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 are functioning normally; reported anomalies (None values) were isolated to the kitchen sensor and did not affect bedroom data continuity or logic.

Explanation: Analysis of C17, C18, and C20 confirms that second_bedroom_temp_sensor consistently reported valid temperature readings at regular intervals (~5-10 mins) with logical thermal drift (cooling from ~22.97 C to ~22.40 C).

Explanation: The second_bedroom_occupancy sensor correctly transitioned between 'occupied' and 'unoccupied' states, aligning logically with other room occupancy events and showing no stuck values or impossible transitions.

Explanation: While the kitchen_temp_sensor experienced intermittent 'MeasuredValue = None' events at T18:52:00 and T19:42:00, this was a localized issue affecting only that specific device, as all other sensors in the system (including living_room and master_bedroom) continued to report valid data during these exact timestamps.

Explanation: No explicit fault events, alarm codes, or recovery failures were recorded for the second_bedroom devices, satisfying the criteria for normal operation under Matter semantics.

second_bedroom_temp_sensor maintained continuous data flow with plausible temperature variations throughout the entire observation window.

Occurrences of 'MeasuredValue = None' were exclusively associated with kitchen_temp_sensor, demonstrating no systemic network failure affecting the bedroom.

Monitor the kitchen_temp_sensor for persistent faults if it fails to recover from 'None' states in future logs.

No immediate action required for second_bedroom sensors.

second_bedroom_temp_sensor reported valid temperatures (e.g., 22.97 C, 22.88 C) at regular intervals (every ~5-10 mins).

Readings showed natural thermal drift consistent with ambient conditions (gradual cooling from ~22.97 C to ~22.40 C over time).

Occupancy sensor correctly reported 'unoccupied' (raw=0) during periods when no occupancy events were logged for that specific room, matching the pattern of other unoccupied rooms like master_bedroom.

No explicit fault events or alarm codes were generated by the second_bedroom devices.

The kitchen_temp_sensor experienced intermittent `None` values, which could suggest a network-wide communication issue rather than isolated device failure, though this does not directly impact the bedroom's normal operation unless systemic.

second_bedroom_temp_sensor maintained continuous data flow with plausible temperature variations.

second_bedroom_occupancy transitions aligned logically with system-wide occupancy patterns (unoccupied when others were unoccupied).

Absence of any raw value anomalies (like `None` or impossible states) specifically associated with the second_bedroom devices.

Historical logs prior to 2026-06-10T18:45:00 to establish baseline stability before the observed window.

Physical inspection status of the second_bedroom sensor hardware (to rule out environmental damage not reflected in digital logs).

Verify baseline stability prior to the observation window (2026-06-10T18:45:00) to confirm no historical degradation patterns.

Cross-validate physical sensor health status if available in device metadata.

The investigator correctly identified that `second_bedroom` sensors are functioning normally. The anomalies (`MeasuredValue = None`) were isolated exclusively to the `kitchen_temp_sensor`, appearing at timestamps T18:52:00 and T19:42:00.

The evidence strongly supports a localized hardware or communication issue with the kitchen sensor rather than a systemic network failure, as all other devices (including those in the same room as the kitchen in some chunks, like living_room, though not explicitly sharing a sub-network in the logs, and definitely others) reported valid data during these exact moments of kitchen sensor failure.

Temperature readings for `second_bedroom_temp_sensor` showed logical thermal drift and consistent intervals (~5-10 mins). Occupancy states aligned logically with unoccupied periods.

No protocol-format misunderstandings detected. The scaling (centi-degrees) was handled correctly by the investigator's raw value checks (e.g., 2297 -> 22.97 C). No false-alarm risks identified regarding impossible temperatures.

The conclusion 'Working Normally' is robust based on the provided evidence.

## 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"],
    "target_question": "Is the sensor in second_bedroom working normally? If not, what is the fault type?"
  },
  "focus_rooms": ["second_bedroom", "kitchen", "living_room"],
  "focus_devices": ["second_bedroom_temp_sensor", "kitchen_temp_sensor", "living_room_temp_sensor"],
  "focus_chunk_ids": ["C17", "C18", "C20"],
  "suspected_patterns": [
    "Intermittent data loss (None values) on specific sensors during high activity periods.",
    "Potential temperature reading divergence or sensor failure requiring cross-room validation."
  ],
  "why_these_chunks": [
    "C17: Contains 'TemperatureMeasurement.MeasuredValue = None' for kitchen_temp_sensor, indicating a potential pattern of sensor data loss that might affect other devices like second_bedroom_temp_sensor if they share similar issues or environmental conditions.",
    "C18: Shows another instance of 'MeasuredValue = None' for kitchen_temp_sensor and occupancy events, reinforcing the suspicion of intermittent hardware faults or communication drops.",
    "C20: Selected to verify if the anomaly persists or resolves after the event in C18, ensuring a complete view of the device's health status over time."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "All sensors in the second_bedroom are functioning normally, providing consistent and logical temperature readings throughout the observation period.",
      "ev


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 functioning correctly, reflecting a gradual ambient temperature rise consistent with environmental conditions across all monitored zones.

Explanation: Cross-device correlation confirms a global trend: From T00:00 to T08:10, the kitchen sensor (22.68°C -> 25.37°C) tracks closely with living_room (22.80°C -> 25.30°C), master_bedroom, and second_bedroom sensors, indicating a uniform environmental shift rather than an isolated device failure.

Explanation: No stuck states or impossible jumps are observed in the kitchen sensor logs; values fluctuate naturally within expected measurement noise margins (e.g., +/- 0.1°C) while maintaining the general upward trajectory.

Explanation: There is a complete absence of `DeviceFault`, `Alarm`, or `Error` events in the Matter protocol stack for the kitchen device throughout the entire log window, which would be expected if a critical hardware malfunction occurred.

Explanation: The rate of change (~0.34°C/hr) is physically realistic for passive heating (e.g., solar gain) and matches the drift observed in other rooms, ruling out rapid overheating or sensor calibration errors.

Kitchen temp rises from 22.68°C to 25.37°C over 8 hours, mirroring the ~2.5°C rise seen in all other room sensors simultaneously.

No explicit fault codes or alarm events were generated by the Matter controller for the kitchen_temp_sensor during the observation period.

Data continuity is maintained without gaps or repeated identical values indicative of a sensor freeze.

Continue monitoring to confirm if temperatures stabilize once external heat sources (sunlight/cooking) are removed.

Verify HVAC system status if temperatures exceed comfort thresholds (>26°C) in subsequent hours.

Data consistency: No repeated identical values (stuck state) or impossible jumps are observed in the raw logs within the sampled intervals.

No fault events: There are no `DeviceFault`, `Alarm`, or `Error` events generated by the Matter protocol stack during this window.

Absence of explicit threshold validation: It is unknown if 25.37°C exceeds a specific 'out-of-range' configuration for this device model, though it is not inherently dangerous.

Sample duration: Only 8 hours of data are available; long-term stability cannot be confirmed.

Uniform temperature increase across all five monitored rooms (Living, Master Bedroom, Second Bedroom, Kitchen, Bathroom implied via pattern) rules out localized sensor failure or hardware malfunction.

Complete absence of any `DeviceFault` or `Alarm` event in the structured logs despite the temperature rising above typical indoor comfort ranges (>25°C).

Device-specific threshold configuration: Does the Matter policy define an alarm at >24°C or >25°C?

External context: Is there scheduled cooking activity or sunlight exposure that justifies the temperature rise?

Verify if the uniform temperature rise pattern persists in chunks C06 and C07 to confirm the environmental trend is stable or if a divergence occurs.

Check for any `DeviceFault` or `Alarm` events specifically associated with the kitchen sensor across all available logs (not just the sampled window in Investigator Output).

Analyze the rate of change (differential) between the kitchen sensor and other room sensors over time to rule out localized heating sources affecting only the kitchen that haven't triggered an alarm yet.

The investigator correctly identified that the rising temperatures are likely environmental rather than a sensor fault based on cross-correlation, but the evidence stops at T08:10. A single 8-hour window is insufficient to declare long-term stability or definitively rule out intermittent faults.

The 'false-alarm risk' is low because the values (22-25°C) are physically realistic and consistent across devices; the investigator did not misinterpret scaled values as literal hundreds of degrees.

To provide a robust verdict, we must see if the temperature stabilizes or continues rising linearly/exponentially in the next chunks (C06, C07). If it stabilizes, it supports the 'normal operation due to sun load' hypothesis. If it diverges, it might indicate a specific kitchen issue.

Do not finalize the verdict as 'Normal' yet due to limited temporal scope.

The kitchen temperature sensor is functioning normally, reporting gradual ambient temperature increases consistent with environmental heating trends observed in other rooms.

From C00 to C03, the kitchen_temp_sensor values show a continuous, logical progression (22.68°C -> 24.63°C) without sudden jumps or stuck states.

The rate of change (~0.05°C per minute average) is physically realistic for passive thermal drift or mild heating.

Values remain within the expected range for indoor environments (22-25°C).

No explicit fault events, alarms, or 'TestInProgress' flags are raised for this device.

Slight lag compared to living_room_temp_sensor at certain intervals (e.g., 04:10), but this could be due to sensor placement (kitchens often cooler initially than living areas due to airflow/opening doors).

Absence of any raw value anomalies, stuck readings, or impossible state transitions in the kitchen logs.

Consistency of data frequency and format across all chunks.

Lack of cross-device correlation issues that would suggest a network-wide synchronization failure affecting only the kitchen.

Actual setpoint targets or alarm thresholds for the kitchen sensor to definitively rule out 'out-of-range' behavior.

Physical layout details (e.g., proximity to stove or HVAC vents) to contextualize the specific temperature trajectory.

Cross-device correlation analysis: Compare kitchen_temp_sensor drift rate against living_room and bedroom sensors to rule out network-wide environmental shifts or clock skew.

Threshold validation: Verify if the observed temperature rise (22.68°C -> 24.63°C over 5.5 hours) exceeds the device's specific 'out-of-range' alarm thresholds defined in Matter configuration.

State transition audit: Check for any implicit 'stuck' behavior where the sensor fails to track rapid ambient changes seen in other rooms, despite the slow overall trend.

Event log completeness: Ensure no `DeviceFault` or `Alarm` events were generated during this window that might have been filtered out or are present in later chunks.

The investigator correctly identified that scaled values (e.g., 2463 = 24.63°C) do not indicate a fault, avoiding a false alarm based on raw integer misinterpretation.

However, the evidence is currently insufficient for a definitive 'Normal' verdict. While the data looks physically plausible as a slow ambient rise, it lacks cross-validation with other devices to confirm the environment itself isn't changing uniformly, nor does it confirm if this specific trajectory triggers an 'out-of-range' alert per device policy.

Extending the investigation to C04 and C05 is necessary to observe if the temperature stabilizes, continues rising beyond realistic bounds, or if a fault event eventually triggers, which would clarify the health status.

The current sample covers only ~5.5 hours; longer-term stability or fault manifestation might require more time.

## 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": "Check if the kitchen temperature sensor is working normally and identify potential faults if not."
  },
  "focus_rooms": ["kitchen"],
  "focus_devices": ["kitchen_temp_sensor"],
  "focus_chunk_ids": ["C00", "C01", "C02", "C03"],
  "suspected_patterns": [],
  "why_these_chunks": "These chunks contain the earliest logs for the kitchen temperature sensor, allowing verification of initial startup behavior, baseline readings, and early transition patterns to determine if the device is functioning within normal parameters or exhibiting stuck/recovering fault states."
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The kitchen temperature sensor is functioning correctly, reporting a gradual rise in ambient temperature consistent with environmental conditions (e.g., solar gain or HVAC cycling) observed across all other room sensors.",
      "evidence": [
        "Cross-device correlation: From T00:00 to T08:10, the living_room, master_bedroom, and second_bedroom sensors show a similar upward trend (approx +2.5°C), indicating a uniform environmental shift rather than an isolated sensor error.",
        "Plausible drift rate: The kitchen sensor rises from 22.68°C to 25.37°C over ~8 hours (~0.34°C/hr). This rate matches the general drift seen in other rooms (e.g., living_room 22.80 -> 25.30), which is physically realistic for passive heating.",
        "Data consistency: No repeated identical values (s


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 sensor exhibits a transient spike to 25.10°C followed by immediate recovery, which contradicts the expected thermal inertia of the environment during active cooling, indicating a likely sensor glitch or data corruption.

Explanation: At 18:24:00, immediately after the AC was turned on at 18:13:31, the living_room_temp_sensor reported a sudden jump from ~23.08°C to 25.10°C.

Explanation: This 2-degree increase in one minute is physically inconsistent with the ambient air temperature dynamics of a room where an air conditioner has just been activated to lower the temperature; such rapid reversal of a cooling trend without a new intense heat source is anomalous.

Explanation: The anomaly resolved within 60 seconds (back to 23.06°C at 18:25:00) and subsequent readings remained stable and consistent with the HVAC cooling effect, ruling out a sustained environmental hazard like a fire or unattended cooking event.

Explanation: Since no explicit `DeviceFault` event was logged and the system recovered autonomously, this pattern is best classified as a momentary sensor malfunction (e.g., RF interference, packet corruption, or internal register error) rather than a persistent hardware failure.

Timestamp 18:24:00: living_room_temp_sensor MeasuredValue = 25.10 C (raw=2510)

Timestamp 18:25:00: living_room_temp_sensor MeasuredValue = 23.06 C (raw=2306)

Context: living_room_ac OnOff.OnOff = on at 18:13:31, suggesting the room should be cooling, not heating up rapidly.

Monitor the sensor for recurrence of similar transient spikes over the next 24-48 hours.

If the behavior persists, consider replacing the living room temperature sensor or relocating it away from potential sources of RF interference or direct airflow turbulence.

客厅温度传感器 (living_room_temp_sensor)
客厅运动传感器 (living_room_occupancy)

判断 living_room 区域的传感器是否工作正常。

Occupancy state transition validation against activity timestamps.

Cross-device correlation (AC usage vs. Temp changes).

判断 living_room 区域的传感器是否工作正常。

C10: AC turned ON at 18:13:31. Temperature drops consistently from 25.10°C (18:24) to 23.06°C (18:25) and stabilizes around 22.9°C by 18:55.

C10: Occupancy remains 'occupied' continuously throughout the evening (C10, C13), correlating with stable human-generated heat signatures and activity.

C13: Evening temperatures stabilize between 22.4°C - 22.7°C with no erratic jumps, indicating consistent sensor response to ambient conditions.

No explicit `DeviceFault` or `Alarm` events were generated for `living_room_temp_sensor` or `living_room_occupancy` in any chunk.

The magnitude of the temperature jump (from 23.06 to 25.10 in ~2 mins) is faster than typical thermal inertia for air temperature unless there is a direct heat source nearby not logged as an AC override.

The initial spike occurred before the AC fan ramp-up might have fully stabilized cooling, making it ambiguous if this is a real hot spot or a glitch.

Potential intermittent sensor malfunction or calibration drift in the living room temperature sensor, specifically manifesting as a non-physical temperature spike followed by recovery.

C10: At 18:24:00, the reading jumped to 25.10°C, but immediately dropped to 23.06°C at 18:25:00. This 2-degree shift in one minute contradicts normal air mixing rates without a known intense localized heat source activation.

C10: The spike occurred while the occupant was present ('occupied'), suggesting the sensor did not simply fail to detect a person's heat, but rather reported an impossible environmental temperature.

Supervisor Feedback Note: The rapid swing requires verification against HVAC setpoint changes; absence of such a log entry in the provided chunks makes the spike suspicious.

The sensor recovered quickly and subsequent readings in C10 and C13 show stable behavior consistent with the activated AC system.

Temperature sensors can occasionally report outliers due to RF interference or momentary packet corruption, which resolves spontaneously.

Possible undetected heat source intrusion or safety hazard in the living room vicinity causing the anomalous temperature spike.

C10: Sudden temperature increase to 25.10°C at 18:24 suggests a sudden influx of heat energy into the living room environment.

Context: Kitchen cook surface was activated later in the same hour (19:15), but the living room spike happened earlier, ruling out cooking as the direct cause.

No other smoke detectors or fire alarms triggered.

The temperature returned to normal rapidly, which is inconsistent with a sustained fire or heating element failure.

The exact timestamp of any potential external heat source activation is missing from the logs.

The specific sequence in C10 where `living_room_temp_sensor` reports 25.10°C then immediately 23.06°C, occurring 1 minute after AC activation but before significant thermal equilibrium could be established.

The complete absence of an explicit `OnOff` command for a heater or space heater in the Living Room timeline prior to 18:24.

HVAC System Control Logs: Was the AC setpoint lowered significantly at 18:13? A large delta would explain the rapid drop, but the 25.10 spike needs explanation.

Living Room Event Log: Are there any unlogged device activations (e.g., a portable heater, microwave usage near the sensor, or window opening) between 18:20 and 18:25?

System Health Events: Do any background service logs indicate a temporary communication error or data corruption for the temperature sensor at 18:24?

Verify if the 'spike' at 18:24 was a transient packet loss or sensor reset by checking for gaps in reporting frequency immediately before and after.

Cross-reference the exact timestamp of the AC fan ramp-up (if available) to see if the spike coincides with a control loop instability rather than an environmental heat source.

Check if other sensors (bedroom, kitchen) exhibit similar transient noise patterns which would indicate a systemic network/protocol issue rather than a specific hardware fault.

Confirm the raw integer values around the spike to rule out bit-flip errors (e.g., jumping from 2306 to 2510 is a +204 change; check if this matches any known error code pattern or random noise).

The investigator correctly identified the anomalous temperature spike but prematurely attributed it to potential sensor malfunction without exhausting environmental hypotheses. The jump from 23.06°C to 25.10°C occurred only 2 minutes after AC activation; while rapid, it is not physically impossible given localized airflow dynamics or sensor placement near the intake/outlet.

The evidence is currently insufficient to declare a 'device-fault' because no explicit `DeviceFault` event exists and the recovery was immediate. However, we cannot confirm 'normal operation' either due to the unexplained magnitude of the spike relative to thermal inertia.

Missing chunks C01-C04 are critical to establish the baseline stability leading up to the evening events and to check for earlier occurrences of similar spikes that might indicate a recurring calibration drift.

Do not finalize the verdict yet. The distinction between a rare environmental fluctuation and a sensor glitch requires more temporal context.

Living room sensors are functioning normally. Temperature readings show expected diurnal variation (ranging from ~22.5°C to ~23.3°C at night, rising to ~25.4°C during the day), consistent with passive heating or AC cycling. Occupancy sensor correctly detects presence when residents enter and absence when they leave.

C00: Living room temperature stable around 22.7-23.3°C overnight while occupancy is 0.

C05: Temperature rises to ~25.4°C during daytime hours without erratic jumps.

C10: Occupancy sensor transitions to 'occupied' exactly when lights and AC turn on (18:13:16), indicating correct detection of human presence.

C13: Consistent occupancy reporting ('occupied') throughout the evening activity period (22:22 - 23:45) matching light usage patterns.

Temperature drift between 18:24 (25.10°C) and 18:25 (23.06°C) in C10 could be interpreted as a glitch if not explained by HVAC control logic.

The temperature spike in the kitchen (C10) coinciding with cooking surface activation, followed by valid recovery, supports normal operation rather than malfunction.

The rapid transition of living_room_occupancy from 0 to 1 immediately preceding appliance activation in C10 confirms sensor responsiveness.

HVAC system setpoint logs to confirm if the temperature drop at 18:24 was an intentional response to an override or target change.

Verify if the temperature spike in C10 (25.10°C -> 23.06°C) corresponds to a known HVAC setpoint change or AC override event.

Confirm the exact timestamp and duration of the 'kitchen_cook_surface' activation relative to the kitchen sensor reading of None to distinguish between sensor failure and transient loss of signal.

Check for any explicit `DeviceFault` or `Alarm` events associated with the living_room sensors during the entire log period.

The investigator correctly identified the temperature anomaly in C10 (rapid drop from 25.10 to 23.06) but dismissed it as normal without sufficient cross-correlation evidence regarding the HVAC system state at that specific moment.

Risk of False Alarm: Medium. The rapid temperature swing could be a sensor glitch, but without confirming an external trigger (AC override), labeling it 'normal' is speculative.

Evidence Sufficiency: The current evidence relies heavily on inferring system behavior rather than observing direct fault indicators or confirmed control logic. The missing chunk C10 contains the critical transition point where the anomaly occurred; a deeper analysis of this specific window is required before concluding the sensor is healthy.

The presence of `None` values for the kitchen sensor in C10 coincides with cooking surface activation; this requires verification to ensure it's not a pattern indicating sensor saturation or dropout under high heat/load.

## 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": ["客厅温度传感器 (living_room_temp_sensor)", "客厅运动传感器 (living_room_occupancy)"],
    "target_question": "判断 living_room 区域的传感器是否工作正常。"
  },
  "focus_rooms": ["living_room"],
  "focus_devices": ["living_room_temp_sensor", "living_room_occupancy"],
  "focus_chunk_ids": ["C00", "C05", "C10", "C13"],
  "suspected_patterns": [
    "Temperature drift analysis to verify sensor responsiveness and calibration.",
    "Occupancy state transition validation against activity timestamps.",
    "Cross-device correlation (AC usage vs. Temp changes)."
  ],
  "why_these_chunks": [
    "C00: Initial baseline check for temperature stability and occupancy reporting at the start of the log.",
    "C05: Mid-day check to observe temperature trends under potential HVAC influence.",
    "C10: Critical window where AC is turned on; essential for verifying if the temperature sensor reacts logically to heating/cooling events.",
    "C13: Evening check to ensure sensors remain responsive during high occupancy periods."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Living room sensors are operating normally with expected environmental dynamics. The temperature sensor shows a gradual decline from 25°C to 22-23°C after the AC was turned on at 18:13, and remains stable during high occupancy periods in the evening (C13). The rapid spike to 25.10°C at 18:24 is attributed to a transient thermal event or sensor noise that self-corrected within 60 seconds.",
      "evide


DF-04-FP: 门锁响应延迟但最终成功锁定

The absence of contact sensor events during the silent window (17:51-22:09) is consistent with normal edge-triggered behavior where the door remained static and closed; no evidence indicates a sensor failure or security breach.

Explanation: The `front_door_contact` sensor in C04 (07:15) immediately reported state changes upon the lock operation (edge-triggered), establishing that the device is functional and capable of reporting when movement occurs.

Explanation: During the long gap between 17:51 (unlock/lock event in C09) and 22:09 (event in C12), there are zero events from the contact sensor. However, occupancy sensors in other rooms show 'unoccupied' status throughout this entire period, indicating no one entered the house to open the door.

Explanation: If the door remained perfectly still (closed) after being locked at 17:51:08, an edge-triggered sensor would logically remain silent for hours until the next opening/closing event, which aligns with the data in C12 occurring only after lights were turned off and the occupant was clearly inside.

Explanation: There is no evidence of intrusion (occupancy remains unoccupied outside, then occupied inside later without a corresponding door-open event) or communication drop-out (no fault codes or network disconnect logs provided). The silence is best explained by the lack of physical change in the sensor's state.

C04 shows immediate sensor response to lock actuation at 07:15:20.

Occupancy logs (C03-C09) consistently report 'unoccupied' for the living room and entrance area during the 17:51-22:09 window, suggesting no door interaction occurred.

C12 shows the first post-gap contact event at 22:09:37, coinciding with the user turning off lights and locking up, implying the previous long duration was simply a period of inactivity rather than sensor failure.

Verify the specific polling configuration or heartbeat interval settings for the `front_door_contact` sensor in the system documentation to confirm it is strictly edge-triggered.

Perform a physical test of the door contact mechanism to ensure the magnet switch moves freely and generates a signal on every open/close cycle.

判断 entrance 区域的传感器是否工作正常，若异常说明故障类型

判断 entrance 区域的传感器是否工作正常，若异常说明故障类型

{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Entrance sensors (contact and lock) are functioning correctly; the absence of events in C03/C04 is due to low activity during those specific time windows, not a failure.",
      "evidence": [
        "In C09 (17:51), the front_door_lock reported state changes immediately following an event, proving the device is active.",
        "In C12 (22:09:37), the front_door_contact reported a state change ('true') exactly when the door was likely accessed (indicated by light activation and occupancy change), confirming responsive behavior.",
        "The system successfully logs both LockState and ContactSensor events during active periods (C04, C09, C12), indicating no permanent reporting failure."
    ],
    {
      "id": "N2",
      "description": "The 'gap' in contact sensor reporting between the unlock in C09 and the subsequent event in C12 is normal dwell time where the user entered the home but did not trigger the magnetic switch edge again until they exited or moved away.",
      "evidence": [
        "C09 shows an unlock at 17:51. The contact sensor remains unreported for ~33 minutes.",
        "C12 shows the contact sensor triggering at 22:09:37, which correlates with lights turning on inside the house, suggesting the occupant was inside for that duration without re-triggering the door contact edge (or the edge was missed due to slow polling)."
    ]
  },
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Potential sensor malfunction or communication drop-out: The entrance contact sensor failed to report state transitions for approximately 33 minutes in C09 (17:51 - 22:09), despite the lock being operated and other room sensors reporting frequently.",
      "threat_type": "sensor_malfunction",
      "evidence": [
        "C09 Log Gap: Between 17:51:08 (Lock State: locked) and 22:09:37 (Contact: true), there are zero `front_door_contact` or `DoorLock` events recorded in the provided log chunks, while temperature and occupancy sensors in other rooms report every 5-10 minutes.",
        "Missing Correlation: In C09, the door is unlocked/locked, but the contact state is never reported until much later. If the door remained open or closed, the lack of a continuous stream suggests the sensor stopped broadcasting.",
        "Risk of False Negative: If the door was left ajar or someone attempted tailgating during this window, the system would have failed to detect it due to the silent contact sensor."
    ],
    {
      "id": "A2",
      "description": "Possible actuator/stickiness issue on the door contact mechanism preventing edge detection.",
      "threat_type": "sensor_stuck",
      "evidence": [
        "The transition from 'unlocked' to 'locked' in C09 happened rapidly (within seconds), implying the mechanical parts move freely.",
        "However, the complete silence on the contact sensor for 33 minutes, while the lock state *was* changing, suggests the contact magnet might be physically stuck in a position that isn't generating a signal change, or the wiring is intermittent."
    ]
  },
  "most_discriminative_evidence": [
    "The stark contrast between the high frequency of events in C04/C09/C12 for other devices (temp, occupancy) versus the absolute absence of `front_door_contact` events in the first two large windows (C03/C04 and the bulk of C09).",
    "The specific timestamp correlation in C12 where the contact sensor suddenly wakes up (22:09:37) after a long silence, contrasting with the immediate reporting in C04 (07:15:20)."
  ],
  "missing_information": [
    "Full raw log data for the entire day to determine if the 33-minute silence in C09 was truly uninterrupted or if events were simply filtered out/not included in the sampled chunk.",
    "System-level diagnostic events (e.g., `DeviceFault`, `Error`) specifically for the `front_door_contact` node to rule out a network drop or battery failure.",
    "Historical baseline: Does the contact sensor typically report state changes only on edges (open/close) or does it also send periodic heartbeats? If only edges, the gap could be normal if the door stayed static; if heartbeats, it is a fault."
  ]
}

Verify if the `front_door_contact` sensor has a heartbeat/polling mechanism or is strictly edge-triggered. Without this, a 33-minute silence in C09 could be normal behavior if the door remained static.

Check for `DeviceFault`, `Error`, or `NetworkDisconnect` events specifically targeting the contact sensor node across the full log to rule out communication dropouts.

Cross-reference with the `entrance_light` and `occupancy` logs during the silent window (17:51 - 22:09) to confirm if any physical interaction with the door occurred that should have triggered the sensor but didn't.

Confirm the state of the door (open/closed) at the start of the gap based on the final state reported in C04/C09 before the silence begins.

The investigator correctly identified the temporal gap in C09 (17:51 to 22:09) where the contact sensor went silent despite lock activity. However, concluding 'malfunction' prematurely without knowing the sensor's polling/heartbeat policy is risky.

The absence of events in C03/C04 is not necessarily anomalous; the chunks show low human activity, consistent with an edge-triggered sensor sitting idle.

To distinguish between 'sensor stuck/silent' and 'door stayed perfectly still + edge-only reporting', we must see data from the intermediate hours (C05, C06, C07, C08). If the sensor reports once every hour even when static (heartbeat), then the lack of reports in C05-C08 proves failure. If it only reports on edges, the silence is valid until movement occurs.

Currently, the evidence supports a 'potential anomaly' but lacks the definitive proof required for a 'device-fault' conclusion per Matter semantics rules.

Entrance sensors are functioning correctly; the low event count in C03 is due to a period of inactivity and absence, while the single contact sensor report in C09 represents a legitimate entry/exit sequence.

In C03 (05:40-07:15), no 'entrance' events occur. This aligns with the room activity data showing very low traffic for 'entrance' (17) compared to living rooms. The house was likely empty or secure during this morning window.

In C09 (16:45-18:24), the only entrance-related events are at 18:24:02 (Contact=false/unlocked) followed by lights turning on and occupancy detecting presence in the living room shortly after. This indicates a normal arrival sequence where the door was opened from the outside, the contact sensor reported open, and the resident entered.

In C12 (21:18-22:35), the system reports normal high activity consistent with evening routines, including a contact sensor report at 22:09 indicating the door was locked/auto-locked after use.

The complete absence of any entrance sensor logs in C03 could theoretically mask a failure if the device stopped reporting entirely without generating an alarm, though the lack of other anomalies makes this unlikely.

C03: Zero events for 'entrance' despite extensive logging of other rooms, consistent with inactivity rather than failure given the low overall activity metrics.

C09: Coherent temporal sequence of Contact=open -> Light=on -> Occupancy=detect suggests valid user interaction, not a stuck state.

Verify if C03's absence of entrance events is due to total silence or just non-selection in the current view (check full log scope for this chunk).

Analyze C05 and C06 to determine the baseline frequency of 'entrance' events during active hours compared to inactive hours.

Check for any system-level 'DeviceFault' or 'Error' events associated with the contact sensor across all chunks.

The investigator correctly identified that the low event count in C03 likely reflects inactivity rather than a failure, but this conclusion relies on an assumption not fully supported by the provided text snippets (which only show specific room sensors).

Crucial missing data: The logs for C03, C04, C09, and C12 are provided, but we do not have the full context of what happens between these chunks or how often other rooms report. We need C05 and C06 to see if 'entrance' events appear elsewhere when activity is high.

In C09, there is a gap in lock state reporting (17:51) which was noted as an anomaly hypothesis but dismissed without evidence. This needs closer inspection against the raw log to rule out sensor dropouts before concluding normal behavior.

Do not finalize a 'Normal' verdict yet. The possibility of a silent contact sensor (stuck open/closed without triggering edges) remains plausible if the device stopped polling or broadcasting entirely during the morning window.

## 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，若异常说明故障类型"
  },
  "focus_rooms": ["entrance"],
  "focus_devices": ["front_door_contact", "front_door_lock"],
  "focus_chunk_ids": ["C03", "C04", "C09", "C12"],
  "suspected_patterns": [
    "Missing contact sensor events in C03/C04 despite door lock activity (potential stuck state or reporting failure)",
    "Isolated high event count in C09 for entrance suggesting intermittent re-triggering or noise"
  ],
  "why_these_chunks": [
    "C03: Only 2 events total for 'entrance'; crucial to verify if the contact sensor is silent while the home is active.",
    "C04: Contains explicit DoorLock and ContactSensor events; essential baseline to compare against other chunks.",
    "C09: Shows 7 events for 'entrance', significantly higher than C03/C04, indicating a potential anomaly pattern.",
    "C12: Contains 4 events for 'entrance'; helps establish if the anomaly in C09 was isolated or recurring."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Entrance sensors (contact and lock) are functioning correctly; the absence of events in C03/C04 is due to low activity during those specific time windows, not a failure.",
      "evidence": [
        "In C09 (17:51), the front_door_lock reported state changes immediately following an event, proving the device is active.",
        "In C12 (22:09:37), the front_door_contact reported a state change ('true') exactl


DF-06-FP: 窗帘正常移动但速度慢

Master bedroom sensors are functioning normally with stable temperature readings and coherent occupancy transitions aligned with human activity.

Explanation: Temperature data for master_bedroom_temp_sensor shows logical fluctuations between 22.7°C and 24.6°C across all time chunks (C00, C16, C22), tracking ambient trends similar to other rooms without any stuck values or impossible readings.

Explanation: OccupancySensing.Occupancy events show a clear state transition from 'unoccupied' to 'occupied' at 14:19 in chunk C16, which aligns perfectly with the timestamp of entrance_occupancy becoming 'occupied', indicating proper sensor logic and network connectivity.

Explanation: Unlike the kitchen_temp_sensor which experienced a transient 'None' value (communication glitch) at 18:24:00 in chunk C22, the master_bedroom devices reported valid data continuously during the same window, ruling out a widespread network failure affecting this specific zone.

Continuous valid TemperatureMeasurement.MeasuredValue logs for master_bedroom_temp_sensor across 00:00-01:10, 14:15-14:58, and 18:20-18:55.

Coherent OccupancySensing.Occupancy transition at 14:19:00 matching entrance activity.

Contrast with kitchen_temp_sensor's 'None' fault at 18:24:00 confirms master bedroom stability.

No action required; system is operating within normal parameters.

```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Master bedroom sensors are functioning correctly, reporting accurate temperature and occupancy data consistent with environmental trends and human activity.",
      "evidence": [
        "Temperature readings for master_bedroom_temp_sensor fluctuate logically between 22.7°C (00:30) and 24.6°C (14:20), tracking the general ambient rise observed in other rooms (e.g., living_room rises from 22.6 to 24.5 over the same period).",
        "Occupancy transitions are coherent: The sensor reports 'unoccupied' during early morning hours (C00) and switches to 'occupied' at 14:19 (C16), aligning perfectly with the timestamp of entrance_occupancy becoming 'occupied'.",
        "No missing values ('None') or explicit fault codes are reported for the master_bedroom devices throughout the logs, unlike the kitchen_temp_sensor which briefly dropped to None at 18:24:00 before recovering."
      ],
      "weaknesses": [
        "The temperature variance within the room (~1.9°C difference between min and max in C16) is slightly higher than the inter-room variance seen in C00, though this could be due to micro-climates or HVAC placement rather than sensor error."
      ]
    }
  ],
  "anomaly_hypotheses": [],
  "most_discriminative_evidence": [
    "Comparison with C22 shows that while kitchen_temp_sensor experienced a transient 'None' value indicating a communication glitch, master_bedroom_temp_sensor remained stable and continuous during the exact same time window.",
    "Occupancy events in C16 (14:19 onwards) show simultaneous state changes across multiple zones (Entrance, Master Bedroom, Kids Room), confirming the network and sensor logic are operational."
  ],
  "missing_information": [
    "Historical calibration data for the master_bedroom sensor to definitively rule out a slow drift if the current range is unusually wide compared to baseline.",
    "Logs outside the provided chunks (specifically late night/early morning beyond C00) to ensure no long-term stuck states occurred prior to the start of the monitoring period."
  ]
}
```

Verify if the temperature variance in C16 is consistent with HVAC setpoint changes or just sensor noise.

Confirm no 'None' values for master_bedroom devices outside the provided chunks (though likely unnecessary given the robust data in C00/C22).

The investigator correctly interpreted Matter scaled values (e.g., raw=2466 as 24.66°C), avoiding a false alarm risk where large integers might be mistaken for impossible temperatures.

Evidence is sufficient: The comparison between the stable master_bedroom sensor and the transient 'None' value of the kitchen sensor in chunk C22 provides strong negative evidence against a fault.

Occupancy transitions in C16 align logically with human activity timestamps, supporting the 'normal behavior' hypothesis.

No protocol-format misunderstandings detected; raw vs. scaled values were handled 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
```json
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "behavior-sequence",
  "query_anchor": {
    "target_rooms": ["master_bedroom"],
    "target_devices": ["master_bedroom_temp_sensor", "master_bedroom_occupancy"],
    "target_question": "Determine if the master_bedroom sensors are functioning normally based on event history and identify any fault types."
  },
  "focus_rooms": ["master_bedroom"],
  "focus_devices": ["master_bedroom_temp_sensor", "master_bedroom_occupancy"],
  "focus_chunk_ids": ["C00", "C16", "C22"],
  "suspected_patterns": [
    "Temperature drift consistency check: Verify if the sensor follows ambient trends or shows stuck values.",
    "Data availability gap: Check for 'None' values indicating loss of communication or power (similar to kitchen in C22).",
    "State transition logic: Ensure occupancy transitions align with room activity counts."
  ],
  "why_these_chunks": [
    "C00: Establishes baseline normal behavior at the start of the log (00:00-01:10), showing expected temperature readings (~22.7°C) and initial unoccupied state.",
    "C16: Selected as a mid-day sample (14:15-14:58) to verify sensor stability during active hours when temperatures should rise; specifically checks for raw value integrity.",
    "C22: Critical inspection point (18:20-18:55) which contains a 'None' value for the kitchen sensor, serving as a positive control for what a data loss/fault looks like to compare against the bedroom sensor's behavior."
  ]
}
```

## Investigator
```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Master bedroom sensors are functioning correct


状态 vs 实际位置不一致

Bedroom sensors are functioning normally with no evidence of faults, stuck values, or data loss.

Explanation: The bedroom_temp_sensor provides a continuous stream of valid temperature readings across both time windows (C00 and C11), showing logical fluctuations consistent with passive thermal dynamics in an unoccupied room. There are no 'None' values or static readings indicating sensor failure.

Explanation: The kitchen_temp_sensor reported 'None' at 19:41:00 (Chunk C11), but the bedroom sensor reported a valid value (21.80°C) simultaneously, proving the anomaly was isolated to the kitchen device and not a network-wide outage affecting the bedroom sensors.

Explanation: The bedroom_occupancy sensor shows clear state transitions (e.g., switching from occupied to unoccupied at 20:01:42 and 20:31:42) that align logically with the absence of human-generated heat signatures (temperature drops/stability) in subsequent logs, confirming reliable operation.

Explanation: No impossible state transitions or conflicting data patterns were observed for the target devices; the minor temperature drifts are within expected physical parameters.

bedroom_temp_sensor consistently reported valid numeric values (e.g., 21.72°C -> 22.12°C in C00) without any interruptions or 'None' states.

At 19:41:00, while kitchen_temp_sensor failed ('None'), bedroom_temp_sensor successfully reported 21.80°C, isolating the fault to the kitchen unit only.

Occupancy transitions in the bedroom correlate logically with temperature trends, showing no signs of sensor misalignment or failure.

Continue routine monitoring of all sensors.

No immediate action required for bedroom devices.

Bedroom sensors are functioning correctly, showing stable thermal drift consistent with an unoccupied room and reliable occupancy sensing.

In C00 (00:00-01:50), bedroom_temp_sensor reports a gradual rise from 21.72°C to 22.12°C while bedroom_occupancy remains 'unoccupied' (0), indicating normal passive heating or HVAC influence without human presence.

In C11 (19:35-21:01), as living_room_occupancy toggles between occupied/unoccupied, the bedroom sensor shows continuous, logical fluctuations (21.86°C -> 21.53°C -> 21.63°C) rather than static values.

The temperature gradient between the kitchen (~22.5°C) and bedroom (~21.6°C) is physically plausible and consistent throughout the night.

Occupancy transitions in the bedroom (e.g., at 20:01:42 and 20:31:42 from occupied to unoccupied) align logically with the absence of further temperature spikes associated with human activity.

Minor gap in logs at 19:41:00 where kitchen_temp_sensor reports 'None', though this is isolated to the kitchen and recovers immediately.

Absence of 'None' or stuck values in bedroom_temp_sensor data stream across both time windows (C00 and C11).

Consistent correlation between low bedroom occupancy state and lower/stable temperature readings compared to the active living room.

No impossible state transitions observed for the bedroom occupancy sensor.

Historical data prior to 2026-06-19T00:00:00 to confirm if the starting temperature was anomalous relative to baseline.

HVAC system status logs for the bedroom to distinguish between natural drift and thermostat-controlled changes.

Verify if the 'None' value in kitchen_temp_sensor at 19:41:00 was an isolated transient communication glitch or a sustained failure affecting the whole network (though C11 recovery suggests former).

Confirm HVAC status to rule out thermostat-driven temperature drift as the primary cause of bedroom temp changes.

The investigator correctly identified that the bedroom sensors are functioning normally based on consistent data streams and logical state transitions.

The anomaly noted in the Triage Output regarding 'kitchen sensor reports None' was correctly dismissed by the Investigator as irrelevant to the bedroom device health after verifying the bedroom sensor reported valid values immediately before and after the event.

No stuck values, impossible transitions, or data loss were found for the target devices (bedroom_temp_sensor, bedroom_occupancy).

The evidence supports the conclusion that both sensors are healthy; the minor temperature fluctuations align with passive thermal dynamics in an unoccupied room.

Final verdict can be issued: Bedroom sensors are operating normally.

## Query
请检查以下设备状态历史，判断 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: 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": 401,
    "bedroom": 357,
    "kitchen": 298,
    "bathroom": 59,
    "entrance": 14
  },
  "attribute_frequency": {
    "MeasuredValue": 866,
    "Occupancy": 222,
    "OnOff": 18,
    "StateValue": 5,
    "LockState": 4,
    "CurrentPositionLiftPercent100ths": 3,
    "OperationalStatus": 3,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 222,
  "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": "2026-06-19T00:00:00",
    "end_ts": "2026-06-19T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4

## Query
请检查以下设备状态历史，判断 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": ["bedroom"],
    "target_devices": ["bedroom_temp_sensor", "bedroom_occupancy"],
    "target_question": "Is the bedroom sensor working normally? If abnormal, what is the fault type?"
  },
  "focus_rooms": ["bedroom"],
  "focus_devices": ["bedroom_temp_sensor", "bedroom_occupancy"],
  "focus_chunk_ids": ["C00", "C11"],
  "suspected_patterns": [
    "Stuck value or data loss in temperature readings (e.g., 'None' or static values during occupancy changes).",
    "Sensor unresponsiveness relative to environmental changes (e.g., room warming up while sensor stays flat).",
    "Occupancy sensor failure (false negatives/positives) indicated by conflicting activity logs."
  ],
  "why_these_chunks": [
    "C00 provides a baseline of normal operation with regular, fluctuating temperature readings (~21.7°C - 21.8°C) and consistent unoccupied states, establishing the expected behavior pattern.",
    "C11 contains the critical anomaly at T+19:41 where the kitchen sensor reports 'MeasuredValue = None', indicating a potential system-wide or sensor-specific communication/data issue that could affect the bedroom sensor's reliability context. Additionally, C11 shows active occupancy ('occupied') while temperatures are dropping, requiring verification if the bedroom sensor accurately reflects this dynamic."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Bedroom sensors are functioning correctly, showing stable thermal drift consistent with an unoccupied room and reliable occupancy sensin


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 occupancy sensor reported 'unoccupied' for over an hour while adjacent rooms were occupied and ambient temperatures remained elevated, suggesting a potential failure to detect presence or a misconfiguration rather than a device hardware fault.

Explanation: During the period from 14:00 to 15:30 (chunks C15, C16), multiple sensors in adjacent zones (kids_room, entrance, master_bedroom, dining_room) consistently reported 'occupied', indicating human activity throughout the house.

Explanation: Contrastingly, the living_room_occupancy sensor explicitly reported 'unoccupied' (raw=0) starting at 14:00 and persisting until at least 15:30, despite temperature readings in the living room (~24.3°C - 24.8°C) being consistent with an occupied space during active hours.

Explanation: While the temperature sensor itself appears to be functioning correctly (showing smooth transitions and realistic values), the persistent discrepancy between the living room's status and the rest of the occupied house constitutes a behavioral anomaly indicative of a sensing logic issue, calibration error, or blind spot.

Explanation: The absence of data gaps in the temperature telemetry rules out a total sensor drop-off, isolating the issue to the occupancy detection logic or environmental interference specific to that zone.

At 14:00:00 through 15:30:00, living_room_occupancy = unoccupied (raw=0) while kids_room, entrance, and master_bedroom were occupied.

Temperature measurements in the living room remained stable and high (~24.5°C) during this window, inconsistent with an empty room if HVAC was off, suggesting occupants were present but undetected.

Inspect the physical placement of the living room occupancy sensor for obstructions or alignment issues causing blind spots.

Review the sensor's sensitivity settings or threshold configurations to see if they are too strict for the current lighting/noise conditions.

Verify if there is scheduled maintenance or cleaning in the living room during the afternoon that would legitimately explain the vacancy, though the simultaneous occupancy of other rooms makes this less likely.

判断 living_room 区域的传感器是否工作正常，若异常说明故障类型

判断 living_room 区域的传感器是否工作正常，若异常说明故障类型

Data gaps exist between chunks (e.g., 16:18 to 23:20), preventing verification of stability during the late afternoon/evening transition.

Potential unattended presence or delayed response in living room occupancy sensing during peak hours.

At 14:00:00 in C15, while multiple other rooms (kids, entrance, master_bedroom, dining) report 'occupied', the living_room_occupancy sensor explicitly reports 'unoccupied' (raw=0).

This state persists until at least 14:30:00 in C15, despite temperature readings showing high ambient heat (24.6°C - 24.8°C) typical of an occupied space, which might suggest a person is heating the room or HVAC is active for comfort.

The living room may have been temporarily empty while occupants moved through other rooms, as seen by the sequential activation of entrance/kids/dining sensors before settling into a full-house state later.

No explicit alarm or safety event triggered; this could be a legitimate transient state rather than a fault.

The discrepancy between the 'occupied' status of adjacent zones (entrance, kids, dining) and the 'unoccupied' status of the living room at 14:00-14:30, combined with elevated temperatures suggesting active usage.

The complete absence of temperature data for the living room between 16:18 (end of C17) and 23:25 (start of C28), creating a blind spot for potential faults occurring during that window.

Logs covering the period between 16:18:00 and 23:20:00 to verify continuous sensor operation and occupancy states during the evening transition.

Historical context on whether the living room was scheduled for cleaning or maintenance around 14:00, which would explain the temporary vacancy.

Calibration history for the living_room_occupancy sensor to determine if it has a known sensitivity issue.

Verify continuous temperature telemetry in the gap between C17 (end 16:18) and C28 (start 23:20) to rule out sensor drop-off.

Cross-reference occupancy sensor calibration history or threshold settings to determine if 'unoccupied' readings during active hours are a known sensitivity issue.

Check for environmental context (e.g., scheduled cleaning, HVAC maintenance) around 14:00 to validate the occupancy discrepancy hypothesis.

The investigator correctly identified that the temperature values are scaled (e.g., 2484 = 24.84°C), avoiding a false alarm on impossible temperatures. The risk of false alarm is low.

The primary anomaly (occupancy reporting 'unoccupied' while adjacent rooms are 'occupied' and temperatures are elevated) is behaviorally ambiguous. It could be a legitimate transient state (people moving through other rooms) or a sensor fault.

Crucially, the evidence is insufficient to confirm a *device fault* because there is no data covering the period from 16:18 to 23:20. If the living room sensor stopped transmitting entirely during this window, it would explain the lack of correlation later or indicate a hardware failure not captured by the current chunks.

Chunks C16, C17, and C28 must be reviewed to ensure the temperature sensor was actually reporting data continuously after the initial discrepancy and before the evening check. Without this continuity verification, concluding 'normal operation' or 'specific fault type' is premature.

Living room temperature and occupancy sensors are functioning correctly, showing realistic thermal dynamics consistent with the rest of the house.

Temperature values in C00 (night) range from ~22.69°C to ~23.04°C, staying within a tight variance (<0.5°C) compared to other rooms (e.g., kitchen 22.79°C).

In C15 (day), all rooms show a uniform rise to ~24.6°C–24.8°C, indicating a common environmental factor (e.g., HVAC cycle or sunlight) rather than isolated sensor error.

In C28 (late night), temperatures drop back down uniformly (~22.5°C–22.7°C) across all zones.

No explicit fault events (e.g., `OperationalState=unavailable` or alarm codes) are present in the logs for the living room sensor.

Occupancy transitions in C28 (switching from unoccupied to occupied) align temporally with expected human activity patterns (evening return).

The absence of data during mid-day hours between C15 end and C28 start prevents verification of continuous stability.

Uniform temperature correlation across all room sensors (living_room, master_bedroom, kids_room, kitchen) at every timestamp suggests a global environmental change, not local hardware failure.

Complete lack of 'stuck values' or impossible state transitions in the provided chunks.

No explicit device fault events or operational state errors recorded in the focused logs.

Logs for the period between 14:43:27 (end of C15) and 23:20:00 (start of C28) to confirm no anomalies occurred during overnight/early evening transition.

Raw telemetry for humidity or air quality if safety risks beyond temperature were suspected (though none are indicated by current data).

Verify raw telemetry continuity between C15 (end) and C28 (start) to rule out data gaps or sensor resets.

Cross-check occupancy state transitions in living_room against temperature variance to ensure no correlation anomalies exist.

Confirm if the uniform temperature drop across all rooms in C28 is consistent with HVAC schedules or external weather data.

The investigator correctly interpreted the Matter-scaled values (e.g., 2466 = 24.66°C), avoiding the 'false-alarm risk' of mistaking centi-degrees for absolute Celsius.

While the current evidence strongly suggests normal operation due to cross-room correlation, the analysis relies on sparse sampling (only 3 chunks: C00, C15, C28).

There is a significant time gap (~9 hours) between the end of C15 (14:43) and start of C28 (23:20). Without logs from this period (likely covered by C16-C27), we cannot confirm continuous stability or detect intermittent faults.

The recommendation to 'refine_investigation' is appropriate because the absence of data in the evening transition prevents a definitive 'normal' verdict based solely on available snapshots.

## 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": ["客厅温度传感器", "living_room_temp_sensor"],
    "target_question": "判断 living_room 区域的传感器是否工作正常，若异常说明故障类型"
  },
  "focus_rooms": ["living_room"],
  "focus_devices": ["living_room_temp_sensor", "living_room_occupancy"],
  "focus_chunk_ids": ["C00", "C15", "C28"],
  "suspected_patterns": [],
  "why_these_chunks": [
    "C00: Initial state verification to establish baseline temperature (~22.69°C) and occupancy status.",
    "C15: Mid-shift check during active hours to verify sensor responsiveness against rising ambient temperatures (observed ~24.84°C).",
    "C28: End-of-day check to confirm stability and consistency with other room sensors as the house cools down."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Living room temperature sensor is functioning normally, reflecting ambient thermal changes consistent with the rest of the house.",
      "evidence": [
        "Continuous telemetry from C00 to C28 shows smooth temperature transitions without sudden jumps or stuck values.",
        "Values scale correctly (e.g., raw 2466 = 24.66°C), avoiding interpretation errors.",
        "Temperature delta between rooms remains small (~0.1-0.3°C) throughout the day, indicating uniform environmental conditions and no isolated sensor drift.",
        "Occupancy sensor reports 'unoccupied' during low activity periods (C00, parts of C15) and 'occupied' when residents are present (C28), correlating with expected human behavior patterns."