温度传感器读数漂移（drift）

温度读数持续单向偏移，不符合正常日间温度曲线 其他传感器或空调自带温度显示不一致（如果可以交叉验证）

The living room temperature sensor exhibits intermittent data loss (None values) occurring periodically throughout the log, indicating a communication or hardware fault despite eventual recovery.

Explanation: Analysis of C04 reveals a missing `TemperatureMeasurement.MeasuredValue` at 08:00:00 immediately following a valid reading at 07:55:00, establishing an initial anomaly pattern for the device.

Explanation: In C05, a second instance of data loss occurs at 09:00:00. Unlike the previous event which had no immediate trigger in the preceding chunk, this event coincides exactly with a timestamp where other sensors reported valid data, suggesting a specific transmission failure rather than a global system outage.

Explanation: In C11, a third instance of data loss is recorded at 20:00:00. This event is notable because it appears twice within a single minute (once at :00 and once implicitly as a gap before the :05 reading), and correlates temporally with the kitchen dishwasher turning on at 20:13:03, though the gap itself predates the appliance activation. The recurrence of these 'silent' drops across different time windows and contexts points to a persistent instability in the sensor's reporting mechanism.

Explanation: While the temperature values when present remain within realistic ranges and follow expected physical trends (e.g., cooling down at night in C09/C10), the presence of repeated `None` readings constitutes a functional anomaly defined by the lack of continuous operational data.

[2026-06-01T08:00:00] living_room_temp_sensor | (Missing Value)

[2026-06-01T09:00:00] living_room_temp_sensor | (Missing Value)

[2026-06-01T20:00:00] living_room_temp_sensor | (Missing Value)

Inspect the living room temperature sensor's physical connection and power supply for stability.

Check local network logs around the timestamps of the gaps (08:00, 09:00, 20:00) for Wi-Fi dropouts or packet loss.

Re-calibrate or replace the sensor if the intermittent failures persist after checking connections.

Check if the living room sensors are functioning normally and identify any anomalies.

Missing temperature reading (None) indicating a potential sensor fault.

C04 shows a stable baseline of ~25.3°C before 08:55.

C09 shows all rooms (living_room, bedroom, kitchen) dropping together from ~25°C to ~23°C between 16:30 and 17:00, consistent with an external environmental shift or house-wide AC engagement.

C10 shows the living room temperature continuing to drop as the kitchen temperature rises significantly (cooking activity), indicating the sensor tracks local heat sources correctly rather than being stuck.

Sensor values remain within realistic physical ranges (15°C - 28°C) throughout the entire log period.

The magnitude of the drop in C10 seems steep compared to the kitchen rise, but this could be due to air flow dynamics (AC blowing on living room while cooking heats kitchen).

Intermittent `None` readings exist, which require explanation though they may represent valid data gaps or transmission latency.

Temporary sensor communication failure or power glitch causing intermittent data loss (`None` values) during periods of high load or network congestion.

C05 at 09:00:00 and C10 at 18:54:00 show explicit `TemperatureMeasurement.MeasuredValue = None` entries.

In C10, the `None` reading occurs immediately after `kitchen_cook_surface` turns ON (18:53:30), suggesting a potential correlation between electrical load spikes and sensor instability.

In C11, another `None` reading appears at 19:49:00 shortly after `kitchen_window_contact` triggers, possibly indicating transient interference or a recurring fault trigger.

Data recovers quickly in subsequent timestamps (e.g., C10 at 18:55:00 returns a valid value), which argues against permanent hardware failure.

No explicit alarm/fault event codes are present in the logs.

Sensor calibration drift leading to inaccurate absolute readings, though relative trends appear correct.

Living room readings consistently run lower than kitchen readings in C10/C11 when both are occupied, potentially due to placement or calibration offset.

If the user expects >26°C based on other sensors, the living room might falsely indicate 'too cold' triggering unnecessary heating, though no such command is logged.

Cross-room comparison in C04 (07:20-08:55) shows living room (~25.3°C) is consistently higher than bedroom (~24.2°C) but similar to kitchen (~25.2°C), suggesting the offset is stable, not drifting erratically.

Behavior aligns perfectly with expected thermal gradients for the time of day and occupancy patterns.

The specific occurrence of `None` values at C05 (09:00) and C10 (18:54) coinciding with system events (time-based interval vs. appliance activation) distinguishes between random packet loss and event-induced sensor faults.

The consistency of the temperature drop across ALL rooms in C09 rules out a localized sensor malfunction causing the initial drop, isolating the anomaly strictly to the intermittent `None` events.

HVAC/System Control State: Did the thermostat command a setpoint change or fan speed adjustment in the living room during the `None` intervals?

Network/Power Logs: Was there a momentary brownout or Wi-Fi dropout correlated with the times of `None` readings?

Sensor Health Attributes: Are there any `OperationalState` or `SystemMode` changes reported specifically by the `living_room_temp_sensor` device itself?

Verify if `None` values in C05 and C11 are preceded by explicit protocol-level fault events (e.g., `OperationalState.Fault`) or if they are silent packet drops.

Check surrounding chunks (C03, C07) to determine if the intermittent data loss pattern is isolated to specific time windows (suggesting network congestion) or random noise.

Confirm whether HVAC system states changed during the `None` intervals which might explain missing readings as a result of controller logic rather than sensor failure.

The investigator correctly identified that the temperature drop across all rooms in C09 rules out a localized living room sensor malfunction for that specific event, validating the 'normal' hypothesis for the bulk data.

However, the core anomaly remains the intermittent `None` readings in C05 (09:00, 10:00) and C11 (20:00). The evidence suggests communication gaps but lacks direct fault codes. Without seeing the context immediately before these timestamps in adjacent chunks (C03, C07), we cannot distinguish between a recurring hardware fault, a Wi-Fi dropout, or a scheduled maintenance window.

The query asks to judge if sensors are 'working normally'. Intermittent data loss technically constitutes a functional anomaly even without a permanent hardware failure. Since the cause is ambiguous (network vs. device), a definitive verdict cannot be reached yet.

Recommend fetching C03 and C07 to check for patterns preceding the first anomaly in C05 and the last anomaly in C11.

Living room sensors are functioning normally, reporting stable ambient temperatures consistent with the rest of the home during unoccupied periods.

Between 08:57 and 18:10 (unoccupied), living_room_temp_sensor reports values consistently between 23.09°C and 25.56°C.

Bedroom sensor reports lower but stable values (~21-24°C) during the same period, indicating a plausible thermal gradient.

Kitchen sensor shows higher values (~25-27°C) due to cooking activity later in the log, confirming environmental variability is being captured correctly.

Fails to explain the significant temperature drop in C10/C11 (from ~25°C to ~22°C) while occupancy remains 'occupied' and no heating/cooling events are recorded for the living room.

The living room temperature sensor is malfunctioning or drifting significantly, reporting erroneously low temperatures that do not match the physical reality indicated by other sensors.

In Chunk C10 and C11, the living_room_temp_sensor drops from ~25.5°C (in C05) to ~22.8°C after occupants enter at 18:10.

This drop occurs despite the bedroom sensor showing only a slight decrease (from ~24°C to ~22°C) and the kitchen sensor rising due to cooking.

The magnitude of the drop (approx. 3°C) in an occupied room without any HVAC interaction suggests a sensor failure rather than environmental change.

Data gaps exist where `TemperatureMeasurement.MeasuredValue` is explicitly empty (e.g., C05 at 09:00:00, C10 at 18:54:00 kitchen, C11 at 19:49:00 kitchen and 20:00:00 living_room), which may indicate intermittent communication failures affecting the living room sensor as well.

No explicit 'fault' or 'alarm' event is logged for the living_room_temp_sensor itself; the anomaly is inferred solely from value inconsistency.

Temporal correlation between occupant entry (18:10) and immediate, sustained temperature drop in living_room sensor, while bedroom sensor remains relatively stable.

Discrepancy between living_room readings (~22-23°C) and kitchen readings (~25-27°C) during the evening, suggesting the living room should be warmer if heated, or cooler if air conditioning is off, but the consistency of the low reading across multiple time points suggests a fixed offset error or drift rather than transient noise.

Explicit null values (`None`) in the log stream indicate potential data acquisition issues.

HVAC system status (thermostat setpoints, mode, fan speed) for the living room to rule out automated cooling causing the drop.

Explicit fault logs or operational state changes for the `living_room_temp_sensor` device.

HVAC/System Control State: Check if a thermostat or smart home system commanded cooling (setpoint change) to the living room during the drop.

Cross-Room Thermal Gradient Validation: Verify if the bedroom and kitchen sensors also dropped proportionally in C10/C11 to rule out a global ambient shift caused by weather changes (e.g., air conditioning engaging house-wide).

Sensor Calibration History/Offset: Determine if the living_room_temp_sensor has a known fixed offset compared to other sensors before the anomaly event.

Data Gap Correlation: Investigate the explicit 'None' values at C10/C11 timestamps more closely to see if they indicate packet loss or sensor freeze rather than valid null readings.

The investigator correctly identified the temperature drop as anomalous given the occupancy state, but relied heavily on cross-sensor comparison which is currently incomplete without checking HVAC status.

The hypothesis of 'sensor malfunction' is plausible but not confirmed. A global environmental change (e.g., AC turning on for the whole house) could explain the drop in all rooms, though the magnitude in the living room seems steeper.

Missing chunks C04 (pre-drop baseline), C06 (transition period), and C09 (context) are needed to establish the pre-anomaly trend and immediate trigger conditions.

Do not conclude 'fault' yet; the evidence shows behavior inconsistent with normal unheated/uncooled conditions, but consistent with an external control event or calibration drift that requires further context to distinguish from a hardware failure.

## 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": 407,
    "bedroom": 357,
    "kitchen": 298,
    "bathroom": 56,
    "entrance": 14
  },
  "attribute_frequency": {
    "MeasuredValue": 866,
    "Occupancy": 225,
    "OnOff": 18,
    "StateValue": 5,
    "LockState": 4,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 225,
  "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": "2026-06-01T00:00:00",
    "end_ts": "2026-06-01T01:50:00",
    "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": "Check if the living room sensors are functioning normally and identify any anomalies."
  },
  "focus_rooms": ["living_room"],
  "focus_devices": ["living_room_temp_sensor", "living_room_occupancy"],
  "focus_chunk_ids": ["C05", "C10", "C11"],
  "suspected_patterns": [
    "Missing temperature reading (None) indicating a potential sensor fault.",
    "Temperature drop magnitude relative to occupancy state requiring verification of sensor accuracy or calibration drift."
  ],
  "why_these_chunks": [
    "C05 contains an explicit event '[2026-06-01T09:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = ', which is direct evidence of a data failure or sensor fault.",
    "C11 shows a significant temperature drop in the living room coinciding with occupancy, potentially indicating a faulty sensor reading that doesn't match environmental expectations (compared to other rooms).",
    "C10 provides baseline context for temperature trends and occupancy transitions in the living room prior to the anomaly in C11."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Living room temperature sensor is functioning normally, reporting accurate ambient temperatures that reflect global HVAC adjustments and natural diurnal cooling.",
      "evidence": [
        "C04 shows a stable baseline of ~25.3°C before 08:55.",
        "C09 shows all rooms (living_ro


The bathroom occupancy sensor is functioning normally; observed transitions reflect valid user activity durations and standard periodic status reports during non-occupancy periods.

Explanation: Morning Sequence (C04-C05): The sensor correctly reported 'occupied' at 08:55 and maintained this state until 09:19 (approx. 24 minutes). This duration aligns with a typical bathroom usage event (e.g., showering or grooming), and the subsequent switch to 'unoccupied' indicates the user exited or ceased movement, which is expected behavior rather than a stuck sensor.

Explanation: Night Sequence (C15-C16): During the late-night hours (21:49 to 23:55), the sensor consistently reported 'unoccupied'. While living room and bedroom sensors show frequent updates confirming presence in other areas, the absence of bathroom events suggests the user did not enter the bathroom during these specific time windows. If the user had entered, we would expect an 'occupied' event similar to the morning pattern.

Explanation: Update Frequency Analysis: The regular intervals of 'unoccupied' reports in C15/C16 (approx. every 30 minutes) correspond to the device's heartbeat or polling interval when no motion is detected. This pattern differs from the high-frequency motion-triggered updates seen in occupied rooms but represents normal low-power operation, not a communication failure or sleep mode anomaly.

Explanation: No Fault Indicators: There are no explicit `DeviceFault`, `DiagnosticReport`, or impossible state transitions in the logs for the bathroom sensor. The data shows coherent temporal logic consistent with human behavior patterns.

[2026-06-07T08:55:00] bathroom_occupancy | OccupancySensing.Occupancy = occupied (raw=1)

[2026-06-07T09:19:22] bathroom_occupancy | OccupancySensing.Occupancy = unoccupied (raw=0)

[2026-06-07T21:49:22] bathroom_occupancy | OccupancySensing.Occupancy = unoccupied (raw=0)

[2026-06-07T23:49:22] bathroom_occupancy | OccupancySensing.Occupancy = unoccupied (raw=0)

Continue monitoring the sensor for future anomalies if unusual patterns emerge.

C16 covers the late-night period (23:05-23:55). Comparing the event count and update frequency of bathroom devices here against the high-frequency updates seen in living_room and bedroom reveals a significant drop in activity, which may indicate the device has gone offline, entered sleep mode incorrectly, or failed to transmit periodic status checks.

Sensor Timeout/Reset Behavior: The bathroom occupancy sensor operates normally but has a short timeout threshold (approx. 4-5 minutes). Users visited the bathroom for short durations during the morning, and the sensor correctly reported 'unoccupied' once they left or ceased movement.

C04 shows bathroom entering 'occupied' at 08:55 and C05 shows it switching to 'unoccupied' at 09:19 (24 mins later) - wait, re-evaluating time diff: 08:55 to 09:19 is 24 mins? No, 08:55 to 09:19 is 24 mins. Let's re-read the logs carefully.

Correction on Evidence: In C04, occupied at 08:55. In C05, occupied at 09:10 and 09:15. Unoccupied at 09:19:22. Duration of continuous reported occupancy from 09:10 to 09:19 is ~9 minutes. This is consistent with a standard bathroom visit duration.

Second Instance: Occupied at 09:49:22 (end of C05 window effectively, based on next unoccupied event in C16?). Wait, C05 ends at 10:25. There is no explicit 'occupied' event logged in C05 after 09:19 until the end? Ah, looking at C05 again: 09:19 is UNOCCUPIED. 09:49:22 is NOT in C05 log provided? Let me check C05 text again.

Re-scanning C05: 09:19:22 -> unoccupied. Then next bathroom event is... actually, there are NO bathroom events listed between 09:19:22 and 10:25:00 in the provided C05 chunk except the start state which was occupied at 09:10? No, 09:10 is occupied. 09:15 occupied. 09:19 unoccupied. That's it for C05? That implies the user left at 09:19 and did not return before 10:25.

Night Sequence: C15 shows unoccupied at 21:49, 22:19, 22:49. C16 shows unoccupied at 23:19, 23:49. These regular intervals suggest either scheduled checks or the user never entered the bathroom during these specific windows, triggering periodic status reports that default to 'unoccupied'.

The absence of any 'occupied' events in the late-night chunks (C15/C16) despite the living room being occupied suggests the sensor might be missing data if the user *did* enter, or simply confirms no entry occurred.

Continuous presence of the user in other rooms (bedroom/living_room) while bathroom remains 'unoccupied' during night hours (C15/C16), but this could simply mean the user stayed in bed/bathroom without activating the sensor, or didn't use the bathroom at all.

Rapid transition from 'occupied' to 'unoccupied' in C05 (09:15 to 09:19) matches typical short-duration usage patterns rather than a stuck sensor failure.

Did the user physically enter the bathroom between 22:00 and 23:55? If yes, and the sensor only reported 'unoccupied', the sensor failed to detect occupancy (Anomaly). If no, then Normal.

Verify if the user physically entered the bathroom during late-night hours (22:00-23:55) when only 'unoccupied' states were reported to rule out missed detection vs. non-entry.

Check for explicit `DeviceFault` or `DiagnosticReport` events in the full log range to confirm no hardware faults were logged silently.

Review C06 and C07 to determine if the sensor eventually recovered after the morning transition or if it remained silent, which would indicate a power/cycle failure rather than a timeout.

The Investigator correctly identified that the morning anomaly (rapid switch from occupied to unoccupied) likely represents normal behavior given the ~9 minute duration, avoiding a false alarm on that specific point.

However, the conclusion relies heavily on the assumption that the user did not use the bathroom between 22:00 and 23:55. Without external ground truth or logs showing an entry event, we cannot definitively classify the lack of 'occupied' readings as 'Normal' (user didn't go there) versus 'Anomaly' (sensor failed to detect presence).

The evidence is currently ambiguous regarding the night-time period. The Triage Output flagged 'Missing/Unstable readings', but the Investigator dismissed this too quickly without ruling out the possibility of a sensor entering a low-power sleep mode or failing to transmit heartbeats during the night while other devices continued updating.

We need C06 and C07 (likely covering the early morning or post-night sequence) to see if the sensor resumes normal high-frequency reporting or if it exhibits a pattern of total silence, which would support a fault hypothesis.

{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The bathroom occupancy sensor is functioning normally, reporting accurate states based on periodic polling or brief presence events that have since ended.",
      "evidence": [
        "At 09:19:22 and 10:19:22, the sensor transitions to 'unoccupied'. If the user had left the room (e.g., moved to the bedroom which remains occupied) or the time elapsed exceeded the sensor's timeout threshold, this is a valid state change.",
        "In the late night window (C16), the sensor reports 'unoccupied' at 23:19:22, 23:49:22. Given the low activity counts for the bathroom (55 vs 482+ in other rooms) and lack of temperature readings, the room is likely empty, making these readings correct.",
        "No explicit fault events (e.g., `OperationalState=offline`, `Fault=stuck`) are present in the logs."
    ]
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Bathroom occupancy sensor malfunction (Stuck/False Positive followed by erratic reset).",
      "threat_type": "sensor_malfunction",
      "evidence": [
        "In Chunk C05, the sensor reports 'occupied' at 09:10:00 and 09:15:00. However, it flips to 'unoccupied' at 09:19:22 (approx. 4 mins later) and again at 10:19:22 (exactly 60 mins after the first flip). The regularity of the ~60 minute interval suggests an artificial reset or a hardware glitch rather than natural human movement patterns.",
        "During the morning period (09:10-10:25), while the living room and bedroom show continuous updates every few minutes, the bathroom shows almost no data points until the specific timestamps mentioned. This intermittent update pattern combined with the sudden state flips indicates the sensor may be losing connection or resetting its internal logic incorrectly.",
        "The sensor reports 'occupied' initially but then immediately reverts without any subsequent 'occupied' event, potentially indicating it failed to detect sustained presence or misinterpreted environmental noise (e.g., steam from a shower not shown in logs) as occupancy before failing to maintain the state."
    ]
  ],
  "most_discriminative_evidence": [
    "The timestamp sequence in C05: Occupancy='occupied' (09:10), Occupancy='occupied' (09:15), then Occupancy='unoccupied' (09:19:22). A rapid drop to unoccupied shortly after consecutive positive readings, followed by a gap of nearly an hour before another report, deviates from typical continuous monitoring behavior.",
    "Absence of `TemperatureMeasurement` events for the bathroom throughout both chunks (C05 and C16), whereas all other sensors provide regular readings. While this could be normal if the temp sensor is off, it correlates with the occupancy gaps, suggesting a broader device instability in the bathroom area."
  ],
  "missing_information": [
    "Raw log data for the bathroom temperature sensor to confirm if it is offline or simply silent.",
    "User activity logs confirming whether someone actually entered the bathroom between 09:15 and 09:19 to validate the 'unoccupied' reading.",
    "Device operational state flags (e.g., `OperationalState`) to explicitly rule out network disconnection vs. internal sensor failure."
  ]
}

Check C04 (preceding window) to see if the 'occupied' state at 09:10 was a delayed report of entry or a lingering stale state from before the chunk.

Check C15 (preceding night window) to establish the baseline occupancy status of the bathroom immediately before the late-night gap in C16.

Verify if `TemperatureMeasurement` events are entirely absent for the bathroom across ALL chunks, not just C05 and C16, to determine if it's an intermittent sensor failure or a complete device power loss.

The investigator correctly identified the anomaly pattern (rapid flip followed by long silence), but the evidence is currently ambiguous between 'sensor malfunction' and 'user behavior + sensor timeout'.

Specifically, the transition from 'occupied' (09:10/09:15) to 'unoccupied' (09:19:22) could mean the user left quickly (bathroom visit duration < 5 mins). Without context from C04 showing when they entered, we cannot confirm this is a fault.

The absence of temperature readings for the bathroom is noted as suspicious, but without checking adjacent chunks (C04, C15), we don't know if the device was ever active or if it has been offline for hours. This increases the risk of a false alarm if the device is simply off due to power saving.

Do not conclude 'fault' yet. The pattern matches both a faulty sensor stuck high then reset, AND a normal short visit followed by a timeout. More temporal context is required.

## Query
请检查以下设备状态历史，判断 bathroom 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": 482,
    "bedroom": 493,
    "kitchen": 308,
    "bathroom": 55,
    "entrance": 3
  },
  "attribute_frequency": {
    "MeasuredValue": 870,
    "Occupancy": 442,
    "OnOff": 18,
    "StateValue": 4,
    "CurrentPositionLiftPercent100ths": 3,
    "SystemMode": 1,
    "OperationalState": 1,
    "LockState": 1
  },
  "lock_state_flips": 1,
  "occupancy_events": 442,
  "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": "2026-06-07T00:00:00",
    "end_ts": "2026-06-07T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4
    },
    "top_devices": [
      "living_room_temp_s

## Query
请检查以下设备状态历史，判断 bathroom 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": ["bathroom"],
    "target_devices": ["bathroom_occupancy_sensor", "bathroom_light"],
    "target_question": "Check if bathroom sensors are working normally and identify potential faults."
  },
  "focus_rooms": ["bathroom"],
  "focus_devices": ["bathroom_occupancy_sensor", "bathroom_light"],
  "focus_chunk_ids": ["C05", "C16"],
  "suspected_patterns": [
    "Occupancy sensor stuck/unresponsive: In C05, the sensor reports 'occupied' (raw=1) at 09:10 and 09:15 but reverts to 'unoccupied' (raw=0) immediately at 09:19:22, potentially indicating a false positive or rapid state reset rather than sustained occupancy.",
    "Missing/Unstable readings: The logs frequently show `bathroom_occupancy` events, but in C16 (23:05-23:55), despite being in the top devices list for the room, there is a lack of continuous heartbeat-like updates compared to other rooms, suggesting potential communication gaps or power issues during low-activity periods.",
    "Correlated light state: The light turns off (C05) shortly after an occupancy change, which could be normal automation, but combined with the occupancy anomalies, it suggests the control logic might be reacting to faulty sensor data."
  ],
  "why_these_chunks": [
    "C05 contains a critical anomaly where the bathroom occupancy sensor switches from 'occupied' to 'unoccupied' very rapidly (within ~4 minutes) while the user appears to still be present (indicated by bedroom occupancy staying 'occupied' until later), suggesting a possible sensor malfunction or interference.",
    "C16 c


LockDoor 命令后 LockState 未变为 Locked
DoorLockAlarm (LockJammed) 触发
门锁故障意味着家庭物理安全降级

通知住户门锁可能卡住/故障，建议手动检查

The sensor sequence following the manual unlock aligns with a rapid physical door cycle (open then immediately closed) rather than a logical inversion or permanent fault.

Explanation: The log shows a Manual Unlock at 07:17:23, followed by the contact sensor reporting 'false' at 07:17:25 and 'true' at 07:17:26. This indicates the door was open for only 2 seconds after unlocking before being shut.

Explanation: While the polarity (what 'false' means) is unknown, both interpretations (False=Open/True=Closed OR False=Closed/True=Open) allow for a physically normal scenario: the user unlocked, opened the door briefly (perhaps to check something or just a fast exit), and shut it immediately before the Auto-Lock triggered at 07:17:27.

Explanation: There are no explicit fault codes, stuck values persisting beyond debounce windows, or impossible state transitions in C03 or C05 that would indicate a hardware malfunction or logic error.

Explanation: The lack of activity in C03 and C05 for this specific sensor does not constitute a fault; it is consistent with a system where the entrance contact sensor is only active/reported during lock/unlock cycles or brief occupancy events, as seen in the sparse data.

Timestamp sequence: Unlock (07:17:23) -> Sensor State Change (07:17:25) -> Opposite State (07:17:26) -> Auto Lock (07:17:27).

Absence of explicit DeviceFault events or alarm states in the logs for front_door_contact.

Verify the physical behavior on-site: Confirm if users typically open the front door and close it within 2 seconds of unlocking.

Review device configuration documentation to confirm the expected polarity mapping for BooleanState.StateValue to fully eliminate the possibility of an inverted wiring setup.

This rapid transition (False -> True) aligns logically with the subsequent `Auto` lock operation at 07:17:27. The sensor accurately reflects the physical state change required to trigger the auto-lock.

No explicit fault events, alarms, or stuck values are recorded for `front_door_contact` in chunks C03, C04, or C05.

Data coverage for the entrance area is sparse compared to other rooms; only one specific event sequence involving the front door is visible in the entire log window.

The single observed cycle does not prove long-term reliability or rule out intermittent failures that did not occur during this specific timeframe.

Absence of evidence is not evidence of absence; the lack of activity in the entrance logs could also indicate a silent failure mode where the sensor stopped reporting entirely (though unlikely given the precise timestamp match).

Potential sensor wiring inversion or logic error causing false 'closed' state reading upon door opening.

At 07:17:23, the user manually unlocks the door.

At 07:17:25 (2 seconds later), the contact sensor reports `false` (open).

At 07:17:26 (1 second later), the contact sensor switches to `true` (closed).

While this sequence matches the subsequent auto-lock, if the standard behavior were 'Open -> False, Closed -> True', the switch from False to True implies the door closed. However, if the sensor logic is inverted (Open=True, Closed=False), the initial `false` would mean 'Closed' while the door was just unlocked (physically open), which would be a dangerous anomaly. Conversely, if the logic is standard, the rapid bounce (False->True) suggests a mechanical bounce or very fast closing.

Alternative Interpretation: If the system expects `Occupancy=0` when locked and `Occupancy=1` when open, the immediate flip might hide a delay. But more critically, if the sensor failed to report `false` until 2 seconds after unlock, it indicates a latency issue rather than a hard fault, but combined with the instant flip back to `true`, it raises the possibility of a 'stuck high' condition resolving too quickly or a debounce filter being bypassed incorrectly.

Wait, re-evaluating based on Matter semantics: Usually Contact=1 means Open, 0 means Closed (or vice versa depending on config). The log says `false` then `true`. If `false`=Open and `true`=Closed, the door opened then closed rapidly. This is normal. If `false`=Closed and `true`=Open, the door was reported Closed while unlocked (impossible unless already closed before unlock), then reported Open immediately after. Given the context of 'Manual Unlock', the door is physically open. If the sensor read `false` (meaning Closed) while the door was open, that is a **sensor malfunction/inversion**.

Without knowing the specific polarity configuration of this device (whether raw `true` maps to Open or Closed), it is impossible to definitively classify the state as anomalous. Standard configurations vary.

The timing (2s delay then 1s recovery) is plausible for a human to close a door after unlocking, making the sequence potentially normal without further context on typical user behavior speed.

There is no corroborating data from an occupancy sensor or video feed in these logs to verify the actual physical position of the door at those exact seconds.

The precise 2-second gap between the Manual Unlock event (07:17:23) and the first BooleanState update (07:17:25), followed by an instantaneous flip to the opposite state (07:17:26).

The total duration of the 'open' state (`false`) being only 2 seconds post-unlock, which is unusually short for a full egress/egress cycle unless the user moved extremely fast or the door was pushed shut almost immediately.

Device configuration details: Does `BooleanState.StateValue = true` correspond to 'Door Open' or 'Door Closed'? This is critical to interpret the 07:17:25 vs 07:17:26 states.

Historical baseline: Logs prior to 07:17:00 showing multiple cycles of the front door contact to establish if the 2-second 'open' duration is an outlier or consistent behavior.

Physical verification: Any accompanying events from cameras or motion sensors confirming the door remained open or was closed at those timestamps.

Verify device configuration for BooleanState polarity (does true=Open or true=Closed?) to definitively interpret the sensor state.

Check historical data in C03 and C05 for baseline behavior of the front_door_contact sensor before the specific event window.

Look for 'stuck value' patterns where the sensor fails to report a state change within the expected debounce window across multiple cycles.

Cross-reference with DoorLock.LockState transitions to ensure sensor readings align with physical lock/unlock events.

The investigator correctly identified the lack of definitive evidence for a fault due to missing polarity configuration. The sequence (Unlock -> false -> true) is ambiguous without knowing if 'false' means Open or Closed.

The current verdict leans towards 'Normal' but relies on the assumption that the short duration of the intermediate state was physically possible (user moving fast), which is speculative without user context or history.

The provided chunks (C04 only) are insufficient to rule out intermittent failures or establish a baseline. The analysis must explicitly check prior (C03) and subsequent (C05) logs for recurring anomalies or consistent behavior.

Do not finalize a 'Normal' verdict yet; the ambiguity regarding the boolean mapping prevents a high-confidence determination of correct operation versus logic inversion.

The entrance sensor (front_door_contact) is functioning correctly, with state transitions accurately reflecting the physical door locking and unlocking sequence.

At 07:17:23, a manual lock operation occurs followed immediately by the state transitioning to 'unlocked' (raw=2).

At 07:17:25, the contact sensor reports 'false' (raw=False), indicating the door is open/unlocked.

At 07:17:26, the contact sensor reports 'true' (raw=True), indicating the door has been closed/locked.

At 07:17:27, an auto-lock operation occurs and the state transitions to 'locked' (raw=1).

The sequence follows logical causality: Lock Operation -> State Change -> Sensor Confirmation -> Auto-Lock.

The temporal correlation between the 'LockOperation' event and the subsequent 'BooleanState' flips on the contact sensor demonstrates coherent behavior without stuck states or impossible transitions.

No explicit fault events, alarm codes, or repeated non-recovery patterns are present in the logs for the entrance devices.

Verify pre-condition state in C03 to establish baseline occupancy/lock status before the event sequence.

Verify post-condition state in C05 to ensure no recovery issues or stuck states occurred after the auto-lock sequence.

Cross-check if 'true' (closed) state at 07:17:26 immediately following a manual unlock is a legitimate bounce or indicates a sensor wiring/logic inversion.

The investigator correctly identified that the provided chunk (C04) contains only one instance of the target device's activity. This creates a high risk of a false negative (concluding 'normal' based on insufficient data rather than confirmed health).

The logic in C04 shows a manual unlock -> door opens (false) -> door closes (true) -> auto-lock. While coherent, this single event cannot rule out intermittent failures (e.g., sensor failing mid-cycle or failing to report on subsequent cycles).

Per rules: 'A device-fault conclusion requires direct fault evidence... Scaled temperature values alone are not enough.' Conversely, a 'normal' verdict also requires robust temporal coverage to avoid missing transient faults. The current evidence is ambiguous regarding long-term stability.

Do not allow final verdict yet. Must check adjacent chunks (C03, C05) for continuity and additional state transitions.

## 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": 400,
    "bedroom": 353,
    "kitchen": 298,
    "bathroom": 57,
    "entrance": 19
  },
  "attribute_frequency": {
    "MeasuredValue": 866,
    "Occupancy": 220,
    "OnOff": 18,
    "LockState": 6,
    "StateValue": 5,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 6,
  "occupancy_events": 220,
  "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": "2026-06-22T00:00:00",
    "end_ts": "2026-06-22T01:50:00",
    "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"],
    "target_question": "Is the entrance area sensor working normally? If not, what is the likely fault type?"
  },
  "focus_rooms": ["entrance"],
  "focus_devices": ["front_door_contact"],
  "focus_chunk_ids": ["C04"],
  "suspected_patterns": [
    "Stuck value followed by recovery indicating a transient glitch or debounce issue rather than permanent failure.",
    "Rapid state transition (false -> true) immediately after lock operation suggests the contact sensor correctly detects door opening/closing."
  ],
  "why_these_chunks": "Chunk C04 is the only chunk containing events from the 'entrance' room. It shows specific interactions with the front door lock and contact sensor. Analyzing this sequence allows us to determine if the boolean transitions are logical (matching lock operations) or if there are anomalies like stuck states, which would indicate a fault."
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The entrance sensor is functioning correctly, exhibiting expected transient behavior consistent with manual door interaction and auto-locking protocols.",
      "evidence": [
        "In C04 at 07:17:23, a manual unlock event occurs, followed immediately by the contact sensor reporting `false` (open) at 07:17:25.",
        "At 07:17:26, the sensor reports `true` (closed), indicating the door was shut within 1 second of unlocking.",
        "This rapid transition (False -> True) aligns logically with the subse


温度传感器读数固定（stuck-at）

Day1 温度有正常的日间波动（上午偏低、中午偏高、晚间回落）
Day2 中午开始读数完全固定在 23.50°C
连续24小时+完全无变化，真实温度不可能如此稳定

标记传感器可能故障，建议住户检查/更换

The kitchen temperature sensor is functioning normally; isolated data anomalies (a malformed value and a transient null) were followed by immediate recovery to consistent readings aligned with neighboring sensors.

Explanation: The sensor reported valid, plausible temperatures (~23°C) consistent with other rooms for the vast majority of the logging period in chunks C00 and C20.

Explanation: The entry at 18:00:03 showing 'MeasuredValue = 2400' lacks a unit or raw code context compared to standard entries but occurs only once immediately before a subsequent valid reading at 18:05:00, indicating a transient parsing error rather than a stuck sensor.

Explanation: At 18:15:00, the sensor briefly reported 'None', while all other room sensors reported valid values; however, it recovered within 3 seconds to 23.11°C, matching the Living Room and Master Bedroom exactly, confirming a momentary communication glitch rather than a hardware failure.

Explanation: No explicit device fault events (e.g., OperationalState=Unreachable), repeated failures, or persistent drift occurred, and the post-anomaly data shows stable thermal behavior consistent with environmental changes.

Recovery from 'MeasuredValue = None' at 18:15:03 to a value identical to neighbor sensors.

Stable temperature trend (23.28 -> 23.21 -> 23.18 -> 23.15 -> 23.01) following the anomaly, consistent with room cooling.

Absence of any logged DeviceFault or OperationalState errors for the kitchen_temp_sensor.

Continue monitoring; no intervention required.

Optional: Check network stability if similar transient packet losses increase in frequency.

At 18:15:00, the log shows `kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = None`, while other sensors report valid values.

Exactly 3 seconds later (18:15:03), the sensor reports `MeasuredValue = 23.11 C`, which aligns closely with neighbors (Living Room: 23.11, Master Bedroom: 23.11).

Subsequent readings (18:20:00, 18:25:00, etc.) show stable, plausible temperature drift (23.23 -> 23.15 -> 23.01) consistent with room cooling after occupancy changes.

No explicit fault events (e.g., `OperationalState=Unreachable` or `DeviceFault`) were recorded for this device.

The single instance of 'None' could theoretically indicate a momentary hardware freeze, though the rapid recovery makes this less likely than a protocol timeout.

The event sequence at 18:15:00 where `kitchen_temp_sensor` returns `None` while all other room sensors return valid floats, followed by an immediate recovery to a consistent value within 3 seconds.

The absence of any persistent error states or repeated failures suggests the system recovered automatically without manual intervention or firmware reset.

Raw network traffic logs around 18:15:00 to confirm if the 'None' was due to a dropped packet or a specific sensor internal error code.

Verify if the `MeasuredValue = 2400` entry in C20 at 18:00:03 is a transient parsing error or a genuine stuck value (though subsequent rapid recovery suggests former).

Confirm no other kitchen-specific fault events exist outside the provided chunks C00 and C20.

The investigator correctly identified the 'None' value at 18:15:00 as a transient communication glitch due to immediate recovery and consistency with neighbors.

The '2400' value in C20 was flagged by Triage but lacks sufficient context for a definitive 'fault' verdict without explicit alarm codes; however, its isolation between valid readings supports the 'normal operation with minor glitches' hypothesis rather than a device failure.

No false-alarm risk detected regarding temperature scales (all values are plausible Celsius temps ~23°C), and the evidence chain (baseline -> anomaly -> recovery -> stability) is coherent.

The current evidence allows a confident conclusion that the sensor is functioning normally despite isolated data packet issues.

## 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: 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": 398,
    "master_bedroom": 515,
    "kids_room": 515,
    "kitchen": 313,
    "dining_room": 223,
    "second_bedroom": 48,
    "master_bathroom": 56,
    "entrance": 227,
    "second_bathroom": 1
  },
  "attribute_frequency": {
    "MeasuredValue": 1166,
    "Occupancy": 1082,
    "OnOff"

## 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 abnormal."
  },
  "focus_rooms": ["kitchen"],
  "focus_devices": ["kitchen_temp_sensor"],
  "focus_chunk_ids": ["C00", "C20"],
  "suspected_patterns": [
    "Temperature drift beyond normal environmental variance compared to other rooms.",
    "Missing or malformed timestamp values in specific log entries (e.g., 'MeasuredValue = 2400' without 'raw' or unit context).",
    "Stuck or non-responsive readings during periods of high room activity."
  ],
  "why_these_chunks": [
    "C00 represents the start of the logging period to establish baseline behavior, initial calibration, and expected temperature ranges relative to other sensors.",
    "C20 contains a suspicious log entry ('[2026-06-09T18:00:00] ... MeasuredValue = 2400') which deviates from the standard format seen elsewhere, potentially indicating a firmware bug, parsing error, or sensor malfunction."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The kitchen temperature sensor is functioning normally. The reading of 'MeasuredValue = None' at 18:15:00 is a transient, non-fatal data packet loss or temporary communication glitch that resolved immediately in the next heartbeat (18:15:03) with a valid value consistent with surrounding sensors.",
      "evidence": [
        "At 18:15:00, the log shows `kitchen_temp_sensor | TemperatureMeasuremen


运动传感器高频率周期性触发 该房间无其他活动迹象（灯关、无门窗事件） 周期性极其规律（不像人类活动的随机性）

The second_bedroom occupancy sensor exhibits erratic state flipping (rapid 0->1->0 toggling) immediately following a significant detection delay, indicating a potential hardware sensitivity issue or signal instability rather than normal environmental presence.

Explanation: Initial Analysis: The temperature sensor in the second_bedroom functions normally, showing consistent readings and gradual warming trends throughout the log, ruling out sensor_drift or sensor_stuck for the temperature device.

Explanation: Anomaly Identification: A critical anomaly is observed in the occupancy sensor starting at T07:48:00. After a ~23-minute gap where the room remained reported as 'unoccupied' despite general house activity (entrance_occupancy active since 07:24), the sensor suddenly begins rapidly toggling between occupied (1) and unoccupied (0) every 30-60 seconds.

Explanation: Causal Link & Pattern Recognition: This rapid oscillation pattern (flipping every ~30s) is characteristic of PIR motion sensors struggling with ambient light changes, low battery, or weak signal reception, often described as 'ghosting' or jitter. It contrasts sharply with the stable 'unoccupied' state seen earlier in the log.

Explanation: Exclusion of Normal Behavior: While a person could theoretically enter and leave quickly, the frequency of the flips (multiple times within minutes) suggests an inability to maintain a stable reading rather than discrete human movements. The preceding long delay further suggests the sensor was not detecting presence correctly during that window until it eventually triggered into this unstable mode.

Explanation: Conclusion: Although no explicit fault code was logged, the coherent temporal pattern of delayed detection followed by high-frequency state instability constitutes a functional anomaly requiring investigation.

Sequence from 07:48:00 to 07:49:10 shows OccupancySensing.Occupancy transitioning rapidly: 1 -> 0 -> 1 -> 0 -> 1 within less than two minutes.

Prior to 07:48:00, the sensor reported 'unoccupied' (raw=0) for over 2 hours while the entrance_occupancy sensor was active, suggesting a failure to detect entry or a stuck-at-zero state that subsequently destabilized.

TemperatureMeasurement.MeasuredValue for second_bedroom_temp_sensor remains smooth and physically plausible, confirming the issue is isolated to the occupancy logic/sensor.

Inspect the physical placement of the second_bedroom occupancy sensor to ensure it is not obstructed or affected by direct sunlight/darkness cycles.

Check the sensor's battery level or power supply connection, as voltage drops can cause intermittent PIR false positives/negatives.

Perform a manual test by walking through the room while observing if the sensor continues to report rapid fluctuations or stabilizes on a single state.

Occupancy sensor transitions are dynamic: it reports 'unoccupied' for the majority of the log but switches to 'occupied' at T07:48:00 after an entrance event and returns to 'unoccupied' shortly after, indicating responsive detection of movement.

The initial long period of 'unoccupied' status (approx. 7.5 hours) could theoretically hide a 'stuck-at-zero' hardware fault if the room was actually occupied during that time, though this is unlikely given the house-wide activity patterns starting later.

Potential false sense of security due to delayed occupancy detection or 'ghosting' in the second_bedroom.

At T07:24:27, the front door is unlocked manually, followed immediately by 'entrance_occupancy' becoming 'occupied'. However, the 'second_bedroom_occupancy' remains 'unoccupied' until T07:48:00. If an intruder entered via the front door and moved directly to the second bedroom, there was a ~23-minute window where the system failed to detect occupancy in that specific room while the rest of the house was active.

The sequence shows rapid toggling between occupied/unoccupied in the second_bedroom starting at T07:48:00 (multiple events in <2 minutes), which contrasts with the single clear transition seen earlier. This erratic behavior immediately following the delay suggests the sensor may have been failing to register presence until a threshold was crossed or reset, rather than detecting continuous motion.

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

It is plausible that no one visited the second bedroom during the early morning hours when the door was accessed, meaning the sensor behavior was correct for the actual physical state of the room.

The temporal gap between the 'entrance_occupancy' event at 07:24:30 and the first 'second_bedroom_occupancy' event at 07:48:00.

The cluster of rapid Occupancy state flips (0->1->0->1->0) in the second_bedroom between 07:48:00 and 07:49:10, occurring right after the initial delay.

Camera or manual verification logs to confirm whether anyone physically entered the second bedroom between 07:24 and 07:48.

Device diagnostic logs to check for missed pings or communication timeouts on the second_bedroom_occupancy sensor during the 07:24-07:48 window.

Verify if 'entrance_occupancy' events (T07:24) correlate with a person physically entering the second_bedroom path vs. just passing through the entrance.

Check sensor sampling intervals during the 07:24-07:48 gap to rule out communication dropouts or missed packets rather than sensor failure.

Analyze subsequent behavior in C06/C07: Does the erratic toggling (flipping every ~30s) persist as a hardware glitch pattern, or does it settle into normal operation?

Cross-reference temperature changes in second_bedroom between T07:48 and T07:50 against occupancy flips to see if heat signatures match the rapid state changes.

The investigator correctly identified the temporal gap and the erratic toggling but jumped to a 'potential fault/ghosting' hypothesis without sufficient direct evidence of device failure.

Matter Notes specify that boolean state transitions must be interpreted via context; the long 'unoccupied' period is not inherently a fault unless physical presence is confirmed elsewhere (which is ambiguous).

The rapid flipping (1->0->1->0) observed in C05 at the end of the log is a strong anomaly indicator for this specific sensor type (PIR motion sensors often exhibit this when struggling with ambient light or low signal), suggesting a hardware sensitivity issue rather than a total outage.

However, concluding a 'fault' requires ruling out the possibility that someone entered, stayed briefly, and left exactly within those windows. The current evidence is speculative regarding the *cause* of the anomaly.

Need to inspect C06 and C07 to see if the erratic behavior continues (indicating stuck/hardware issue) or resolves (indicating temporary environmental interference).

All sensors in the second_bedroom are functioning normally, reporting consistent temperature readings that track with environmental changes and neighboring rooms without deviation or failure.

Temperature values for second_bedroom_temp_sensor range logically from 22.69 C to 24.51 C over 5.3 hours, showing natural variance (~1.8°C) consistent with HVAC cycles or solar heating patterns.

Readings remain highly correlated with master_bedroom and kitchen temperatures (e.g., at 05:20, all rooms cluster between 24.42-24.64 C).

Occupancy sensor reports 'unoccupied' consistently throughout the log, matching the absence of occupancy events in the structured signals for this room.

No explicit fault codes, alarm states, or stuck values (repeated identical readings despite time passing) are present in the provided chunks.

Sampling intervals appear regular (mostly 5-minute steps), indicating normal communication health.

The dataset only covers a single night/day cycle; longer-term stability cannot be confirmed.

Without historical baseline data, it is impossible to definitively rule out a slow drift that has not yet been flagged as an anomaly by the system logic.

Consistent correlation between second_bedroom temperature and other rooms across multiple timestamps.

Absence of any raw value outliers (e.g., -40, 9999) or stuck states in the focused chunks.

Regular timestamp progression and successful reporting of both TemperatureMeasurement and OccupancySensing attributes.

Historical logs prior to 2026-06-08T00:00:00 to establish if recent initialization caused any transient errors.

Explicit fault event logs (if any exist outside these chunks) that might indicate a warning issued before these specific readings were captured.

Verify if the 'unoccupied' occupancy state persists throughout the entire log period to rule out a sensor that is stuck reporting 0 (stuck-at-zero fault).

Analyze later chunks (C04+) for potential drift, offset changes, or missed reports compared to other sensors.

Check for any explicit Fault/Alarm events associated with these specific devices in the full log.

The investigator correctly identified that the provided data (C00-C03) shows normal behavior with no obvious faults. However, the evidence is insufficient for a definitive verdict because the sample window (5.5 hours) is too short to confirm long-term stability or detect intermittent issues.

Crucially, the OccupancySensor consistently reports 'unoccupied'. Without seeing an event where it transitions to 'occupied' and back, we cannot definitively rule out a 'stuck-at-zero' hardware failure. A single night of empty rooms is not enough to prove the sensor detects people when they arrive.

The request explicitly asks to identify *potential* faults. To be thorough, we must check subsequent chunks (C04, C05) to see if the room gets occupied during the day. If the room becomes occupied but the sensor remains 0, that would be a critical finding.

No false-alarm risk regarding temperature scaling; values are reasonable and consistent across rooms.

## 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": 399,
    "master_bedroom": 336,
    "second_bedroom": 341,
    "kitchen": 298,
    "bathroom": 52,
    "entrance": 230
  },
  "attribute_frequency": {
    "MeasuredValue": 1154,
    "Occupancy": 473,
    "OnOff": 14,
    "StateValue": 5,
    "LockState": 4,
    "SystemMode": 1,
    "OperationalState": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 473,
  "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": "Check if the second_bedroom sensors are working normally and identify potential faults."
  },
  "focus_rooms": ["second_bedroom"],
  "focus_devices": ["second_bedroom_temp_sensor", "second_bedroom_occupancy"],
  "focus_chunk_ids": ["C00", "C01", "C02", "C03"],
  "suspected_patterns": [],
  "why_these_chunks": "These chunks contain the initial and early time-series data for the second_bedroom temperature sensor (C00, C01, C02) and occupancy sensor (C00). Analyzing the start of the log is crucial to establish baseline behavior, check for immediate initialization errors, and verify consistent sampling rates before looking at later anomalies. The preview lines show regular timestamped updates with plausible temperature values, suggesting normal operation so far."
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Both the temperature and occupancy sensors in the second_bedroom are operating correctly, reflecting environmental changes and human presence accurately.",
      "evidence": [
        "Temperature values (C00-C05) show a consistent upward trend from ~22.69°C to ~25.24°C, matching the general heating pattern observed across all rooms (e.g., living_room rising from 22.71°C to 25.30°C). The raw integer values (e.g., 2269 -> 2524) scale correctly to degrees Celsius without drift or offset errors relative to other sensors.",
        "Occupancy se


通知住户该区域烟雾报警器失灵，建议立即更换，期间注意防火

The kitchen temperature sensor exhibits a transient communication failure (reporting None) and shows reduced sensitivity to localized heat changes compared to other sensors.

Explanation: The log explicitly records `TemperatureMeasurement.MeasuredValue = None` at 2026-06-08T18:53:00 for the kitchen sensor, while all other room sensors reported valid values simultaneously. This indicates a momentary loss of data transmission or processing failure specific to this device.

Explanation: During the dishwasher cycle starting at 19:35:00, the Master Bedroom temperature dropped significantly (-0.21°C over ~45 mins), indicating effective cooling in that zone. In contrast, the Kitchen temperature remained nearly stable (-0.1°C drop) despite being adjacent to the active appliance. This divergence suggests the sensor is either failing to detect the localized thermal spike or is drifting/calibrated incorrectly relative to the actual environment.

Explanation: While the sensor recovered immediately after the glitch and resumed reporting, the combination of an explicit `None` value (indicating a protocol-level issue) and the subsequent behavioral anomaly (insensitivity to heat) constitutes a malfunction rather than normal operation.

[2026-06-08T18:53:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = None

Comparison of thermal response between Master Bedroom (-0.21°C change) and Kitchen (-0.1°C change) during the 19:35-20:25 dishwasher window

Inspect the physical connection and power supply to the kitchen temperature sensor.

Check for firmware updates or reset the device to recalibrate the zero-point.

Is the kitchen sensor working normally? If not, what is the fault type?

Normal temperature drift observed across all chunks.

No explicit HardwareFaultAlert or stuck values found in previews.

Conclusion: Sensor appears healthy based on available data.

Readings follow physical laws: temperatures rise during the day/house occupancy (C00, C17) and drop significantly during the night/unoccupied periods (C18 end, C19).

The sensor reacts to local heat sources; after the dishwasher starts at 19:35 (C18), the reading fluctuates around 22.7-22.8°C rather than dropping with other rooms, indicating it is detecting the localized heat.

After the dishwasher cycle ends (implied by lack of 'on' state later in C19), the temperature gradually decreases from ~22.6°C back towards ~22.4-22.5°C, showing proper thermal lag and recovery.

The magnitude of the temperature increase during the dishwasher cycle (approx +0.2°C over 40 mins) is smaller than expected for an open kitchen environment, potentially suggesting under-reporting or significant insulation, though not definitively a fault without calibration data.

A single momentary loss of signal at 18:53:00 in C17 requires context to rule out a transient glitch vs. a sustained failure, although the sensor immediately recovered.

The kitchen temperature sensor exhibits potential malfunction characterized by signal loss and insufficient sensitivity to a known heat source (dishwasher), possibly indicating a drift, calibration error, or internal communication fault.

At 2026-06-08T18:53:00 (Chunk C17), the sensor abruptly reports `MeasuredValue = None`, deviating from its previous pattern of regular integer reporting. This indicates a temporary failure to report data.

During the dishwasher cycle starting at 19:35:00 (Chunk C18), while other rooms show a clear cooling trend (e.g., Master Bedroom drops from 22.85°C to 22.64°C between 19:35 and 20:10), the kitchen only shows a marginal fluctuation (22.83°C -> 22.78°C -> 22.77°C). If the dishwasher heats the room significantly, the sensor should reflect a higher baseline or a slower cooling rate compared to adjacent zones.

In Chunk C19, the kitchen temperature (~22.5°C) tracks closely with the cooling trend of other rooms, but the absolute values remain slightly lower than the pre-dishwasher average, which could indicate the sensor failed to capture the peak heat load properly.

The absence of an explicit `HardwareFaultAlert` event makes this a behavioral anomaly rather than a confirmed hardware failure.

The observed temperature delta might be physically accurate if the kitchen has high air circulation or if the dishwasher is well-insulated, meaning the 'under-reporting' is actually normal physics rather than a sensor fault.

The signal loss at 18:53:00 was extremely brief (single timestamp) and followed immediately by valid data, making it a minor anomaly that does not necessarily imply permanent failure.

The specific timestamp 2026-06-08T18:53:00 where `kitchen_temp_sensor` reported `None` instead of a value, contrasting with continuous reporting from all other sensors.

The divergence in thermal behavior between the kitchen and master bedroom during the dishwasher cycle (19:35-20:25): the master bedroom cooled steadily (-0.21°C), while the kitchen remained relatively stable (-0.1°C), suggesting the kitchen sensor may not be capturing the localized heat spike effectively.

The exact duration and power output of the dishwasher cycle to determine the expected thermal impact on the kitchen.

Calibration history or baseline accuracy of the kitchen sensor to distinguish between a shifted zero-point (drift) and actual ambient conditions.

Logs immediately preceding 18:53:00 to confirm if the `None` value was preceded by any error codes or actuator commands (like window contact closing) that might have triggered a temporary safe-mode silence.

Verify if the `None` value at C17 (18:53:00) was part of a multi-event sequence or an isolated glitch by checking the surrounding log lines in C16 and C17 immediately before/after.

Confirm the operational state of the dishwasher during the specific window where the kitchen sensor showed minimal response to ambient cooling (19:40-20:10) to rule out active heating masking the drop.

Cross-reference the exact timestamp of the `None` event with the `kitchen_window_contact` false event to determine causal linkage (e.g., did closing the window trigger a safe-mode silence?).

Check for any explicit `DiagnosticFault` or `CommissioningDataChanged` events in the full log that might explain a calibration drift.

The investigator correctly identified the 'None' reading as the primary anomaly but lacks sufficient context to classify it definitively as a hardware fault versus a transient protocol glitch. A single timestamp loss followed by immediate recovery is often benign noise unless it repeats or correlates with a known failure mode.

The thermal analysis regarding the dishwasher is speculative without knowing if the dishwasher was actually running (heating) or just idle (not adding heat). The logs show `OnOff=on` at 19:35:00, but do not explicitly show `OperationalState` changing later, implying it might have finished early or been paused. This ambiguity prevents confirming 'insensitivity' as a fault.

Chunk C16 is missing from the focused set but contains the time leading up to the `None` event (18:53). Reviewing C16 is critical to see if there were preceding error codes or actuator commands that caused the silence.

Do not conclude 'Normal' yet due to the unexplained data gap. Do not conclude 'Hardware Fault' definitively without ruling out environmental factors (window closure) or transient errors.

Kitchen temperature sensor is functioning normally, reporting accurate ambient temperature changes consistent with room dynamics and other sensors.

In Chunk C00 (00:00 - 01:20), the kitchen sensor reports values fluctuating between 22.68°C and 22.98°C, showing natural drift over time without sticking or dead zones.

In Chunk C18 (19:30 - 20:25), the kitchen sensor tracks a cooling trend (from ~22.83°C down to 22.53°C in master bedroom context, though kitchen remains slightly higher at 22.64°C) consistent with evening cooling.

The sensor updates frequently (~every 5 minutes) matching the `attribute_frequency` for `MeasuredValue` (1156 events), indicating active polling/heartbeat.

No explicit `HardwareFaultAlert` or `OperationalState=unavailable` events are present in the logs for the kitchen device.

Cross-referencing with other room sensors shows similar magnitude of variation, suggesting global environmental shifts rather than local sensor failure.

Limited temporal coverage (only two distinct chunks); no data exists for morning/evening peaks or appliance usage cycles specifically affecting the kitchen.

Potential latent sensor calibration drift or slow response lag in the kitchen sensor, possibly exacerbated by proximity to the activated dishwasher.

At 19:35:00 in Chunk C18, the `kitchen_dishwasher` transitions to `on`. Simultaneously, the kitchen_temp_sensor reads 22.83°C. By 20:00:00 (25 mins later, during dishwashing cycle), it reads 22.79°C, while other rooms show more pronounced drops or stability.

Between 19:35:00 and 20:00:00, the kitchen sensor shows minimal variance (0.04°C drop) compared to the `second_bedroom` which dropped significantly (22.84 -> 22.71). This could indicate a slower thermal response time or a slight offset relative to actual rising heat from the dishwasher that hasn't fully registered yet.

The absence of a corresponding spike in the kitchen temperature reading immediately following the dishwasher activation suggests the sensor might be under-reporting heat accumulation, leading to potential 'unattended cooking' safety blind spots if the thermostat relies on this data.

The temperature difference observed (0.04°C vs larger drops elsewhere) falls within normal measurement noise and hysteresis; it does not constitute a definitive 'stuck' value or impossible state transition.

No explicit fault code was generated by the system, making this a probabilistic inference rather than a confirmed hardware failure.

The dishwasher duration is unknown; if the cycle was short, the lack of temperature rise is expected.

The temporal correlation between the `kitchen_dishwasher` turning ON at 19:35:00 and the subsequent lack of significant temperature increase in the `kitchen_temp_sensor` readings compared to the general cooling trend seen in other rooms.

Consistency of raw integer values (`raw=2272`, etc.) across all timestamps indicates the communication link and scaling logic are intact, ruling out simple transmission errors.

Duration and intensity of the dishwasher cycle (how long was it running?).

Actual physical layout: Is the kitchen sensor located near the dishwasher or the exhaust vent?

Historical baseline data for the kitchen sensor during previous dishwasher cycles to determine if this behavior is a recurring anomaly or a one-time event.

Thresholds for 'sensor drift' detection in this specific Matter implementation (what constitutes a statistically significant deviation?).

Verify if the kitchen sensor reading at 20:00 (22.79°C) is physically consistent with the massive drop in other rooms (Master Bedroom ~0.3°C, Second Bedroom ~0.2°C). A lack of correlation here might indicate a drift or a different thermal zone, but the Investigator's claim that 'kitchen remains slightly higher' while others drop significantly needs closer inspection of the raw deltas.

Check for 'stuck value' patterns specifically around the dishwasher cycle. Did the sensor repeat the same value for multiple intervals despite environmental changes?

Confirm the duration of the dishwasher event to rule out that the heat source was too short to cause a measurable rise in such a small sensor interval.

The Investigator correctly identified that scaled values are realistic (centi-degrees), avoiding the common pitfall of flagging 2272 as 2272°C. This keeps the false-alarm risk low.

However, the evidence for a fault is weak and speculative. The hypothesis relies on the *absence* of a temperature spike during a dishwasher cycle, which could easily be explained by normal physics (sensor lag, air circulation preventing local heating, or the dishwasher being contained/insulated).

The Investigator noted limited temporal coverage. We need Chunk C17 (likely leading up to the dishwasher start) to see the baseline trend more clearly, and Chunk C19 (post-cycle) to see if the sensor recovers or lags further once the heat source stops. Without seeing the recovery phase, we cannot confirm a 'lag' vs a 'normal response'.

Do not conclude 'Normal' yet; the anomaly story (potential under-reporting of heat) is plausible but unproven without the post-event data.

## 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": 402,
    "master_bedroom": 516,
    "second_bedroom": 336,
    "kitchen": 306,
    "bathroom": 48,
    "entrance": 230
  },
  "attribute_frequency": {
    "MeasuredValue": 1156,
    "Occupancy": 644,
    "OnOff": 15,
    "StateValue": 7,
    "LockState": 5,
    "SystemMode": 1,
    "HardwareFaultAlert": 1,
    "ExpressedState": 1,
    "OperationalState": 1,
    "CurrentPositionLiftPercent100ths": 1
  },
  "lock_state_flips": 5,
  "occupancy_events": 644,
  "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": "e

## 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": "Is the kitchen sensor working normally? If not, what is the fault type?"
  },
  "focus_rooms": ["kitchen", "living_room"],
  "focus_devices": ["kitchen_temp_sensor", "living_room_temp_sensor"],
  "focus_chunk_ids": ["C00", "C18"],
  "suspected_patterns": [
    "Normal temperature drift observed across all chunks.",
    "No explicit HardwareFaultAlert or stuck values found in previews.",
    "Sensor readings follow physical laws (cooling down at night, heating up during day).",
    "Conclusion: Sensor appears healthy based on available data."
  ],
  "why_these_chunks": [
    "C00: Shows the start of the log with baseline temperatures (~22.7°C) and confirms the sensor is reporting valid MeasuredValue events regularly.",
    "C18: Shows evening/nighttime conditions where the home cools down; verifies the sensor continues to track environmental changes correctly without sticking or drifting abnormally compared to other rooms."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The kitchen temperature sensor is functioning correctly, accurately reporting ambient temperature changes in response to environmental shifts (day/night cycles) and appliance operation.",
      "evidence": [
        "The sensor reports a continuous stream of valid `TemperatureMeasurement.MeasuredValue` events throughout all chunks (C00, C17, C18, C19).",
        "Readings follow physical laws: temperatures rise during the