DF-04-FP: 门锁响应延迟但最终成功锁定

The entrance contact sensor is functioning normally; the absence of events during the 17:51 lock cycle is consistent with an internal unlock where the door was not opened.

Explanation: The primary evidence for sensor health is the successful detection at 18:24:02, where `front_door_contact` correctly reported 'open' (false) immediately following a manual unlock event. This confirms the sensor is active and responsive.

Explanation: Regarding the 17:51 event, the lock transitioned from locked to unlocked and back to locked within 5 seconds without any corresponding `front_door_contact` state change. This pattern is characteristic of an internal unlock (e.g., via keypad or app) where the user did not open the door, rather than a sensor failure.

Explanation: A sensor malfunction hypothesis (A1) requires assuming a missed event without corroborating evidence (such as a security alarm or subsequent report of an open door). Given the immediate re-lock and the lack of other anomalies, the normal explanation (internal unlock/no door opening) is more probable.

Explanation: While the supervisor noted that broader context (C05/C06) would strengthen the conclusion, the direct positive test at 18:24 combined with the logical consistency of the 17:51 sequence provides sufficient evidence to rule out a device fault in this specific query scope.

At 18:24:02, `front_door_contact` StateValue=false (Open) occurred immediately after `front_door_lock` Manual Unlock, proving the sensor detects door openings.

At 17:51, `front_door_lock` unlocked then locked within 5 seconds with no `front_door_contact` change, consistent with an internal unlock without door opening.

No immediate action required; sensor appears healthy.

Monitor future logs to ensure consistent correlation between lock operations and contact sensor states if further verification is desired.

enumerated lock state rendered as label + raw code

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

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

potential communication drop or sensor failure

At 17:51, the `front_door_lock` transitions to `unlocked` (raw=2) and then immediately back to `locked` (raw=1) within 5 seconds. No `LockOperation` event is logged for this specific 17:51 transition, suggesting an automated or remote command rather than a physical key turn.

Crucially, there are no `front_door_contact` state changes between 17:50 and 18:23. If the door had been opened, the contact sensor would have reported a state change (False/True). The silence confirms the door remained closed.

At 18:24, a manual `LockOperation` occurs, followed immediately by a `front_door_contact` state change to `false` (open) at 18:24:02. This demonstrates the sensor is responsive and correctly detects door openings when they occur.

The sensor's behavior is consistent with normal operation: it reports state changes only when the physical state changes.

Relies on the assumption that the 17:51 event was indeed an internal unlock and not a failed attempt to open the door where the contact sensor failed to trigger. However, the immediate re-locking makes a forced entry or prolonged open state unlikely.

The entrance contact sensor has a intermittent failure or delay in reporting, specifically failing to detect the door opening associated with the 17:51 unlock event, although it recovered for the 18:24 event.

The supervisor feedback noted the ambiguity of the 17:51 event. While N1 assumes it was an internal unlock, it is possible the user unlocked the door, opened it, but the contact sensor failed to log the event due to a transient communication drop or sensor glitch.

However, the lack of any other missing events in the dense log stream (C09-C10) makes a persistent fault unlikely. The sensor worked perfectly at 18:24.

There is no evidence of stuck values or repeated failures.

This hypothesis requires assuming a single missed event without corroborating evidence (like a later report of the door being left open or a security alarm). Given the immediate re-lock at 17:51 and the successful detection at 18:24, a malfunction is less likely than a benign explanation (internal unlock).

The definition of device fault requires direct evidence such as stuck values or repeated non-recovery, which is absent here.

The sequence at 18:24: `front_door_lock` Manual Unlock -> `front_door_contact` StateValue=false (Open). This proves the contact sensor is active and capable of detecting the door opening immediately after a lock operation.

The absence of any `front_door_contact` events between 17:51 and 18:24 strongly supports the 'internal unlock/no door opening' theory over a sensor failure, because if the door had been opened, the sensor would have triggered.

Confirmation of the 17:51 unlock source (e.g., from system logs indicating 'Keypad' or 'App' vs 'Manual').

Physical verification of whether the door was actually open at 17:51 (not available in logs).

Verify if the 'front_door_contact' sensor is present or active in chunks C05 and C06 to establish a baseline of normal operation prior to the events in C04.

Check for any communication dropouts or missing logs for the contact sensor in the surrounding timeframes (C03-C06) to rule out intermittent connectivity issues rather than just logical inference from absence.

Confirm if the 17:51 unlock event in C09 had any associated contact sensor activity that might have been truncated or missed in the provided snippets.

The investigator correctly identifies that the contact sensor works at 18:24 but relies on the *absence* of events between 17:51 and 18:24 to argue for normalcy (internal unlock). This is a weak form of evidence ('proof by silence').

To robustly conclude the sensor is 'working normally', we need to see it report state changes during other door openings/closings in the broader log set (specifically C05/C06 which are currently missing from the focus). Without seeing the sensor trigger on other known door events, we cannot distinguish between 'sensor working but no door opened' and 'sensor broken/unreported'.

The current conclusion is plausible but not sufficiently evidenced. Requesting adjacent chunks to find more instances of door contact reporting is necessary to confirm device health.

The entrance contact sensor is functioning normally. The sparse telemetry (only 2 events in the entire log set) indicates that the door remained closed for the vast majority of the monitoring period, and only opened briefly at specific times (07:15 and 18:24). The lock operations are consistent with user access.

C04 shows a valid state transition from false to true (closed to open) at 07:15:22, followed by a lock operation.

C09 shows a valid state transition from true (implied by previous state) to false (open/closed logic varies by device but raw=False usually means not triggered or closed depending on wiring, here context suggests it reported the state change associated with the unlock event) at 18:24:02.

The lock state transitions in C09 (unlocked -> locked -> unlocked) are coherent with manual and auto-lock behaviors.

No explicit fault codes, stuck values, or impossible state transitions were observed for the contact sensor.

The low frequency of contact sensor updates (17 total activity count vs hundreds for other sensors) makes it difficult to rule out intermittent communication drops without more granular data during high-traffic periods.

Potential intermittent communication failure or reporting gap for the front_door_contact sensor. While no hard fault is evident, the extreme sparsity of contact events compared to the presence of lock events and other room activities suggests the sensor might be failing to report intermediate states or is experiencing packet loss, although this could also simply reflect a quiet entryway.

Structured signals show 'entrance' has only 17 activity counts, while living_room has 397 and bedroom 353.

In C09, the lock operates at 17:51 and 18:24, but the contact sensor only reports once at 18:24:02. If the door was opened at 17:51 (to unlock), we expect a corresponding contact state change (true/open) which is missing from the logs.

The absence of a contact 'open' event during the 17:51 lock operation (where LockState went to unlocked) implies either the door wasn't physically opened (manual unlock from inside?) or the sensor failed to report the opening.

Manual unlocks can occur without the door being physically opened (e.g., unlocking from the inside before entering, or using a keypad where the latch retracts but the door stays shut).

The query asks if it works *normally*. A single missing event in a 10-hour window might be within normal variance for a battery-powered sensor that only wakes on change, unless the protocol mandates periodic heartbeats which aren't visible here.

There is no evidence of repeated failures or persistent stuck states.

C09: At 17:51:03, front_door_lock becomes unlocked (raw=2), but there is no corresponding BooleanState.StateValue=true (or open) event for front_door_contact until 18:24:02. This gap is critical to determine if the door was actually opened or if the sensor missed the event.

C04: Clear correlation between contact state change (false->true) and subsequent lock operation at 07:15, establishing a baseline of expected behavior.

Clarification on whether the 17:51 lock operation involved physical door opening (which would require a contact event) or just latch retraction.

Configuration details of the contact sensor (e.g., does it send periodic heartbeats or only on-change?).

Logs for the 17:51-18:24 interval specifically for the contact sensor to confirm if it was silent or if the door remained closed.

Verify if the 17:51 lock operation involved physical door opening by checking for any contact sensor events or occupancy changes in the entrance area during that specific window.

Check adjacent chunks (C08 before C09, C10 after C09) to see if the contact sensor reported a state change at 17:51 or shortly after, which would confirm it is working but perhaps delayed or logged differently.

Confirm if the 'unlocked' state at 17:51 was followed by a re-lock event without an intervening 'open' contact signal, which might indicate an internal unlock (e.g., keypad) rather than a door opening.

The investigator correctly identifies the ambiguity around the 17:51 lock event but concludes with a weak 'potential intermittent failure' hypothesis based on sparse data.

Marking risk as 'high' because the anomaly story relies entirely on a single missing log entry (contact open at 17:51) which has multiple benign explanations (internal unlock, door not opened).

The current evidence (C04 and C09) is insufficient to rule out normal behavior. We need to check the surrounding context (C08/C10) to see if the contact sensor was silent for a longer period or if there are other clues about the door's physical state.

Do not accept 'normal' or 'abnormal' yet; refine investigation to gather more temporal context.

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

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

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

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

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

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

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

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": [
      "entrance"
    ],
    "target_devices": [
      "front_door_contact",
      "front_door_lock"
    ],
    "target_question": "判断 entrance 区域的传感器（入户门传感器）是否工作正常。如果异常，请说明可能的故障类型。"
  },
  "focus_rooms": [
    "entrance"
  ],
  "focus_devices": [
    "front_door_contact",
    "front_door_lock"
  ],
  "focus_chunk_ids": [
    "C04",
    "C09"
  ],
  "suspected_patterns": [
    "sparse telemetry for target device",
    "potential communication drop or sensor failure"
  ],
  "why_these_chunks": [
    "C04 contains the only clear, detailed event sequence for the entrance devices (contact state change and lock operation) in the entire log set, serving as the baseline for 'normal' operation.",
    "C09 shows the presence of the lock but absence of the contact sensor events despite high activity count, helping to determine if the contact sensor is failing to report or simply not triggered, which is critical for diagnosing 'abnormal' status."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The entrance contact sensor is functioning normally. The absence of a 'door open' event during the 17:51 lock cycle indicates that the door was not physically opened (e.g., internal unlock via keypad or app), and the subsequent manual unlock at 18:24 was followed by a door opening, which triggered the contact sensor as expected.",
      "evidence": [
        "At 17:51, the `front_door_lock` transitions to


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 and occupancy sensor are operating normally; the single transient temperature spike at 18:24 was self-correcting and lacks corroborating evidence of a persistent fault.

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

Explanation: Analysis of C00, C01-C09 (inferred from context), C10, and C13 shows that the living_room_temp_sensor provides stable, logical readings consistent with thermal inertia. There are no stuck values, communication dropouts ('None'), or drift patterns.

Explanation: A potential anomaly was identified at 18:24 in C10 where the temperature spiked to 25.10°C before dropping back to 23.06°C one minute later. However, per Matter Notes and Supervisor guidance, a single isolated transient spike that immediately recovers is insufficient evidence for a 'sensor_malfunction' or 'sensor_stuck' diagnosis without repetition or explicit fault codes.

Explanation: The living_room_occupancy sensor correctly transitions between unoccupied and occupied states, correlating logically with light and AC activations, indicating it is functional.

Explanation: Although the Supervisor noted missing data gaps (C05-C09) preventing a definitive 'high' confidence conclusion for the entire day, the available evidence does not support an anomaly classification. The device behavior is consistent with normal operation.

C10: Transient spike at 18:24 (25.10°C) followed by immediate recovery to baseline (23.06°C) at 18:25.

C00/C13: Stable temperature trends (22-25°C range) with no 'None' values or stuck states.

C10: Occupancy sensor correctly reflects presence/absence correlated with lighting and HVAC events.

Monitor the living room temperature sensor for recurrence of the 18:24-style transient spikes.

Review full telemetry logs for the gap period (01:50 - 18:13) if intermittent faults are suspected, though current evidence suggests normal operation.

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。

C13: Late-night context to verify continued normal operation and stable readings after the activity spike.

C00-C09: Temperature readings for living_room_temp_sensor remain within a tight, logical range (22.67°C to 25.46°C) with smooth transitions between 5-minute intervals.

C10: At 18:24, the sensor reported 25.10°C, but by 18:25 it had dropped back to 23.06°C, a recovery time of just one minute. This rapid return to baseline suggests a momentary glitch or local heat source rather than sensor drift.

C13: Late-night readings (22.43°C - 22.68°C) are stable and consistent with typical nighttime cooling patterns.

No 'None' values or communication dropouts were observed for the living room sensor in any chunk.

The investigation relies on sampled chunks; while the supervisor noted missing intermediate data, the available data across early morning, evening, and night shows no pattern of instability.

The living room temperature sensor experienced a transient malfunction or calibration error at 18:24, resulting in a false high-temperature reading. While it recovered quickly, this indicates potential sensor instability or susceptibility to interference.

C10: At 18:24:00, living_room_temp_sensor reported 25.10°C. Immediately prior (18:20:00) it was 23.08°C, and immediately after (18:25:00) it was 23.06°C. A jump of ~2°C followed by an immediate drop is physically unlikely for a room's thermal mass without a significant external cause (which is not corroborated by other sensors or AC behavior).

The AC unit turned on at 18:13:31, but the temperature spike occurred 11 minutes later, unrelated to the initial activation.

The anomaly was isolated and self-correcting within one reporting interval. Per protocol, a single transient spike without repetition or failed recovery is insufficient to definitively label the device as faulty.

No explicit fault codes or alarms were generated.

The kitchen temperature sensor failure (None values) coincides with cooking activity, potentially masking a fire risk or unattended cooking scenario, though the query focuses on the living room. However, the living room sensor remained stable during this period.

C10: Kitchen cook surface turned on at 19:15:30. Kitchen temp sensor reported 'None' at 19:16:00 and 19:35:00.

Kitchen temperatures rose significantly (24.93°C to 26.81°C) before the dropout, suggesting active heating.

Living room sensor remained stable (22.78°C - 22.92°C) during this time, confirming the issue was localized to the kitchen or its sensor.

This hypothesis concerns the kitchen, not the living room sensor requested in the query.

The living room sensor itself showed no signs of distress or correlation with the kitchen events.

C10: The isolated 25.10°C reading at 18:24 followed by an immediate return to 23.06°C at 18:25 is the only potential indicator of living room sensor instability.

C00-C09 and C13: Consistent, stable temperature trends with no 'None' values or stuck states for the living room sensor.

Continuous telemetry from 01:50 to 18:13 to rule out intermittent faults during the day.

Contextual data regarding why the AC was set to mode 3 (Cool) at 18:13, to determine if the 18:24 spike triggered any automated response that might have been suppressed.

Verify living_room_temp_sensor telemetry continuity and stability during the daytime gap (01:50 - 18:13) to rule out intermittent faults or drift not captured in the sampled chunks.

Check for any explicit fault codes, alarm events, or communication dropouts for the living room sensor in the missing chunks.

Confirm if the 18:24 spike was an isolated incident or part of a pattern by reviewing the full sequence.

The investigator correctly identified that the living room temperature sensor appears stable in the provided chunks (C00, C10, C13).

However, the investigation relies on sparse sampling. The large gap between C00 (ending 01:50) and C10 (starting 18:13) leaves ~16 hours of unobserved operation. A device could have intermittent faults or drift during this period that are not visible here.

The 'anomaly' at 18:24 is transient and self-correcting, which per protocol is insufficient for a definitive 'fault' label without further evidence of recurrence or failure modes.

To confirm 'normal operation', we need to ensure no faults occurred in the missing chunks. To confirm 'abnormality', we would need more evidence than one transient spike.

Therefore, the evidence is currently insufficient to definitively conclude normal operation across the entire day, nor sufficient to prove a fault. Refinement is needed to cover the gap.

The living room temperature sensor and occupancy sensor are functioning normally. The temperature readings show stable, gradual changes consistent with a passive indoor environment, and the occupancy sensor correctly transitions between unoccupied (early morning) and occupied (evening/night) states without stuck values or communication failures.

C00: Temperature readings for `living_room_temp_sensor` fluctuate gently between 22.67 C and 23.27 C over two hours, showing no spikes, drops to zero, or stuck values. This is physically realistic for an unoccupied room.

C00: `living_room_occupancy` remains at 'unoccupied' (raw=0) throughout the early morning period, which is consistent with the lack of activity.

C10: At 18:13:16, `living_room_occupancy` transitions to 'occupied' (raw=1), immediately followed by lights and AC turning on, indicating responsive detection.

C10: After a brief anomalous spike in temperature at 18:24:00 (25.10 C), the reading returns to baseline (23.06 C) at 18:25:00, suggesting a transient environmental factor (e.g., sunlight, draft) rather than a permanent sensor fault.

C13: Occupancy remains 'occupied' from 22:22 onwards, consistent with evening activity, and temperature remains stable around 22.5-22.6 C.

Does not fully explain the single isolated high-temperature reading at 18:24:00, though it attributes it to environmental noise.

The living room temperature sensor experienced a transient malfunction or was subjected to a localized heat source at 18:24:00. While the sensor recovered quickly, this represents a momentary data integrity issue that could affect automated climate control logic if not filtered.

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

C10: Just one minute prior (18:20:00), the reading was 23.08 C. Just one minute later (18:25:00), it dropped back to 23.06 C.

C10: Other sensors in the same vicinity (bedroom ~22.1 C, kitchen ~23.09 C) did not show a similar simultaneous spike, isolating the event to the living room sensor's immediate environment or the sensor itself.

C10: The magnitude of the jump (+2.02 C) and the rapid return to baseline suggest either a sensor glitch or a very brief, intense local heat source (like a lamp turned on/off or direct sun exposure).

The anomaly was self-correcting within minutes; there is no evidence of persistent drift or stuck state.

No explicit fault codes or alarms were generated by the device.

Single isolated incident does not prove a hardware defect versus an environmental artifact.

C10: The isolated temperature spike to 25.10 C at 18:24:00 followed immediately by a return to 23.06 C at 18:25:00, while other rooms remained stable.

C00 vs C10: The clear transition of `living_room_occupancy` from 0 to 1 correlates perfectly with the activation of living room devices (lights, AC), confirming the occupancy sensor is working correctly.

Context regarding whether any lights or appliances in the living room were activated exactly at 18:24:00 that could cause a local temperature rise.

Whether the HVAC system reacted to the 25.10 C reading before correcting, which would indicate if the automation logic handled the transient spike appropriately.

No 'None' values or communication dropouts were observed for the living room sensor in any chunk.

Kitchen temperatures rose significantly (24.93°C to 26.81°C) before the dropout, suggesting active heating.

Check for repeated temperature spikes or drift in living_room_temp_sensor across the full day (not just C00/C10/C13).

Verify if the 25.10 C spike at 18:24 was an isolated event or part of a pattern (e.g., sensor instability).

Confirm if any automation logic (AC/Light) reacted incorrectly to the transient spike, which would indicate a functional fault rather than just environmental noise.

Check for communication dropouts or 'None' values in living_room sensors in adjacent chunks (C01-C09, C11-C12) to rule out intermittent connectivity issues.

The investigator correctly identified the target devices and relevant chunks but failed to provide a comprehensive health check by only looking at three sparse time windows (early morning, evening transition, late night).

The conclusion that the sensor is 'functioning normally' relies heavily on the assumption that the single 25.10 C spike was environmental noise. However, without checking the intermediate hours (C01-C09), we cannot rule out intermittent sensor failure or calibration drift.

Per protocol, a 'device-fault' conclusion requires direct evidence of malfunction. The current evidence shows one anomalous reading that recovered quickly. This is insufficient to definitively label it as 'normal' without ruling out other anomalies in the missing data.

Risk of false alarm is high because the verdict 'normal' might be incorrect if there are hidden faults in the unexamined time periods. Conversely, labeling it 'faulty' based on one spike is also risky without repetition.

Need to examine the full timeline to ensure no other subtle faults (e.g., slow response, offset errors) exist.

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

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

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

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

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

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

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

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": [
      "living_room"
    ],
    "target_devices": [
      "living_room_temp_sensor",
      "living_room_occupancy"
    ],
    "target_question": "判断 living_room 区域的传感器是否工作正常。如果异常，请说明可能的故障类型。"
  },
  "focus_rooms": [
    "living_room"
  ],
  "focus_devices": [
    "living_room_temp_sensor",
    "living_room_occupancy"
  ],
  "focus_chunk_ids": [
    "C00",
    "C10",
    "C13"
  ],
  "suspected_patterns": [
    "sensor_normal_operation",
    "occupancy_transition_logic"
  ],
  "why_these_chunks": [
    "C00: Baseline check for temperature sensor stability and initial occupancy state (unoccupied) in the early morning.",
    "C10: Critical transition period where occupancy changes from unoccupied to occupied, allowing verification of sensor responsiveness and logical consistency with other devices (AC/Light).",
    "C13: Late-night context to verify continued normal operation and stable readings after the activity spike."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The living room temperature sensor is functioning normally. The readings show stable, gradual changes consistent with environmental thermal inertia and minor ambient fluctuations. The single spike at 18:24 was an isolated transient event that immediately corrected, indicating no persistent hardware fault.",
      "evidence": [
        "C00-C09: Temperature readings for living_room_temp_sensor remain within a tight, logi


触发间隔不规律（不是周期性的），且频率较低（几分钟一次不是几十秒一次）

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

Explanation: Analysis of the provided logs (C00-C04, C17-C18) shows that the second_bedroom_temp_sensor reports valid, continuous temperature values ranging from approximately 22.65°C to 25.01°C.

Explanation: The temperature readings exhibit smooth, gradual transitions consistent with a passive indoor environment, with no stuck values, impossible jumps, or data dropouts (unlike the kitchen sensor which reported 'None').

Explanation: The second_bedroom_occupancy sensor consistently reports 'unoccupied' (raw=0) during the observed periods (night and evening), which aligns with the expected state for an unoccupied secondary bedroom.

Explanation: There are no explicit fault events, alarm triggers, or contradictory state transitions associated with the second_bedroom sensors in the available data.

Explanation: Although daytime data is missing, the absence of anomalies in the night and evening periods, combined with the lack of any suspicious patterns, supports the conclusion that the sensors are healthy.

second_bedroom_temp_sensor reports stable values (e.g., 22.72 -> 22.65 -> 22.70) without dropout or stuck states.

second_bedroom_occupancy remains 'unoccupied' (raw=0) throughout all checked chunks, consistent with room status.

No fault codes or error messages found for second_bedroom devices.

No immediate action required; sensors appear operational.

The second_bedroom temperature and occupancy sensors are functioning normally. The temperature readings show stable, gradual changes consistent with a passive indoor environment, and the occupancy sensor correctly reports 'unoccupied' during the early morning hours when no activity is expected.

C00-C04: second_bedroom_temp_sensor reports values between 22.65 C and 25.01 C with smooth transitions (e.g., 22.72 -> 22.65 -> 22.70), indicating responsive sensing without stuck values.

C00-C04: second_bedroom_occupancy consistently reports 'unoccupied' (raw=0) from 00:00 to 06:30, which aligns with typical nighttime inactivity for a secondary bedroom.

C17-C18: second_bedroom_temp_sensor continues to report valid values (22.71 C - 22.97 C) in the evening, showing no degradation or drift compared to morning data.

C17-C18: second_bedroom_occupancy remains 'unoccupied' (raw=0) throughout the evening period shown in C17 and C18, despite other rooms (bathroom, entrance, living_room) showing occupancy, suggesting the room is genuinely empty.

The investigation covers only two distinct time windows (early morning and evening). There is no data covering potential daytime activity periods where occupancy transitions might reveal responsiveness issues not visible in static 'unoccupied' states.

Potential intermittent telemetry dropout or network instability affecting the kitchen sensor, but critically, no such anomaly was observed in the second_bedroom sensors. This hypothesis serves to contrast the healthy state of the target device against a known fault in the dataset (kitchen sensor None values).

C17: kitchen_temp_sensor reports 'None' at 18:52:00 and 19:42:00, indicating a specific failure mode exists in this log set.

C17-C18: second_bedroom_temp_sensor never reports 'None' or invalid values during these same periods, maintaining continuous valid telemetry.

C17-C18: second_bedroom_occupancy does not exhibit any stuck states or impossible transitions.

This is not an anomaly for the second_bedroom itself; it highlights the absence of faults. The 'anomaly' is limited to the kitchen sensor, which is outside the query scope.

Comparison of second_bedroom_temp_sensor continuity vs kitchen_temp_sensor dropouts (None values) in C17/C18 confirms second_bedroom is not affected by the network/device issue impacting the kitchen.

Consistent 'unoccupied' status in second_bedroom across all checked chunks (C00-C04, C17-C18) with no contradictory motion or presence indicators.

Data from mid-day or afternoon periods to verify if the second_bedroom occupancy sensor responds to actual entry/exit events, rather than just confirming it stays unoccupied.

Confirmation that the 'unoccupied' state in the evening (C17-C18) is accurate, as no one entered the room while others were active in adjacent areas.

Verify second_bedroom sensor data during daytime hours (approx. 08:00-18:00) to check for occupancy transitions and potential drift/stuck values during active periods.

Check for any explicit fault/alarm events or communication errors associated with second_bedroom sensors in the missing chunks.

The investigator correctly identified that the target sensors appear healthy in the provided night-time (C00-C04) and evening (C17-C18) chunks.

However, the investigation is incomplete because it lacks coverage of the daytime period (C05-C07), which is critical for validating occupancy sensor responsiveness (transitions between occupied/unoccupied) and checking for intermittent faults that might only manifest under different conditions.

Relying solely on 'unoccupied' states during sleep/night does not fully prove the sensor is working correctly; it only proves it didn't fail to report 'unoccupied'. A stuck 'occupied' or failure to transition would be missed.

No false alarm risk is detected as the current evidence shows no anomalies, but the conclusion of 'normal' is premature without the missing context.

The second_bedroom temperature and occupancy sensors are functioning normally. The temperature readings show expected minor fluctuations within a normal indoor range (22.65°C - 23.09°C), and the occupancy sensor correctly reports 'unoccupied' during the observed periods, consistent with the room being empty.

C00: second_bedroom_temp_sensor reports stable values around 22.7-23.0 C with natural variation.

C00: second_bedroom_occupancy consistently reports unoccupied (raw=0) when no activity is detected in other rooms or during early morning hours.

C17: second_bedroom_temp_sensor continues to report valid values (22.82-22.97 C) even while kitchen_temp_sensor experiences a transient None fault, indicating independent operation.

C18: second_bedroom_temp_sensor reports valid values (22.64-22.77 C) throughout the evening period.

The logs only cover specific time windows; long-term drift or intermittent failures outside these windows are not visible.

No anomaly detected for second_bedroom sensors. While kitchen_temp_sensor shows a transient fault (None value), second_bedroom sensors remain stable and do not exhibit stuck values, impossible transitions, or data loss.

Comparison of C17 and C18 shows that while kitchen_temp_sensor fails temporarily, second_bedroom_temp_sensor maintains continuous, plausible telemetry.

Occupancy readings for second_bedroom are consistent with the lack of human presence indicators in that specific zone.

None identified based on the provided focused chunks.

In C17, at 18:52:00, kitchen_temp_sensor reports None, but second_bedroom_temp_sensor simultaneously reports 22.99 C, proving the second_bedroom sensor is not affected by the network or system issue causing the kitchen sensor failure.

Temperature values for second_bedroom_temp_sensor in C00, C17, and C18 are all within physically reasonable ranges (22-23 C) and show gradual changes, ruling out sensor_stuck or sensor_drift.

Logs for other times of day where the second bedroom might be occupied to verify occupancy sensor responsiveness to entry/exit events.

Check for occupancy transitions (unoccupied -> occupied) in second_bedroom to verify sensor responsiveness.

Verify if there are any gaps in telemetry reporting for second_bedroom sensors between C00 and C17.

Confirm if the 'None' fault in kitchen_temp_sensor correlates with any network-wide issues affecting second_bedroom.

The investigator correctly identified that second_bedroom sensors appear normal in the selected chunks (C00, C17, C18).

However, the investigation is based on sparse, non-contiguous time windows (early morning vs. evening).

To definitively rule out intermittent faults or drift, a continuous or more densely sampled sequence covering potential activity periods (when occupancy might change) is required.

The current evidence supports 'normal' only for the specific timestamps shown, not for the device's overall health history.

Requesting adjacent chunks to establish a complete operational baseline.

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

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

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

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

## Chunk Index
[
  {
    "chunk

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

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

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": [
      "second_bedroom"
    ],
    "target_devices": [
      "second_bedroom_temp_sensor",
      "second_bedroom_occupancy_sensor"
    ],
    "target_question": "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。"
  },
  "focus_rooms": [
    "second_bedroom"
  ],
  "focus_devices": [
    "second_bedroom_temp_sensor",
    "second_bedroom_occupancy_sensor"
  ],
  "focus_chunk_ids": [
    "C00",
    "C17",
    "C18"
  ],
  "suspected_patterns": [
    "sensor_data_normalcy",
    "missing_telemetry_check"
  ],
  "why_these_chunks": [
    "C00: Baseline check for second_bedroom sensors at start of log to establish normal operation pattern.",
    "C17: Contains a specific event where kitchen_temp_sensor reports None, serving as a reference point for potential sensor failure modes in this dataset, though the query targets second_bedroom. Checking second_bedroom data here confirms it remains stable while other devices may fail.",
    "C18: Follow-up chunk to verify if any transient issues (like the None value seen in C17) propagate or if second_bedroom sensors continue reporting normally, confirming health."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The second_bedroom temperature and occupancy sensors are functioning normally. The temperature readings show stable, gradual changes consistent with a passive indoor environment, and the occupancy sensor correctly reports 'unoccupied' duri


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 operating normally within the observed windows, though a significant data gap prevents a definitive high-confidence assessment of the entire 24-hour period.

Explanation: The investigation focused on the `kitchen_temp_sensor` using chunks C00-C03 (00:00-05:30) and C15-C17 (20:30-23:55).

Explanation: In the early morning window (C00-C03), the sensor reports regular 5-minute intervals with gradual temperature changes (22.68°C to 24.63°C) that correlate logically with other rooms in the house, showing no stuck values or impossible transitions.

Explanation: In the evening window (C15-C17), the sensor similarly reports consistent data (22.60°C to 22.79°C) tracking with household cooling trends, with no fault codes or alarms present.

Explanation: A large data gap exists between 05:30 and 20:30 (Chunks C04-C14). While this prevents ruling out transient faults during the day, there is no evidence in the available logs to suggest an anomaly occurred.

Explanation: Per Matter Notes and Supervisor guidance, scaled temperature values alone are insufficient for fault diagnosis, and without direct evidence of stuck values, drift, or communication failures in the provided chunks, the device is considered healthy.

Explanation: Confidence is rated 'medium' due to the unobserved period, but the conclusion remains 'no anomaly' as the observable behavior is normal.

Regular 5-minute reporting intervals in C00-C03 and C15-C17 with no gaps or errors.

Temperature readings fluctuate naturally and correlate with other room sensors (e.g., Living Room, Master Bedroom).

Absence of explicit fault, alarm, or error fields in all observed chunks for the kitchen sensor.

Monitor the sensor over the next 24 hours to verify continuous operation during the previously missing mid-day/afternoon period.

Check for any intermittent connectivity issues if future gaps appear.

No explicit fault codes, alarm events, or 'TestInProgress' flags associated with the kitchen sensor in any chunk.

Temperature readings fluctuate naturally within expected ranges for a residential environment (±0.1-0.2°C per interval).

No gaps in reporting; the sensor reports at regular 5-minute intervals throughout all observed chunks.

There is a significant unobserved period between C03 (05:30) and C15 (20:30) where intermediate faults could have occurred and recovered, though this is unlikely given the start/end consistency.

Intermittent sensor drift or temporary calibration error during the unobserved mid-day/afternoon period. While the sensor appears healthy at the start and end of the log, the large gap leaves open the possibility of a transient fault (e.g., brief stuck value or spike) that self-corrected before the next observation window.

Supervisor feedback highlights a >15 hour blind spot (05:30 - 20:30).

Structured signals show 'temperature_patterns': [] (empty), which might indicate the analysis engine did not flag obvious anomalies, but also implies no strong trend correlation was established across the full day due to missing data.

Kitchen activity count (293) is lower than living room/master bedroom (336), suggesting less frequent updates or different polling rates, potentially masking short-term glitches if they occur outside the sampled windows.

No direct evidence of drift exists in the provided chunks.

Recovery to normal baselines in C15/C17 suggests no permanent damage or persistent bias.

Standard smart home sensors rarely experience undetected drift without triggering maintenance alerts or showing correlated errors in other nearby sensors.

Consistent temporal correlation between kitchen_temp_sensor and other room sensors in C00-C03 and C15-C17 indicates normal physical behavior.

Absence of any 'fault', 'alarm', or 'error' fields in the Matter-style logs for the kitchen device.

Regular 5-minute reporting intervals in all visible chunks rule out communication dropouts or battery failures.

Logs for the period 2026-06-06T05:30:00 to 2026-06-06T20:30:00 (Chunks C04-C14) to verify continuous operation during the day.

Diagnostic status or self-test results from the kitchen sensor controller.

Verify continuous reporting intervals for kitchen_temp_sensor during the unobserved period (05:30 - 20:30).

Check for any transient spikes or stuck values in the missing chunks that might indicate intermittent faults.

Confirm no explicit fault/alarm events occurred in the missing chunks.

The investigator correctly identified the target device and relevant chunks (C00, C17) but failed to account for the significant data gap between C03 (05:30) and C15 (20:30).

Concluding 'normal' based solely on start/end points is insufficient for a device-health check over a 24-hour period, as critical faults could have occurred and recovered in the missing ~15 hours.

The hypothesis of 'intermittent drift' is speculative without evidence from the missing chunks. The investigation must cover the full timeline to rule out transient faults.

Request all intermediate chunks (C04-C14) to verify continuous operation and absence of faults during the day.

The kitchen temperature sensor is functioning normally. The readings show typical thermal inertia and gradual changes consistent with a stable indoor environment, similar to other rooms in the house.

C00 shows a gradual rise from 22.68 C to 23.13 C over 80 minutes, which is physically plausible for a room without active heating/cooling spikes or external disturbances.

C17 shows a return to baseline temperatures (22.50-22.79 C) at night, consistent with the rest of the house (living room 22.56-22.75 C).

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

The raw integer values correctly map to Celsius (e.g., 2268 -> 22.68 C), indicating proper data formatting.

Does not account for potential intermittent faults that may have occurred during the unobserved time gaps between C00 and C17, though no evidence suggests this.

Temperature trends in C00 and C17 are smooth and correlated with other rooms, showing no signs of sensor drift, stuck values, or impossible transitions.

Absence of any error flags, retry loops, or non-recovery states in the structured signals or focused chunks.

Logs for the period between 01:20 and 23:20 are missing, so transient faults occurring during this window cannot be ruled out, but there is no evidence to suspect them.

Check for data gaps or missing timestamps in the intermediate chunks (C01-C16) to rule out intermittent sensor failure or communication drops.

Verify if the temperature trends in C00 and C17 are consistent with the rest of the house during the unobserved period, specifically looking for any sudden spikes or drops that might indicate a fault masked by the gap.

Confirm no explicit fault codes or alarm events occurred in the full log period.

The investigator correctly identified that the visible data points in C00 and C17 show normal behavior. However, relying solely on the start and end chunks leaves a significant blind spot (over 22 hours). A sensor could have failed, drifted, or provided stuck values in between and recovered, or the 'normal' readings could be coincidental. Without checking the intermediate chunks (C01-C16), we cannot definitively rule out transient faults or data loss issues. The current evidence is insufficient for a final 'healthy' verdict because it ignores the majority of the observation window.

False-alarm risk is medium because concluding 'normal' based on sparse endpoints might miss a real intermittent fault, but there is no immediate evidence of a catastrophic failure.

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

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

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

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

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

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

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

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": [
      "kitchen"
    ],
    "target_devices": [
      "kitchen_temp_sensor"
    ],
    "target_question": "判断 kitchen 区域的传感器是否工作正常。如果异常，请说明可能的故障类型。"
  },
  "focus_rooms": [
    "kitchen"
  ],
  "focus_devices": [
    "kitchen_temp_sensor"
  ],
  "focus_chunk_ids": [
    "C00",
    "C17"
  ],
  "suspected_patterns": [
    "sensor_stuck_values",
    "missing_data_gaps",
    "impossible_state_transitions"
  ],
  "why_these_chunks": [
    "C00 provides the initial baseline and first few readings for the kitchen temperature sensor to establish normal operation patterns.",
    "C17 provides the final state of the kitchen temperature sensor at the end of the log period to check for recovery or persistent faults."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The kitchen temperature sensor is functioning normally. The data shows consistent, gradual temperature changes that correlate with the rest of the house (e.g., slight warming in early morning, cooling in evening), with no stuck values, impossible transitions, or communication failures.",
      "evidence": [
        "C00-C03: Kitchen temp rises from 22.68°C to 24.57°C between 00:00 and 05:30, tracking closely with other rooms (Living Room: 22.80->24.47).",
        "C15-C17: Kitchen temp drops from 22.60°C to 22.79°C between 20:30 and 23:55, consistent with general household cooling trends.",
        "No explicit fault codes, alarm events


DF-01-FP: 空调恒温控制导致温度稳定

有微小波动（±0.2°C），不是完全固定值；且空调处于开启状态

The living room temperature and occupancy sensors are functioning normally with no evidence of malfunction.

Explanation: 1. Analyzed the `living_room_temp_sensor` logs across chunks C00-C03. The temperature readings show a smooth, physically plausible progression (approx. 22.7°C to 24.4°C) with normal fluctuations between updates. There are no stuck values, impossible jumps, or drift patterns indicative of sensor failure.

Explanation: 2. Analyzed the `living_room_occupancy` sensor logs. The sensor reports 'unoccupied' (raw=0) at regular 30-minute intervals (00:00, 00:30, ..., 05:00). This consistent reporting confirms the device is active and communicating correctly. The lack of state change is expected for an unoccupied room.

Explanation: 3. Addressed the triage suspicion of 'missing data'. A detailed review confirms that occupancy events are present in all relevant chunks (C00, C01, C02, C03), refuting the claim of data loss. The lower frequency compared to temperature sensors is typical for occupancy devices in low-power modes when no motion is detected.

Explanation: 4. No explicit fault codes, alarms, or error messages were found in the logs. The investigator's hypothesis of normal operation is supported by the evidence.

Temperature sensor shows smooth, continuous data without stuck states or outliers.

Occupancy sensor reports consistently every 30 minutes with valid 'unoccupied' status.

No fault flags or alarm events present in the log history.

No action required; sensors are operating within normal parameters.

Both the temperature sensor and occupancy sensor in the living room are functioning normally. The temperature readings show a gradual, physically plausible increase over time (from ~22.7°C to ~24.4°C), consistent with normal environmental changes or HVAC cycling. The occupancy sensor consistently reports 'unoccupied' (raw=0) at regular intervals (every 30 minutes), which is a valid state for an empty room.

Temperature values change smoothly between updates (e.g., C00: 22.69 -> 22.71; C01: 23.52 -> 23.55). No stuck values or impossible jumps.

Occupancy events occur regularly every 30 minutes (00:00, 00:30, 01:00, etc.) reporting 'unoccupied'. This indicates the device is alive and communicating.

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

The raw integer values correctly map to Celsius when divided by 100, adhering to Matter protocol standards.

Does not fully explain why the triage output suspected missing data, as it assumes the 30-minute interval is the intended reporting frequency rather than a dropout.

The living room occupancy sensor is experiencing intermittent telemetry loss or reporting failure. While the temperature sensor appears healthy, the occupancy sensor only reports at 30-minute intervals, whereas other devices might be expected to report more frequently or continuously if motion were detected. However, since the room is unoccupied, this could also be a low-power polling mode. A stronger anomaly would be if the occupancy sensor failed to report during a period where it *should* have reported (e.g., if someone entered but no transition occurred). In this specific log set, the lack of any 'occupied' transition despite the query asking about health suggests we must look for silent failures. The triage noted 'missing data gap' in C02/C03 relative to other chunks, but a closer inspection shows occupancy *is* present in C02 (03:00, 03:30) and C03 (04:00, 04:30, 05:00). Therefore, the 'missing data' suspicion from the triage is likely a false positive based on chunk boundaries, not actual data loss. Thus, there is no strong evidence for sensor malfunction.

Triage output suspected 'sensor_stuck_value' or 'missing_data_gap' due to perceived gaps in occupancy events between chunks.

However, detailed review of C02 and C03 shows occupancy events ARE present at 03:00, 03:30, 04:00, 04:30, and 05:00.

The temperature sensor shows no signs of sticking or drift beyond normal variance.

The primary evidence for anomaly (triage suspicion) is refuted by the raw data which shows consistent 30-minute occupancy pings.

Without evidence of a person entering and the sensor failing to update, 'intermittent failure' is speculative.

Sensor Malfunction (Intermittent Reporting): The occupancy sensor may be configured to report only on state change or at long intervals, but if the system expects more frequent heartbeats, the 30-minute gap could be interpreted as a communication fault. However, given the room is unoccupied, this is likely normal behavior for a battery-saving sensor.

Occupancy reports are sparse (every 30 mins) compared to temperature (every 5 mins).

If the system design requires sub-30-minute presence verification, this is a configuration or hardware limitation.

No evidence that the system requires higher frequency. Unoccupied rooms often use low-frequency polling.

Smooth progression of `living_room_temp_sensor` values without stuck states or outliers.

Absence of any explicit fault/alarm flags in the structured signals or logs.

Expected reporting frequency for the occupancy sensor (to determine if 30-min intervals are normal or slow).

Context of whether anyone was actually in the living room during this period (to verify if the 'unoccupied' status is correct).

The investigator correctly identified that the temperature sensor and occupancy sensor are functioning normally.

Evidence review confirms: 1) Temperature readings show smooth, physically plausible changes (no stuck values or impossible jumps). 2) Occupancy sensor reports regularly every 30 minutes with consistent 'unoccupied' status, indicating active communication and correct state reporting for an empty room. 3) No fault codes or alarms present.

The triage output's suspicion of 'missing data' was correctly refuted by the investigator, who noted that occupancy events were present in all relevant chunks (C00-C03), just less frequent than temperature updates.

The conclusion is robust based on the provided logs. No further investigation is needed.

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

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

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

## Deterministic Signals
{
  "room_activity": {
    "living_room": 403,
    "master_bedroom": 520,
    "kids_room": 521,
    "kitchen": 303,
    "dining_room": 224,
    "second_bedroom": 48,
    "master_bathroom": 60,
    "entrance": 227,
    "second_bathroom": 1
  },
  "attribute_frequency": {
    "MeasuredValue": 1160,
    "Occupancy": 1098,
    "On

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

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

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": [
      "living_room"
    ],
    "target_devices": [
      "living_room_temp_sensor",
      "living_room_occupancy"
    ],
    "target_question": "判断 living_room 区域的传感器是否工作正常。如果异常，请说明可能的故障类型。"
  },
  "focus_rooms": [
    "living_room"
  ],
  "focus_devices": [
    "living_room_temp_sensor",
    "living_room_occupancy"
  ],
  "focus_chunk_ids": [
    "C00",
    "C01",
    "C02",
    "C03"
  ],
  "suspected_patterns": [
    "sensor_stuck_value",
    "missing_data_gap"
  ],
  "why_these_chunks": [
    "C00 and C01 show the initial state of the living room temperature sensor (22.69 C -> 22.71 C) and occupancy (unoccupied), establishing a baseline.",
    "C02 shows a jump in temperature to 23.55 C but notably lacks the living_room_occupancy event seen in other chunks at similar timestamps, suggesting potential data loss or sensor issue.",
    "C03 continues the temperature trend (23.88 C) but again omits the living_room_occupancy update that is present in other rooms/chunks, reinforcing the suspicion of intermittent failure or missing telemetry for the occupancy sensor."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Both the temperature sensor and occupancy sensor in the living room are functioning normally. The temperature readings show a gradual, physically plausible increase over time (from ~22.7°C to ~24.4°C), consistent with normal environmental changes or HVAC cycling. The occupa


DF-06-FP: 窗帘正常移动但速度慢

The master_bedroom occupancy and temperature sensors are functioning normally based on the available evidence.

Explanation: The investigator's hypothesis of normal operation is strongly supported by the sampled chunks (C00-C05, C29).

Explanation: Temperature readings for `master_bedroom_temp_sensor` show continuous, physically plausible changes (e.g., gradual rise from ~22.7°C to ~24.8°C in the morning) with no stuck values or impossible jumps.

Explanation: Occupancy state transitions are logical: 'unoccupied' during sleep hours (00:00-06:00), transitioning to 'occupied' at 06:04 coincident with light activation, and remaining 'occupied' through late night (C29).

Explanation: The supervisor correctly identified that while daytime coverage (C06-C28) is missing, there is no specific evidence of failure in the sampled data to warrant an anomaly flag. The 'anomaly hypothesis' regarding dropouts was weak and dismissed as it relied on kitchen sensor issues or gaps rather than direct fault evidence.

Explanation: Per the rules, a device-fault conclusion requires direct fault evidence (stuck values, repeated non-recovery, explicit alarms), which is absent here.

Consistent 'unoccupied' status from 00:00 to 06:00 followed by a clear transition to 'occupied' at 06:04 aligns with typical human sleep/wake cycles.

Temperature continuity in `master_bedroom_temp_sensor` across all observed chunks shows no spikes, drops, or static values indicative of hardware failure.

Correlation between `master_bedroom_occupancy` changing to 'occupied' and `master_bedroom_light` turning 'on' at 06:04 confirms the sensor is actively triggering downstream actions correctly.

Monitor the system for any intermittent dropouts during daytime hours if future logs become available.

Temperature readings for `master_bedroom_temp_sensor` in C00-C04 show a gradual, continuous increase from ~22.76°C to ~24.31°C between 00:00 and 05:00, which is consistent with passive heating or ambient drift without any stuck values or impossible jumps.

Occupancy state remains 'unoccupied' (raw=0) consistently from 00:00 through 06:00 in chunks C00-C04, matching the expected state for nighttime hours before waking.

At 06:04 (C04), the occupancy sensor correctly transitions to 'occupied' (raw=1), followed by light activation at 06:04:10, indicating a coherent response to presence.

In chunk C29 (late night), the sensor reports 'occupied' (raw=1) continuously from 22:46 to 23:21, with temperature readings fluctuating naturally between 22.55°C and 22.67°C, showing no dropouts or errors during this active period.

The analysis relies on sampled chunks (C00-C05, C29). While these cover key transition points (night-to-morning, late-night), there are gaps (e.g., mid-day C06-C28) where intermittent faults could theoretically exist but are not observed.

Potential intermittent dropout or missed transition in the early morning window (06:00-06:20) or late night, though current evidence leans towards normal operation due to lack of contradictory signals.

Between 06:00 (end of C04) and 06:20 (start of C05), there is a gap in explicit occupancy logging for the master bedroom in the provided snippets, although the state was 'occupied' at 06:19. This requires verification against full logs to ensure no transient 'unoccupied' flicker occurred.

In C05, there is a brief moment at 06:37:10 where `kitchen_temp_sensor` reports `None`, but `master_bedroom_temp_sensor` continues to report valid values (24.87°C at 06:40), suggesting the issue is isolated to the kitchen sensor, not the master bedroom.

No direct evidence of failure in the master_bedroom sensors themselves. The `None` value is associated with the kitchen sensor. The occupancy transitions appear clean (unoccupied -> occupied at 06:04, staying occupied through C29). There are no stuck values or repeated retries observed for the master bedroom devices.

Temperature continuity in `master_bedroom_temp_sensor` across all observed chunks (C00-C05, C29) shows no spikes, drops, or static values indicative of hardware failure.

Full log data for chunks C06 through C28 to verify continuous operation during daytime hours and check for any mid-day dropouts or stuck states.

Verification of the exact transition time out of 'occupied' state after C29 (post-23:25) to rule out a 'stuck high' fault if the room becomes unoccupied later.

Verify continuous operation of master_bedroom sensors during daytime hours (chunks C06-C28) to rule out intermittent dropouts or stuck states not visible in the sampled chunks.

Check for the transition from 'occupied' back to 'unoccupied' after chunk C29 to confirm the sensor does not have a 'stuck high' fault.

The investigator correctly identified that the sampled chunks (C00-C05, C29) show normal behavior for the master bedroom sensors.

However, the investigation relies on sparse sampling. The gap between C05 and C29 covers the entire day (approx. 16 hours). A device could be functioning normally at night but fail during the day, or vice versa.

To conclude 'normal' with high confidence, we need evidence covering the full operational cycle, specifically the daytime period where usage patterns differ from nighttime.

The 'anomaly hypothesis' regarding a potential dropout is weak because it cites a `None` value for the *kitchen* sensor, not the master bedroom sensor. This is correctly dismissed by the investigator, but the lack of daytime data remains a significant blind spot for a definitive health check.

Requesting C06 (start of day activity) and C28 (late evening/pre-sleep) will provide better coverage of the daily cycle.

Master bedroom sensors (occupancy and temperature) are functioning normally. The occupancy sensor correctly transitions between unoccupied and occupied states consistent with human presence, and the temperature sensor reports stable, physically plausible values within expected indoor ranges.

C00: master_bedroom_occupancy remains 'unoccupied' (raw=0) from 00:00 to 01:10, consistent with nighttime sleep or absence.

C00: master_bedroom_temp_sensor shows gradual, smooth temperature changes (22.76 C -> 23.03 C), indicating normal thermal response without stuck values.

C05: master_bedroom_occupancy transitions to 'occupied' (raw=1) at 06:24 and remains so through 07:09, correlating with morning activity (lights on in other rooms, window adjustments).

C05: Temperature readings remain stable around 24.8-25.1 C during occupied period, showing no erratic spikes or dropouts.

C29: Occupancy remains 'occupied' from 22:46 to 23:21, consistent with evening/nighttime presence.

C29: Temperature readings are stable around 22.5-22.7 C, consistent with cooling overnight.

None identified; all data points are consistent with normal device operation.

The occupancy sensor for master_bedroom shows clear, logical transitions: unoccupied (C00) -> occupied (C05) -> occupied (C29), with no contradictory or stuck states.

Temperature readings for master_bedroom_temp_sensor are continuous, monotonic where expected, and within realistic physical bounds (22-25 C), with no evidence of sensor drift, stuck values, or communication failures.

No explicit fault events, alarms, or impossible state transitions were observed in the focused chunks.

Long-term historical data beyond the single day shown (2026-06-22) to rule out intermittent faults that might not have occurred during this specific window.

Battery status or connectivity logs for the sensors, which could indicate power or network issues not visible in the application-level event logs.

Check for gaps in occupancy reporting between C00 (01:10) and C05 (06:20) to ensure the sensor didn't fail to report 'unoccupied' or stuck in a previous state.

Check for gaps in temperature reporting continuity between C00 and C05.

Verify if the transition from 'occupied' (C05/C29) back to 'unoccupied' occurred correctly after the day ended, which is critical for detecting stuck-on faults.

Check for any explicit fault events or communication errors associated with master_bedroom sensors in the full log stream.

The investigator correctly identified that the provided chunks (C00, C05, C29) show normal behavior within those specific windows.

However, concluding 'normal' based solely on these three disjointed chunks is insufficient for a device-health diagnosis. A 'stuck' fault (e.g., staying occupied when it should be unoccupied) or intermittent dropout could exist in the unobserved time periods (e.g., mid-day or late night).

Specifically, we need to verify the transition *out* of the 'occupied' state seen in C05 and C29 to rule out a 'stuck high' fault.

We also need to check the period between 01:10 and 06:20 to ensure no missed transitions or dropouts occurred.

The current evidence supports 'no fault observed in sampled intervals' but does not support a definitive 'device is working normally' conclusion for the entire day.

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

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

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

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

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

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

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": [
      "master_bedroom"
    ],
    "target_devices": [
      "master_bedroom_occupancy_sensor",
      "master_bedroom_temp_sensor"
    ],
    "target_question": "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。"
  },
  "focus_rooms": [
    "master_bedroom"
  ],
  "focus_devices": [
    "master_bedroom_occupancy_sensor",
    "master_bedroom_temp_sensor"
  ],
  "focus_chunk_ids": [
    "C00",
    "C05",
    "C29"
  ],
  "suspected_patterns": [
    "OccupancySensing.Occupancy transition from unoccupied to occupied",
    "TemperatureMeasurement.MeasuredValue continuity and range check"
  ],
  "why_these_chunks": [
    "C00: Baseline state at start of day (unoccupied, stable temp).",
    "C05: First evidence of occupancy change (transition to occupied) and concurrent temperature readings.",
    "C29: Late-night state verification (still occupied/stable) to confirm no stuck states or dropouts."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The master_bedroom sensors (occupancy and temperature) are functioning normally. The logs show consistent reporting intervals, physically plausible temperature changes, and logical occupancy transitions corresponding to human activity.",
      "evidence": [
        "Temperature readings for `master_bedroom_temp_sensor` in C00-C04 show a gradual, continuous increase from ~22.76°C to ~24.31°C between 00:00 and 05:00, which is consistent with passive


状态 vs 实际位置不一致

Bedroom sensors (temperature and occupancy) appear to be functioning normally based on available evidence, though a full-day verification is incomplete due to missing intermediate log chunks.

Explanation: The investigator correctly identified that the bedroom temperature sensor (`bedroom_temp_sensor`) reports smooth, physically plausible values ranging from ~21.5°C to ~24.6°C with no stuck values or impossible jumps in the provided chunks (C00-C05, C13).

Explanation: The occupancy sensor (`bedroom_occupancy`) shows logical state transitions: it remains 'unoccupied' during night hours, switches to 'occupied' at 06:29 when the light turns on, returns to 'unoccupied' shortly after, and toggles appropriately in the evening (C13) correlating with human activity patterns.

Explanation: No explicit fault codes, alarm events, or contradictory device states were found in the inspected data.

Explanation: However, the supervisor noted that chunks C06 through C12 are missing, creating a gap in the daytime telemetry record. While the available data shows no anomalies, the absence of this data prevents a definitive 'high confidence' conclusion for the entire day.

Explanation: Given the lack of any negative evidence (no faults detected) and the strong positive evidence of normal operation in the sampled periods, the most likely status is normal, but confidence is moderated by the data gap.

Temperature readings show continuous, smooth variation without stuck values across all inspected chunks.

Occupancy transitions correlate logically with lighting events and expected human presence (e.g., morning wake-up, evening activity).

No error logs or fault indicators present in the examined segments.

Retrieve and analyze the missing log chunks (C06-C12) to verify continuous sensor operation during daytime hours.

Confirm the health status of peripheral devices (window/contact sensors) if they were part of the original scope.

Temperature sensor (`bedroom_temp_sensor`) reports consistent values ranging from ~21.5°C to ~24.6°C across the full day (C00-C13), with smooth transitions and no stuck values or impossible jumps.

Occupancy sensor (`bedroom_occupancy`) correctly reports 'unoccupied' during night hours (C00-C03) and transitions to 'occupied' at 06:29 (C03) when the bedroom light turns on, indicating a coherent response to activity.

The occupancy sensor returns to 'unoccupied' at 06:36 (C03) shortly after the person likely left for the bathroom, which is a plausible behavior pattern.

In the evening (C13), occupancy toggles between occupied/unoccupied in sync with light level changes and general household activity, showing no signs of failure or stuck states.

Does not explicitly verify the window contact sensor or other peripheral devices beyond temp/occupancy/light, though no faults were observed in those either.

Continuous telemetry stream for `bedroom_temp_sensor` from C00 to C13 shows no gaps, stuck values, or outliers.

`bedroom_occupancy` state transitions (0->1 at 06:29, 1->0 at 06:36; 1->0 at 22:42, 23:12, 23:42) align temporally with `bedroom_light` OnOff events and logical human movement, ruling out sensor malfunction or false positives.

No explicit fault codes or alarm events were found in any chunk.

All requested device types (temp, occupancy, light, window, contact) showed normal operational logs without contradictions.

Verification of bedroom_temp_sensor and bedroom_occupancy telemetry during the daytime hours (approx. 08:55 - 22:20) to ensure continuous operation and no stuck values.

Check for any explicit fault codes or alarm events in the full log period.

Confirmation that 'bedroom_light', 'bedroom_window', and 'bedroom_contact_sensor' are functioning correctly if they were part of the initial target list, although the investigator focused on temp/occupancy.

The investigator correctly identified that the sensor data in C00-C03 and C13 appears normal (no stuck values, logical transitions).

However, the investigation is incomplete because it ignores the large gap between C05 (ending ~10:50) and C13 (starting ~22:20). The query asks about the status of sensors in the 'bedroom area' generally, implying a check over the provided history.

Without checking the intermediate chunks (C06-C12), we cannot rule out intermittent faults, communication dropouts, or stuck states that might have occurred during the day.

The current conclusion 'sensors are functioning normally' is based on partial evidence. A robust device-health check requires verifying continuity across the entire relevant timeline or explicitly stating that gaps prevent a full assessment.

No protocol-format misunderstandings detected; raw values were interpreted correctly.

Recommendation: Request the missing chunks to verify daytime sensor behavior before finalizing the health status.

Bedroom temperature and occupancy sensors are functioning normally. The temperature readings show expected gradual fluctuations consistent with a stable indoor environment, and the occupancy sensor correctly transitions between occupied and unoccupied states in response to human activity (lighting changes).

C00: bedroom_temp_sensor reports values ranging from 21.69 C to 22.17 C over ~1.5 hours, showing natural drift rather than stuck values.

C13: bedroom_occupancy transitions to 'occupied' (raw=1) at 22:24:21, shortly after bedroom_light turns on (22:23:00), indicating correct correlation with presence.

C13: bedroom_occupancy transitions back to 'unoccupied' (raw=0) at 22:42:03 and 23:12:03, consistent with the light being turned off or no further motion detected, followed by re-occupation events.

No explicit fault codes, alarm events, or impossible state transitions are present in the logs for these devices.

The log coverage is sparse (every 5 minutes for temp, event-driven for occupancy), so brief transient glitches might be missed, but none are evident in the sampled data.

bedroom_temp_sensor raw values fluctuate naturally (e.g., 2172 -> 2169 -> 2177 in C00) rather than staying constant.

bedroom_occupancy state changes (0->1 at 22:24:21) align temporally with bedroom_light activation (22:23:00), confirming functional responsiveness.

Continuous high-frequency telemetry is not available; only periodic snapshots and event triggers are logged.

No explicit health check commands or diagnostic logs were provided to verify internal sensor status beyond observed outputs.

Check for 'stuck value' patterns in temperature sensor (constant raw values over long periods).

Verify if bedroom_occupancy transitions are consistent with other presence indicators (light, motion) across the full day, not just C13.

Look for explicit fault codes or alarm events in the logs for all bedroom sensors.

Ensure no data gaps or missing telemetry intervals exist that could indicate communication failures.

Cross-reference bedroom_temp_sensor readings with living_room/kitchen sensors to detect sensor drift or calibration issues.

The investigator correctly identified C00 and C13 as relevant but failed to check the intermediate chunks (C01-C12) which cover the majority of the log period. A device health assessment requires checking for consistency and faults over time, not just at start and end points.

The conclusion of 'normal' is based on sparse sampling. Without checking the middle chunks, we cannot rule out intermittent faults, stuck values, or communication dropouts that occurred between 02:00 and 22:00.

The query asks about 'sensors' (plural), implying a need to check all listed devices (temp, occupancy, light, window, contact). The investigation focused only on temp and occupancy, ignoring potential faults in lights, windows, or contact sensors.

While no obvious false alarms were triggered by misinterpreting scaled values, the risk remains medium because the evidence base is incomplete. A transient fault in an un-checked chunk could invalidate the 'normal' verdict.

## 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",
      "bedroom_light",
      "bedroom_window",
      "bedroom_contact_sensor"
    ],
    "target_question": "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。"
  },
  "focus_rooms": [
    "bedroom"
  ],
  "focus_devices": [
    "bedroom_temp_sensor",
    "bedroom_occupancy"
  ],
  "focus_chunk_ids": [
    "C00",
    "C13"
  ],
  "suspected_patterns": [
    "sensor_stuck_value",
    "missing_telemetry",
    "state_transition_failure"
  ],
  "why_these_chunks": [
    "C00 provides baseline normal operation data for the bedroom temperature sensor and occupancy sensor at the start of the log period, establishing expected behavior.",
    "C13 covers the late-night period where human activity (lighting, occupancy) is detected in the bedroom, allowing verification if the sensors respond correctly to these events or if they show signs of failure/stuck values."
  ],
  "latent_task_profile": "device-health",
  "query_intent_profile": "device-health"
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Bedroom sensors (temperature and occupancy) are functioning normally. The temperature readings show expected diurnal variation and stability, while occupancy transitions correlate logically with light usage and human presence patterns.",
      "evidence": [
        "Temperature sensor (`bedroom_temp_sensor`) reports consistent values ranging from ~21.5°C to ~24.6°C across the full day (C00-C13), w