
    "C03"
  ],
  "suspected_patterns": [
    "sensor_stuck_value",
    "missing_data",
    "normal_operation_verification"
  ],
  "why_these_chunks": [
    "C00-C03 cover the initial period (00:00-05:00) where temperature sensor data is sparse and occupancy is consistently unoccupied, allowing verification of baseline behavior and detection of any immediate faults or missing logs.",
    "These chunks provide sufficient context to determine if the lack of frequent updates or specific values constitutes a fault versus normal low-activity state."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The living_room temperature and occupancy sensors are functioning normally. The temperature readings show expected gradual drift consistent with ambient environmental changes (night to morning), and the occupancy sensor correctly reports 'unoccupied' during the entire observed period, which aligns with the lack of activity in other rooms until later in the morning.",
      "evidence": [
        "living_room_temp_sensor rep