DF-02-FP: 夏季午后持续升温（正常日照效应）

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

The living room temperature and occupancy sensors are operating normally; the observed temperature spike was a transient event consistent with environmental factors or sensor noise, not a hardware fault.

Explanation: Analysis of the temperature logs shows that the living room sensor generally reports values within normal physical bounds (21-24°C) with gradual changes typical of indoor thermal inertia.

Explanation: A significant anomaly occurred in Chunk C10 where the temperature spiked to 25.10°C at 18:24 but immediately recovered to 23.06°C at 18:25. This transient behavior, isolated to a single minute without affecting neighbor sensors or persisting, is characteristic of a localized environmental disturbance (e.g., HVAC airflow draft) or momentary sensor noise rather than a permanent hardware malfunction like a stuck value or drift.

Explanation: The Matter notes explicitly state that scaled temperature values alone are insufficient for a fault conclusion, and no explicit alarm or fault events were logged.

Explanation: The occupancy sensor demonstrates logical consistency with user presence (lights/AC on) and maintains correct boolean states (occupied=1) throughout the relevant chunks, showing no evidence of being stuck or failing to transition.

Explanation: Since there is no recurrence of the spike in other chunks (C00, C13) and no other fault indicators, the device health is assessed as normal.

C10: Temperature spike from 23.08°C to 25.10°C recovered instantly to 23.06°C within one minute.

C00/C13: Temperature readings remain stable and within realistic ranges (22-23°C).

Occupancy sensor consistently reports 'occupied' aligned with active devices (lights/AC), indicating functional logic.

Monitor for recurrence of similar transient spikes to rule out intermittent HVAC drafts.

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

enumerated lock state rendered as label + raw code

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

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

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 sustained occupancy to check for long-term drift or stuck values.

Living room occupancy sensor shows consistent transitions between occupied/unoccupied states aligned with light/AC usage, indicating no stuck values.

Temperature readings across all chunks (C00, C13) remain within realistic physical bounds (21-24°C) with gradual drifts typical of indoor thermal inertia.

Raw values are correctly scaled (e.g., 2510 = 25.10°C), showing no protocol parsing errors.

The magnitude of the spike (+2.02°C in 1 minute) is physically possible but unusually fast for ambient air without direct heat source interaction, requiring assumption of local airflow dynamics.

Living room temperature sensor exhibits intermittent measurement error or hardware instability, characterized by rapid, high-magnitude fluctuations not fully explained by stable ambient conditions.

C10: At 18:24:00, the sensor reported 25.10°C, which is +2.02°C higher than the previous reading (23.08°C) and significantly deviates from the bedroom (22.09°C) and kitchen (23.08°C) which were stable.

The recovery was immediate (23.06°C at 18:25:00), creating a 'spike' pattern often associated with sensor contact issues or internal ADC glitches rather than slow environmental changes.

While the kitchen sensor had its own anomalies (cooking heat), the living room spike lacked a corresponding actuator change (AC was on, but temp dropped instantly, contradicting expected thermal lag if AC caused it).

Single isolated incident; no recurrence in C00 or C13 reduces probability of permanent hardware failure.

Matter notes state scaled temperatures alone are insufficient for fault conclusion; this spike could be a valid transient physical event (e.g., vent blowing directly on sensor).

C10 timestamp 18:24:00: living_room_temp_sensor reports 25.10°C while neighbors report ~23.0°C, followed by immediate return to baseline at 18:25:00.

Occupancy sensor consistency: Living room occupancy remains logically consistent with user presence (lights/AC on) throughout all chunks, ruling out sensor-wide data loss.

HVAC duct configuration near living room sensor to determine if air draft could cause 2°C/min swings.

Sensor maintenance history to check for battery levels or known firmware bugs related to rapid sampling.

Window/Door status logs for living room to rule out sudden ingress of outside air.

Verification of occupancy sensor 'stuck' behavior: The logs show `living_room_occupancy` reporting `occupied` continuously from 18:13 to 23:45 without any transition to `unoccupied`. In a typical smart home scenario, this is highly suspicious for an occupancy sensor (unless the user never left), but given the query asks about 'working normally', the lack of state transitions makes it impossible to verify dynamic functionality. However, since no fault *events* are logged and values are consistent with presence, it's likely just a long-duration stay or sensor logic quirk. The investigator correctly noted consistency but didn't flag the lack of transitions as a potential 'false normal' risk if the user actually left.

Clarification on the temperature spike cause: While the investigator attributes it to transient noise/airflow, the magnitude (+2C in 1 min) is physically aggressive for ambient air unless directly affected by a vent or sunlight. However, since it recovered instantly and neighbors were stable, hardware fault is unlikely. The conclusion of 'normal' is robust enough for this dataset.

The investigator correctly identified that scaled values are handled properly (no false alarms on units).

The investigation focuses correctly on the living room sensors.

The core anomaly (temp spike) is analyzed well: isolated, transient, recovered. This meets the bar for 'not a fault'.

The occupancy sensor data is static (always occupied in C10/C13). While not necessarily a fault, it provides no evidence of *dynamic* health. However, in the absence of contradictory evidence (e.g., lights off but occupancy high), we cannot declare a fault. The investigator's conclusion of 'normal operation verification' is acceptable because there is no evidence of malfunction.

No further chunks are needed to rule out faults based on current evidence. The pattern is clear: stable baseline, one transient spike, stable recovery.

The living room temperature and occupancy sensors are functioning normally. The temperature readings show typical thermal inertia and fluctuation consistent with an unoccupied/occupied residential environment, and the occupancy sensor correctly transitions between states based on human presence.

C00: Temperature fluctuates gradually between 22.67°C and 23.27°C over ~1.5 hours while unoccupied, showing normal drift.

C10: Occupancy sensor switches from 'unoccupied' (raw=0) to 'occupied' (raw=1) at 18:13:16, correlating with lights/AC turning on.

C13: Occupancy remains 'occupied' (raw=1) continuously from 22:22 to 23:45, consistent with sustained presence.

Temperature values remain within realistic physical bounds (22-23°C range) across all chunks.

Does not fully account for the isolated high-temperature spike in C10 without ruling out transient environmental factors or sensor noise.

The living room temperature sensor experienced a transient glitch or measurement error, evidenced by a sudden, large, and immediate temperature spike followed by a return to baseline, which is inconsistent with the slow thermal mass of the room.

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

At 18:25:00 (just 1 minute later), the reading drops back to 23.06°C (raw=2306).

This represents a ~2°C jump in 1 minute and a recovery in the next minute, which is physically unlikely for a room's air temperature given the previous trend (~23°C).

Other sensors (bedroom, kitchen) do not show similar spikes at this exact time, isolating the anomaly to the living room sensor or its local environment.

Could be caused by a temporary heat source (e.g., sunlight hitting a specific spot, a device placed near the sensor) rather than a sensor fault.

Single data point anomaly; no repeated pattern of sticking or drifting observed in other chunks.

C10 18:24:00 living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 25.10 C (raw=2510) followed immediately by 23.06 C at 18:25:00.

C10 18:13:16 living_room_occupancy transition from unoccupied to occupied matches actuator changes (lights/AC).

Contextual logs regarding window/door status or HVAC airflow specifically during the 18:24 timestamp to rule out external heat sources.

Raw signal strength or battery level indicators for the temperature sensor to check for low-power transmission errors.

Verify if the C10 spike (25.10°C) is a transient glitch or actual physical event by checking for correlated HVAC activity or window status at that exact timestamp.

Check other temperature sensors in adjacent rooms (bedroom/kitchen) for simultaneous spikes to rule out local environmental factors vs sensor hardware fault.

Confirm if 'None' values in kitchen sensor indicate data loss or just lack of reporting, ensuring network stability for living room sensor.

The investigator correctly identified a potential anomaly (temp spike in C10) but flagged it as a likely malfunction without sufficient ruling out of environmental causes (e.g., AC airflow, sunlight).

According to Matter Notes, scaled temperatures alone are not enough for a fault conclusion. The spike (25.1°C) is physically possible (e.g., from a vent or draft), whereas a value like 2466°C would be an obvious scaling error.

The current evidence shows only one isolated spike followed by immediate recovery. This is ambiguous between 'transient noise/glitch' and 'environmental interference'.

No direct fault evidence (stuck values, repeated failures, explicit alarm events) was found. The occupancy sensor logic appears sound.

Further investigation is needed to distinguish between sensor noise/hardware issue and environmental context before concluding 'malfunction'. However, given the single point and rapid recovery, it leans towards non-faulty behavior or minor glitch, but 'abstain' might be premature if we can confirm no other anomalies exist.

Recommendation: Refine investigation to explicitly look for environmental correlates or dismiss based on lack of recurrence. If no other chunks show similar behavior, the verdict should likely be 'Normal' with a note about transient noise, rather than 'Malfunction'.

Since the query asks to judge if they work normally and explain faults *if* abnormal, and the evidence is currently inconclusive between normal (with noise) and abnormal (glitch), we need to tighten the criteria. The current 'Anomaly Hypothesis' is weak because it attributes causality to the sensor without evidence.

Action: Refine to allow final verdict of 'Normal' if no other evidence supports fault, OR refine to check specific environmental logs if available. Given the constraints, refining to confirm the nature of the spike is best.

## 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": [
      "temperature_sensor",
      "occupancy_sensor"
    ],
    "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_stuck_value",
    "sensor_no_data_gap",
    "normal_operation_verification"
  ],
  "why_these_chunks": [
    "C00: Early morning baseline data to check initial sensor responsiveness and temperature range.",
    "C10: Evening occupancy event to verify occupancy sensor transitions (unoccupied -> occupied) and temp consistency.",
    "C13: Late night sustained occupancy to check for long-term drift or stuck values."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Living room sensors are functioning normally. The temperature spike in C10 was a transient environmental event (e.g., HVAC airflow or sensor self-calibration noise) that resolved immediately, consistent with normal operation.",
      "evidence": [
        "C10: Temperature spike from 23.08°C to 25.10°C occurred at 18:24 but recovered to 23.06°C by 18:25 (1-minute duration).",
        "No other adjacent sensors (bedroom, kitchen) showed simultaneous spikes, suggesting the event was localized to the living room environment or specific sensor noise rather than a whole-house fault.",
        "Living room occu


DF-04-FP: 门锁响应延迟但最终成功锁定

The entrance sensors and lock are functioning normally; the observed behavior represents standard auto-lock cycles triggered by contact sensor state changes.

Explanation: Analysis of C04 (07:15) and C12 (22:09) shows a consistent and correct causal chain: `front_door_contact` transitions to true (closed), immediately followed by `front_door_lock` executing an Auto Lock operation. This confirms the contact sensor is correctly triggering the lock.

Explanation: In C09 (17:51), although the specific `LockOperation` event log is missing or suppressed in the snippet, the state transition from unlocked to locked occurs rapidly (within 5 seconds). Given that `front_door_contact` reports 'false' (open) at 18:24 after a manual unlock, it is highly probable the door was closed prior to 17:51, triggering the auto-lock as seen in other chunks.

Explanation: There is no evidence of stuck states (e.g., remaining open when it should be locked, or failing to lock after closure in multiple instances). The device recovered and operated correctly in subsequent events.

Explanation: The supervisor's concern about missing intermediate chunks (C05-C08) does not constitute proof of fault, especially since the functional logic is verified in C04 and C12. The 'empty line' in C09 is likely a logging artifact rather than a device malfunction.

C04: Contact sensor closing at 07:15:22 triggers Auto Lock at 07:15:23.

C12: Contact sensor closing at 22:09:37 triggers Auto Lock at 22:09:38.

C09: Rapid state change to locked at 17:51:08 without prolonged unsafe state.

No immediate action required; devices appear healthy.

Continue monitoring for consistency in future logs if the missing event logs persist, but treat current data as normal operation.

判断 entrance 区域的传感器（入户门传感器 contact_sensor、门锁 door_lock）是否工作正常，若异常说明故障类型。

C03, C04, C09, C12 are the only chunks containing 'entrance' room activity (event counts > 0).

C04 contains specific events for front_door_contact and front_door_lock.

C09 contains front_door_lock in top devices.

Other chunks lack entrance data, making them irrelevant for this specific query.

C12: At 22:09:37, `front_door_contact` is true (closed). At 22:09:38, `front_door_lock` performs an Auto Lock operation and becomes locked. Consistent with N1 pattern.

C03: At 07:15:18, `front_door_lock` is unlocked via Manual operation. This is consistent with the user having just entered or being inside.

C09: At 18:24:00, `front_door_lock` is unlocked manually. At 18:24:02, `front_door_contact` is false (open), indicating the user exited. The absence of an immediate auto-lock here suggests the auto-lock timer had not yet expired or the door remained open long enough, which is normal behavior.

The 'empty line' in C09 at 17:51:00 lacks explicit context, but if interpreted as a logging artifact rather than data loss, the subsequent rapid state change fits a normal auto-lock cycle.

Potential logging gap or transient signal glitch during an auto-lock event in C09. While not a permanent 'stuck' fault, the lack of a visible trigger event (LockOperation) or corresponding contact sensor state change immediately preceding the lock/unlock sequence at 17:51:00-17:51:08 raises a question about whether the auto-lock was properly triggered or if there was a momentary communication failure between the contact sensor and the lock.

C09: At 17:51:00, there is an empty log line for `front_door_lock`. At 17:51:03 it becomes unlocked, and at 17:51:08 it becomes locked. There is no intervening `LockOperation` event logged for this specific transition, nor is there a clear `front_door_contact` state change in the immediate vicinity (previous contact log is not shown in C09 snippet, but C04 shows contact logic works).

Supervisor Feedback Note: The previous investigator incorrectly labeled a 2-second transition as 'stuck'. However, the *absence* of the triggering event (LockOperation) in the logs for the 17:51 sequence is a genuine ambiguity. If the lock auto-locked without a recorded trigger, it might indicate a configuration issue or a dropped packet from the contact sensor that failed to log.

This could simply be a display/logging format issue where the `LockOperation` event was suppressed or occurred before the chunk start time. The device eventually reached a stable 'locked' state, so no permanent malfunction occurred.

No evidence of the door remaining unlocked when it should have been, or stuck in an unsafe state.

Battery/Power Instability causing intermittent reporting gaps. The empty line in C09 and the slight timing discrepancies in other chunks could suggest low battery power affecting the wireless connection stability, leading to missed events.

C09: Empty line at 17:51:00 suggests a packet loss or device hang.

Low activity room (entrance has only 17 activity count vs living room 397), making battery status harder to verify from high-frequency polling logs, but silence can sometimes indicate power-saving modes or failures.

No explicit low-battery warnings in the logs.

Other devices in the same network segment (bedroom, kitchen) show regular updates, suggesting the hub/network is fine, isolating the issue potentially to the specific device or its local radio.

C04: Clear correlation between `front_door_contact` becoming true (closed) and `front_door_lock` executing an Auto Lock operation. This proves the contact sensor successfully triggers the lock.

C12: Similar successful correlation at 22:09:37-22:09:38.

C09: Ambiguous sequence at 17:51:00-17:51:08 with missing trigger event, contrasting with the clear triggers in C04/C12.

Raw log lines surrounding 17:51:00 in C09 to see if a `LockOperation` event exists but was omitted in the summary view.

Contact sensor state logs for the entrance room immediately before 17:51:00 to confirm if the door was detected as closed prior to the lock engaging.

Device health/battery status logs for `front_door_lock` and `front_door_contact`.

Retrieve chunks between C04 and C09 (C05-C08) to locate the missing LockOperation event or contact sensor state change for the 17:51 sequence.

Verify if 'front_door_lock | ' at 17:51:00 is a logging artifact or indicates a device hang/connection drop.

Check if front_door_contact reported 'false' (open) immediately before 17:51:03 to support an auto-unlock hypothesis, or 'true' (closed) to support an auto-lock trigger.

Confirm battery status or signal strength logs if available, as intermittent connectivity could explain the missing event log.

The investigator correctly identified that C04 and C12 show normal operation, but failed to definitively resolve the ambiguity in C09.

The gap in evidence at 17:51:00-17:51:08 in C09 is critical. The investigator speculates about 'logging gaps' but does not verify this against intermediate data.

Crucially, chunks C05, C06, C07, and C08 are missing from the investigation. These likely contain the events leading up to or following the 17:51 timestamp, which would clarify whether the lock was manually unlocked (missing manual event log) or if there was a communication failure.

Without checking these intermediate chunks, we cannot distinguish between a 'stuck sensor', 'missing log', 'manual override without log', or 'device fault'.

The current conclusion of 'normal with potential minor glitch' is unsupported by direct evidence for the anomaly case. Refinement is required to either confirm the glitch or rule it out.

Entrance sensors (contact and lock) are functioning normally. The observed state changes correspond to legitimate user activity (entering/exiting/locking/unlocking) with no signs of hardware failure or sensor drift.

The front_door_contact sensor correctly transitions between false (closed) and true (open) in response to door movement events (C04, C09, C12).

The front_door_lock sensor updates its LockState accurately following LockOperation events (Manual/Auto), transitioning between locked (1) and unlocked (2) states as expected.

No 'stuck' values were observed; the contact sensor did not remain open/closed indefinitely without cause, and the lock responded immediately to commands.

Temperature data for other rooms shows normal fluctuations, suggesting the system clock and general telemetry pipeline are working correctly, ruling out a systemic reporting error.

Does not account for potential micro-latencies or missed frames if the sampling rate is low, though the provided logs appear sufficient for basic health checks.

Possible 'false negative' or brief communication drop for the front_door_contact sensor during the morning entry event. At 07:15:20, the contact reported 'false' (closed) while the lock was being manually operated/unlocked, followed by 'true' (open) at 07:15:22. This rapid flip might indicate the sensor failed to capture the initial opening or reported a transient state incorrectly before stabilizing.

At 2026-06-12T07:15:18, `front_door_lock` performs a Manual Unlock.

Immediately after, at 07:15:20, `front_door_contact` reports `StateValue = false` (closed).

Just 2 seconds later at 07:15:22, it flips to `true` (open).

In a typical scenario, unlocking the door often coincides with the door being slightly ajar or opened, which should trigger the contact to go 'open'. Reporting 'closed' briefly during/after unlock suggests a potential timing mismatch or momentary sensor glitch.

The duration is very short (2 seconds), which could simply be due to the door being closed and re-opened quickly, or the sensor update delay relative to the mechanical action. It does not constitute a clear failure like a stuck value.

Lock state inconsistency or race condition during the evening event. At 17:51:01, the lock state is listed as empty/null in the log snippet, followed by an immediate transition from Unlocked to Locked within 5 seconds without a corresponding visible LockOperation event source (like Manual/Auto) explicitly logged between those two state lines, although one might have occurred just outside the snippet boundaries or been suppressed.

At 2026-06-12T17:51:01, `front_door_lock` shows an empty event line or missing status.

At 17:51:03, LockState becomes `unlocked`.

At 17:51:08, LockState becomes `locked`.

There is no explicit `LockOperation` event logged between 17:51:03 and 17:51:08 that explains the auto-locking behavior, unlike other entries where Auto locks are explicitly flagged.

The log snippet for C09 might be truncated or formatted such that the intermediate operation event was omitted. The 5-second interval is consistent with standard auto-lock timers, making this likely a logging omission rather than a fault.

The sequence at C04 (07:15:20-22) showing Contact=false then true around a manual unlock, which is slightly counter-intuitive if the door was being opened.

The absence of explicit LockOperation events in C09 for the quick lock/unlock cycle at 17:51, compared to the detailed logging elsewhere.

Raw binary logs or higher-frequency telemetry to confirm if the contact sensor truly flipped twice or if it was a single delayed report.

Configuration settings for the auto-lock timer to verify if the 5-second lock at 17:51 was triggered by software timeout.

Battery levels for the entrance devices to rule out low-power transmission issues causing dropped packets or delayed states.

C09: Empty line at 17:51:00 suggests a packet loss or device hang.

The investigator labels A1 as 'sensor_stuck' but the evidence describes a transient state change (false -> true) over 2 seconds, which is not a 'stuck' fault. This is a protocol misunderstanding of fault types.

The investigator relies on C09 where `front_door_lock` shows an empty line at 17:51:00 followed by state changes. Without the preceding contact sensor log or explicit LockOperation event in that specific snippet, it is impossible to confirm if this was a valid auto-lock cycle or a logging gap/fault. The current chunks (C03-C12) are provided, but the analysis of C09 is weak because it lacks the necessary context (contact sensor state) to validate the lock operation causality.

Missing check for battery/power status which could explain dropped packets or delayed states, although often inferred from other logs, explicit mention is needed to rule out low-power faults.

Protocol Misunderstanding: The investigator classifies a rapid state flip (A1) as 'sensor_stuck'. In Matter diagnostics, 'stuck' implies a value failing to change over time. A 2-second transition is likely a timing mismatch or normal mechanical delay, not a stuck fault. This needs correction to avoid false alarms.

Insufficient Evidence for Causality: In Chunk C09, the lock state changes from null/unlocked to locked without a visible trigger event (LockOperation) or corresponding contact sensor event in the immediate vicinity within the provided text. While C12 shows similar patterns, the lack of explicit correlation in C09 makes the conclusion of 'normal behavior' speculative. The investigator admits this is likely a logging omission but presents it as a potential anomaly without resolving it.

Recommendation: Clarify the definition of 'stuck' vs 'transient'. Investigate if the 'empty line' in C09 is a known log formatting artifact or a genuine data loss event. Since the query asks for fault determination, and the evidence is ambiguous regarding the lock's internal logic (auto-lock timer vs manual), further clarification or a different set of logs focusing on the lock's configuration/events might be needed. However, given the constraints, the primary issue is the misclassification of the fault type in A1.

## 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",
      "entrance_light"
    ],
    "target_question": "判断 entrance 区域的传感器（入户门传感器 contact_sensor、门锁 door_lock）是否工作正常，若异常说明故障类型。"
  },
  "focus_rooms": [
    "entrance"
  ],
  "focus_devices": [
    "front_door_contact",
    "front_door_lock"
  ],
  "focus_chunk_ids": [
    "C03",
    "C04",
    "C09",
    "C12"
  ],
  "suspected_patterns": [
    "low_activity_device_monitoring",
    "contact_sensor_state_flip",
    "lock_operation_event"
  ],
  "why_these_chunks": [
    "C03, C04, C09, C12 are the only chunks containing 'entrance' room activity (event counts > 0).",
    "C04 contains specific events for front_door_contact and front_door_lock.",
    "C09 contains front_door_lock in top devices.",
    "Other chunks lack entrance data, making them irrelevant for this specific query."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Entrance sensors (contact and lock) are functioning normally. The observed state changes represent standard operational cycles: manual unlocking for entry/exit, followed by automatic re-locking triggered by the contact sensor detecting door closure or a timer.",
      "evidence": [
        "C04: At 07:15:20, `front_door_contact` transitions from false to true (door closed). Immediately after at 07:15:23, `front_door_lock` executes an Auto Lock operation and moves to 'locked'. This demonstrates a valid causal chain: Contact Sensor -> Auto Lock.",


DF-05-FP: 烟雾报警器正常自检

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

The kitchen temperature sensor is functioning normally, showing dynamic and plausible readings that correlate with other sensors in the home.

Explanation: Analysis of C00 (00:00-01:20) shows the kitchen sensor values fluctuating between 22.67°C and 23.13°C, demonstrating active updates and physical plausibility for an indoor environment.

Explanation: Analysis of C13 (17:55-19:05) confirms continued normal operation with values ranging from 22.92°C to 23.31°C, tracking closely with other room sensors like the living room and second bedroom.

Explanation: There are no signs of 'stuck' values (the sensor changes frequently), drift, or impossible state transitions. No fault codes or disconnection events were logged for this device.

Explanation: Although a smoke alarm self-test occurred at 18:45, it is a distinct event unrelated to the temperature sensor's functionality.

C00: kitchen_temp_sensor values change dynamically (e.g., 22.68 -> 22.67 -> ... -> 23.13).

C13: kitchen_temp_sensor values remain consistent with ambient trends (e.g., 23.31 -> ... -> 22.95).

No explicit fault events, alarms, or connection drops associated with the kitchen sensor.

No immediate action required; continue monitoring.

If user reports specific issues not visible in logs (e.g., slow response), consider physical inspection or battery replacement.

C13 shows kitchen_temp_sensor reporting 23.31 C down to 22.95 C, tracking closely with other sensors (e.g., living_room 23.25 -> 22.97).

No explicit fault events, alarms, or connection drops associated with the kitchen sensor were logged in the provided chunks.

Raw values (e.g., 2268, 2331) convert correctly to Celsius using the centi-degree scale defined in Matter Notes.

Limited time coverage; we only see two short windows (early morning and evening), so long-term drift or intermittent connectivity issues might be missed.

We cannot verify calibration accuracy against a known reference, only relative consistency.

No evidence of sensor malfunction detected in the provided logs. The query asks to check if it works normally, and the data supports normal operation. However, as a counter-check for 'sensor_stuck' or 'sensor_drift', the values clearly change over time and correlate with ambient trends, ruling out these specific faults.

Kitchen sensor values change frequently (e.g., 22.68 -> 22.67 -> 22.71 -> ... -> 23.13 in C00).

Kitchen sensor values change frequently in C13 (23.31 -> 23.25 -> 23.19 -> ... -> 22.95).

No 'TestInProgress' or 'ExpressedState' anomalies linked to the temp sensor itself (the smoke alarm had a self-test, but that is distinct from the temp sensor).

If the user suspects a hardware fault not reflected in software logs (e.g., slow response time), this log analysis cannot detect it without dynamic testing.

Comparison of kitchen_temp_sensor raw values across timestamps in C00 and C13 demonstrates active updating and correlation with other room sensors, contradicting 'stuck value' or 'malfunction' hypotheses.

Absence of any error codes, disconnection events, or implausible temperature spikes/drops in the kitchen sensor logs.

Longer historical logs to assess daily cycles and potential battery/power issues.

Physical inspection data to rule out environmental interference (e.g., heat source proximity) which might cause 'accurate but misleading' readings.

Network connectivity logs specifically for the kitchen sensor to rule out packet loss or delayed updates.

The investigator correctly interpreted Matter-scaled values (centi-degrees Celsius) and avoided the false-alarm trap of treating raw integers as literal temperatures.

Evidence from chunks C00 and C13 is sufficient to determine that the kitchen sensor is functioning normally within the context of available data. The temperature readings are physically plausible, change dynamically (ruling out 'stuck' faults), and correlate logically with other sensors in the home.

No explicit fault events or impossible state transitions were found.

While longer logs would improve confidence regarding drift or intermittent connectivity, the current evidence robustly supports a 'normal operation' verdict for the specific question asked.

## 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",
    "C13"
  ],
  "suspected_patterns": [
    "sensor_stuck_value",
    "normal_operation_verification"
  ],
  "why_these_chunks": [
    "C00 represents the start of the log period with baseline temperature readings for all sensors, allowing comparison of kitchen sensor behavior against others.",
    "C13 shows a later time point where other rooms are also reporting stable temperatures; checking this chunk helps verify if the kitchen sensor continues to report consistent, plausible values over time or exhibits drift/sticking."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The kitchen temperature sensor is functioning normally. The reported temperatures (ranging from ~22.6°C to ~23.3°C) are physically plausible for an indoor environment and track consistently with other rooms, showing typical thermal inertia and minor fluctuations rather than stuck values or drift.",
      "evidence": [
        "C00 shows kitchen_temp_sensor reporting 22.68 C at start and 23.13 C by end of period, following a gradual rise consistent with living_room (22.80 -> 23.13) and master_bedroom (22.66 -> 22.88).",
        "C13 shows kitchen_temp_sensor reporting 23.31 C down to 22.95 C, tracking closely with other sensors


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）

No evidence of sensor malfunction was found in the observed periods; however, a significant gap in data coverage prevents a high-confidence confirmation of continuous operation.

Explanation: The provided log chunks (C00-C04 and C17-C18) cover the early morning and evening periods respectively. In both intervals, the second_bedroom_temp_sensor and second_bedroom_occupancy sensors report consistent, physically plausible temperature values and occupancy states without any 'None' entries or error codes.

Explanation: Specifically, C00-C04 shows normal nocturnal temperature trends (~22.7°C to ~25.0°C) with stable unoccupied status. C17-C18 shows stable evening temperatures (~22.6°C - 22.9°C) while other rooms experience activity, further confirming the sensor is online and reporting correctly during those times.

Explanation: In contrast, the kitchen_temp_sensor exhibits explicit faults ('None' values) at 18:52 and 19:42, indicating intermittent network or hardware issues exist in the system. However, these faults are isolated to the kitchen device and do not affect the second bedroom sensors in the sampled windows.

Explanation: The supervisor correctly identifies that chunks C05 through C16 (approx. 06:48 to 18:45) are missing. Since the kitchen sensor had intermittent failures, there is a non-zero risk that the second bedroom sensor also experienced dropouts during this unobserved daytime period.

Explanation: Because no direct fault evidence exists for the target devices in the available data, and the observed behavior is normal, we cannot classify it as anomalous. However, due to the lack of full-day coverage requested by the supervisor, confidence is limited to medium rather than high.

Second bedroom sensors show continuous, valid data in C00-C04 (00:00-06:48) and C17-C18 (18:45-20:30).

No 'None' values, stuck states, or impossible transitions observed for second_bedroom_temp_sensor or second_bedroom_occupancy in provided logs.

Kitchen sensor faults (C17/C18) demonstrate system-level intermittency, but are distinct from the second bedroom sensor's performance.

Data gap exists for midday hours (C05-C16), preventing definitive exclusion of intermittent failure.

Retrieve and analyze log chunks C05 through C16 to verify continuous operation during daytime hours.

If gaps are found in the missing chunks, investigate potential network congestion or hardware battery issues specific to the second bedroom cluster.

判断 second_bedroom 区域的传感器是否工作正常，如有异常说明故障类型

C00: Baseline check for normal temperature and occupancy readings in the morning.

C17-C18 (18:45 - 20:30): Continuous temperature readings continuing into the evening. Values remain stable (~22.6°C - 22.9°C) while kitchen temperatures spike due to cooking. Occupancy remains 0 (unoccupied).

No explicit fault codes, alarm events, or stuck value patterns observed in any chunk for second_bedroom sensors.

Temperature values are physically plausible and correlate logically with environmental context (e.g., bedroom stays cool while kitchen heats up).

The investigation relies on the specific chunks provided (C00-C04, C17-C18). While these cover morning/night transitions and evening activity, a comprehensive 24-hour health check would ideally verify the midday period (not explicitly detailed in the focused chunks but implied by the lack of anomalies in sampled points) to rule out intermittent daytime failures.

Potential intermittent sensor dropout or communication failure in the second_bedroom device during the unobserved midday period, analogous to the faults seen in the kitchen sensor. Although no direct evidence exists in the provided chunks, the suspicion arises from the supervisor's note regarding missing data checks for the full 24 hours.

Kitchen_temp_sensor exhibited 'None' values (faults) at 18:52 and 19:42 in C17/C18, indicating potential network or hardware instability in the system.

Supervisor feedback explicitly flagged the need to verify sensor continuity across the entire 24-hour period, noting that the gap between 01:20 and 18:45 was largely unexamined in the previous round.

If the second_bedroom sensor dropped out during the day, it would not be detected in C00-C04 or C17-C18.

No direct evidence of missing data, 'None' values, or stuck states for second_bedroom sensors in any of the provided focused chunks (C00-C04, C17-C18).

Occupancy sensor also reports continuously without interruption, suggesting the device is generally online when checked.

Speculative nature: The hypothesis relies on the possibility of failure in unseen data rather than observed failure.

Presence of continuous, valid temperature and occupancy data for second_bedroom_temp_sensor and second_bedroom_occupancy in all provided chunks (C00-C04, C17-C18).

Absence of 'None' values or fault indicators for second_bedroom sensors, contrasting with the explicit faults seen in kitchen_temp_sensor.

Physically consistent temperature progression (22.7°C -> 25.0°C overnight -> 22.7°C evening) matching expected building thermal behavior.

Log chunks covering the midday period (approx. 06:48 to 18:45) to fully confirm continuous operation and rule out intermittent dropouts during high-activity hours.

Explicit confirmation of device connectivity status or heartbeat logs for the second_bedroom cluster during the unobserved time windows.

Verify continuous presence of second_bedroom_temp_sensor and second_bedroom_occupancy data in the unobserved midday/afternoon chunks (C05-C16).

Check for any 'None' values or gaps in the target sensors during high-activity hours, analogous to kitchen sensor faults.

Confirm no stuck values or impossible transitions occurred between the observed morning (C00-C04) and evening (C17-C18) periods.

The investigator correctly identifies that the provided chunks (C00-C04, C17-C18) show normal operation for the second bedroom sensors.

However, there is a massive gap in coverage from ~06:48 to ~18:45 (Chunks C05 through C16). The investigation claims 'normal throughout the entire 24-hour period' based on limited samples, which is insufficient for a definitive health check.

The kitchen sensor faults in C17/C18 suggest potential intermittent network/hardware issues. It is critical to verify if the second bedroom sensors experienced similar dropouts during the unobserved daytime hours.

Do not conclude 'normal' without verifying the missing chunks. Request retrieval and analysis of C05-C16 to rule out intermittent failures.

second_bedroom sensors (temperature and occupancy) are functioning normally. The temperature readings show expected minor fluctuations consistent with a stable indoor environment, and the occupancy sensor correctly reports 'unoccupied' during periods when no other activity is detected in that specific room.

C00: second_bedroom_temp_sensor provides regular updates every 5 minutes with values ranging from 22.65 C to 23.09 C, showing natural thermal drift.

C00 & C17: second_bedroom_occupancy consistently reports 'unoccupied' (raw=0) during early morning and evening hours, which aligns with the absence of specific occupancy events for this room in the logs.

No explicit fault codes, alarm events, or missing data entries are present for second_bedroom devices in the provided chunks.

Temperature values are within reasonable bounds for an indoor residential setting.

The logs only cover a subset of the day (early morning and late evening). Gaps in data (e.g., midday) prevent a full 24-hour health assessment.

Occupancy is binary; it cannot distinguish between 'unoccupied' and 'sensor failure', though lack of conflicting signals makes failure less likely.

No significant anomaly detected for second_bedroom sensors specifically. While kitchen_temp_sensor shows intermittent 'None' values (potential malfunction), second_bedroom sensors remain consistent. Any perceived 'abnormality' might be due to misinterpretation of scaled temperature values or confusion with other rooms.

Comparison with C17/C18: kitchen_temp_sensor explicitly shows `Value = None` at 18:52 and 19:42, indicating a clear fault pattern in that device which is absent in second_bedroom logs.

second_bedroom_temp_sensor values remain numeric and plausible throughout all observed windows.

second_bedroom_occupancy transitions are logical (mostly static unoccupied), lacking the erratic flipping seen in lock_state_flips elsewhere.

Limited temporal coverage means a transient fault occurring outside the logged windows would not be detected.

second_bedroom_temp_sensor provides continuous, valid numeric temperature readings across all timestamps in C00, C17, and C18.

Absence of `Value = None` or error codes for second_bedroom devices, contrasting with the explicit faults seen in kitchen_temp_sensor.

Consistent 'unoccupied' status for second_bedroom_occupancy without contradictory motion/activity signals.

Logs for the midday period (approx. 01:20 - 18:45) to assess sensor behavior during potential daytime activity or HVAC cycles.

Battery level diagnostics for second_bedroom sensors to rule out low-power warnings.

Connectivity signal strength logs to ensure data gaps aren't caused by wireless interference.

Verify sensor continuity across the entire 24-hour period (gaps between 01:20 and 18:45 are significant for health assessment).

Check for intermittent disconnections or 'None' values in second_bedroom sensors during other time windows.

Correlate occupancy changes with temperature stability to detect if the sensor responds correctly to environmental changes.

The investigator only analyzed C00 (early morning) and C17/C18 (evening), leaving a large gap (approx. 17 hours) unexamined. While the sampled data shows no faults, this is insufficient to declare the device 'working normally' for the whole day without ruling out intermittent failures or dropouts in the missing chunks.

The conclusion relies on negative evidence (absence of fault in sampled data) rather than comprehensive positive verification.

No false alarm risk detected as the investigator correctly interpreted scaled temperatures and did not misread raw values.

Further investigation into intermediate chunks is required to ensure consistent reporting.

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

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

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

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

## Chunk Index
[
  {
    "chunk

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

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

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": [
      "second_bedroom"
    ],
    "target_devices": [
      "second_bedroom_temp_sensor",
      "second_bedroom_occupancy"
    ],
    "target_question": "判断 second_bedroom 区域的传感器是否工作正常，如有异常说明故障类型"
  },
  "focus_rooms": [
    "second_bedroom"
  ],
  "focus_devices": [
    "second_bedroom_temp_sensor",
    "second_bedroom_occupancy"
  ],
  "focus_chunk_ids": [
    "C00",
    "C17",
    "C18"
  ],
  "suspected_patterns": [
    "missing_data",
    "sensor_read_failure"
  ],
  "why_these_chunks": [
    "C00: Baseline check for normal temperature and occupancy readings in the morning.",
    "C17 & C18: Inspection of evening period where kitchen_temp_sensor shows 'None' (fault), to cross-reference if second_bedroom sensors exhibit similar missing data or error patterns, although specific faults were observed in other rooms/chunks."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The second_bedroom temperature and occupancy sensors are functioning normally throughout the entire 24-hour period. The data shows consistent reporting intervals, plausible temperature gradients relative to other rooms (e.g., cooler than kitchen during cooking), and stable occupancy states corresponding to unoccupied periods.",
      "evidence": [
        "C00-C04 (00:00 - 06:48): Continuous temperature readings for second_bedroom_temp_sensor with no gaps or 'None' values. Values range from ~22.7°C to ~25.0°C, showing typical nocturnal heating trends. Occupancy remains 0 (unoccupied).",


DF-06-FP: 窗帘正常移动但速度慢

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

Explanation: The investigator's hypothesis N1 is supported by direct evidence in the logs: the master_bedroom_temperature_sensor reports physically plausible temperatures (ranging from ~22.5°C to ~25.1°C) that drift naturally over time, without any stuck values, impossible jumps, or data gaps.

Explanation: The master_bedroom_occupancy_sensor shows logical state transitions, changing from 'unoccupied' (raw=0) during the night to 'occupied' (raw=1) at 06:24, consistent with typical wake-up behavior, and remaining occupied during evening activities.

Explanation: There are no explicit fault events, alarm logs, or error messages associated with these specific devices in the provided chunks (C00, C05, C29).

Explanation: Although a transient null value was observed for the kitchen sensor in C05, this is isolated to that device and does not indicate a systemic network failure or affect the integrity of the master bedroom sensor data.

Temperature readings in C00 (22.68-23.03 C), C05 (24.87-25.09 C), and C29 (22.55-22.67 C) are stable and physically realistic.

Occupancy transition from unoccupied to occupied at 06:24 aligns with human presence patterns.

Absence of fault codes or stuck values for master_bedroom sensors.

No action required; sensors are operating within normal parameters.

Temperature values in C00 range from 22.68 to 23.03 C, consistent with nighttime indoor temperatures.

Temperature values in C05 rise to ~25.09 C by 06:55, consistent with morning heating/activity.

Temperature values in C29 stabilize around 22.5-22.7 C, consistent with late-night cooling.

Occupancy transitions from 'unoccupied' (raw=0) at 01:00 to 'occupied' (raw=1) at 06:24, aligning with typical wake-up times.

Occupancy remains 'occupied' through morning routines (C05) and evening activities (C29), with logical correlations to other room activity (e.g., bathroom usage).

No explicit fault events, alarm logs, or impossible state transitions observed for these specific devices.

The log sampling frequency for occupancy appears lower than for temperature (bulk updates vs. immediate events), which might mask very brief vacate/re-enter events, but this is likely a reporting configuration rather than a fault.

Potential intermittent communication drop or minor data inconsistency affecting the kitchen sensor, potentially indicative of network instability that could theoretically affect nearby sensors like the master bedroom, though no direct fault is seen in the master bedroom sensors themselves.

In C05, at 06:37:10, `kitchen_temp_sensor` reports `TemperatureMeasurement.MeasuredValue = None`, while other sensors report valid values.

This null value recovers immediately at 06:40:00.

Master bedroom sensors do not show similar nulls or stuck values.

The anomaly is isolated to the kitchen sensor, not the master bedroom sensors requested in the query.

Recovery was immediate, suggesting a transient packet loss or temporary read error rather than a persistent device malfunction.

No evidence suggests this impacted master bedroom sensor functionality.

Master bedroom temperature sensor shows continuous, logically consistent readings across all three time blocks (C00, C05, C29) without gaps, spikes, or stuck values.

Master bedroom occupancy sensor shows clear, binary transitions (0->1) corresponding to human presence patterns without erratic toggling or stuck states.

Absence of any 'fault', 'error', or 'alarm' events specifically tagged to master_bedroom devices.

Detailed log entries for intermediate times between the sampled chunks (e.g., mid-day) to confirm no prolonged offline periods occurred.

Battery level or connectivity signal strength metrics if available, to rule out environmental interference issues.

The investigator correctly interpreted Matter-scaled temperature values (centi-degrees) and did not fall for false alarms regarding impossible temperatures.

The focus on `master_bedroom` sensors is correct and adheres to the query scope. The mention of kitchen sensor issues in A1 is correctly dismissed as out-of-scope/irrelevant to the specific fault diagnosis of the target devices.

Evidence from C00, C05, and C29 provides a comprehensive view: baseline night behavior, morning transition/activity, and late-night stability. No stuck values, impossible transitions, or missing data patterns were found for the target sensors.

The conclusion that sensors are working normally is well-supported by the logical consistency of occupancy transitions and physically plausible temperature drifts.

## 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_temperature_sensor"
    ],
    "target_question": "Check if sensors in master_bedroom are working normally; if abnormal, identify fault type."
  },
  "focus_rooms": [
    "master_bedroom"
  ],
  "focus_devices": [
    "master_bedroom_occupancy_sensor",
    "master_bedroom_temperature_sensor"
  ],
  "focus_chunk_ids": [
    "C00",
    "C05",
    "C29"
  ],
  "suspected_patterns": [
    "stuck_values",
    "missing_data",
    "impossible_transitions"
  ],
  "why_these_chunks": [
    "C00: Baseline check for temperature sensor and initial occupancy state.",
    "C05: Check for occupancy transitions (unoccupied to occupied) and temperature stability during morning activity.",
    "C29: Check for late-night behavior, potential stuck values, or communication failures before log end."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Both the master bedroom occupancy sensor and temperature sensor are functioning normally. The occupancy sensor correctly reports unoccupied status during the night (C00) and occupied status during morning and evening activity periods (C05, C29). The temperature sensor reports stable, physically plausible values that drift naturally with ambient conditions and heating/cooling cycles, showing no signs of hardware failure or calibration errors.",
      "evidence": [
        "Temperature values in C00 range from 22.68 to 23.03 C,


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; observed behaviors are consistent with typical device operation and room usage patterns.

Explanation: Temperature sensor analysis: The `living_room_temp_sensor` reports continuous, realistic values (e.g., 22.69°C to 23.52°C in C00-C01) with natural thermal inertia. There are no stuck values, gaps, or impossible jumps, indicating the hardware is healthy.

Explanation: Occupancy sensor analysis: The `living_room_occupancy` sensor correctly reports 'unoccupied' (0) during periods when adjacent rooms (master bedroom, kids room) are also unoccupied (nighttime). During the morning transition (C05), while other rooms become occupied, the living room remains 'unoccupied'. This is logically consistent with residents moving directly from bedrooms to bathrooms without entering the living room, rather than a sensor failure.

Explanation: Functional verification: In chunk C27, the `living_room_occupancy` successfully transitions to 'occupied' (1) at 22:36, proving the sensor is capable of detecting presence and reporting state changes correctly.

Explanation: Conclusion: No direct fault evidence (stuck values, alarm events, or impossible transitions) exists. The distinction between 'no event' and 'device fault' confirms normal operation.

C00-C01: Temperature sensor shows valid fluctuations (22.69°C - 23.52°C) with no stuck values.

C05: Occupancy remains 0 despite activity in adjacent rooms, consistent with physical absence in the living room.

C27: Occupancy sensor successfully updates to 1, confirming functional capability.

C00 & C01: Living room occupancy remains 0 (unoccupied) while other rooms like master bedroom also remain unoccupied initially, consistent with night time.

C05: At 06:30 and 07:00, living room occupancy is explicitly reported as 0, while adjacent areas (kids room, master bath) show occupancy changes, indicating distinct sensor zones are working correctly.

C27: From 22:36 onwards, living room occupancy switches to 1 (occupied), correlating with high activity in other rooms (kids, dining, master bedroom), suggesting a family presence event.

Does not account for potential intermittent connectivity issues that might be masked by the sampling rate.

Assumes 'unoccupied' at 07:00 in C05 is correct despite general morning activity; however, this is plausible if the living room was not entered yet.

The living room occupancy sensor may have a delayed transition or slight desynchronization compared to other household activities, potentially indicating a configuration issue or sensitivity threshold anomaly rather than a hard fault. Specifically, during the morning wake-up sequence (C05), while kids and master bedrooms became occupied around 06:18-06:24, the living room remained 'unoccupied' until at least 07:00 (or possibly later, as it wasn't logged as occupied in the snippet). This contrasts with the evening pattern (C27) where it updated quickly.

C05: Kids room occupancy becomes 1 at 06:18/06:23. Master bedroom at 06:19/06:24. Living room temp sensor updates, but occupancy remains 0 at 06:30 and 07:00.

C27: In the evening, living room occupancy updates to 1 at 22:36, shortly after the day ends, showing it can detect presence.

It is highly plausible that the living room was genuinely unoccupied during the 06:18-07:00 window as residents went directly to bathrooms/bedrooms.

No explicit fault codes or stuck values observed.

Temperature sensor data shows no anomalies.

Potential intermittent communication timeout or packet loss for the living room occupancy sensor during the morning transition. While the temperature sensor reports consistently, the occupancy status stays static (0) despite significant ambient activity detected in nearby zones.

C05: Multiple occupancy events occur in neighboring zones (master_bathroom, kids_room, master_bedroom) between 06:18 and 06:30, but living_room_occupancy does not flip.

C00/C01: Regular reporting intervals for temperature are maintained, so total device failure is unlikely.

Lack of evidence for actual failure; absence of 'occupied' signal is consistent with the physical state of the room being empty.

Cannot distinguish between 'sensor not triggering' and 'no one present' without ground truth video or additional context.

C05: Living room occupancy remains 0 at 06:30 and 07:00 while adjacent rooms (kids, master bath) are actively occupied starting at 06:18. This suggests either the living room was truly empty or the sensor has a slow detection delay/high threshold.

C27: Living room occupancy successfully updates to 1 at 22:36, proving the device is capable of detecting presence and reporting changes.

C00-C01: Temperature sensor `living_room_temp_sensor` shows continuous, realistic variation (22.69 C to 23.52 C) with no stuck values or gaps, confirming basic hardware health.

Timestamps of when living room lights were turned on/off to corroborate occupancy claims.

Detailed timeline of resident movement paths from 06:00 to 07:00 to verify if anyone entered the living room.

Sensor detection range and sensitivity settings for the living room occupancy sensor.

The investigator correctly interpreted Matter-scaled temperature values (centi-degrees) and avoided false alarms based on magnitude alone.

The analysis of the occupancy sensor in C05 is logically sound: while adjacent rooms were occupied, the living room remained unoccupied. The investigator correctly identified this as likely due to physical absence rather than sensor failure, supported by the successful detection in C27.

Temperature data across C00-C01 shows normal thermal inertia and no stuck values.

Occupancy data in C27 confirms the device is functional and capable of detecting presence.

Conclusion that sensors are working normally is well-supported by the distinction between 'no event' and 'device fault'.

## 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",
    "C05",
    "C27"
  ],
  "suspected_patterns": [
    "sensor_stuck_value",
    "communication_timeout",
    "normal_operation_verification"
  ],
  "why_these_chunks": [
    "C00: Initial state check, baseline temperature and occupancy for living_room.",
    "C01: Continuity check of temperature trends in early morning.",
    "C05: Check behavior during transition to occupied state (morning wake-up).",
    "C27: Late night status to verify sensor functionality over the full 24h period."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The living room temperature and occupancy sensors are functioning normally. The temperature readings show natural thermal inertia and minor fluctuations consistent with an unoccupied residential environment during night and early morning hours. Occupancy transitions align with typical human activity patterns (e.g., waking up in the morning, returning home in the evening).",
      "evidence": [
        "C00: Temperature fluctuates between 22.69 C and 23.52 C over several hours, showing valid sensor updates without sticking.",
        "C00 & C01: Living room occupancy remai


状态 vs 实际位置不一致

Bedroom temperature and occupancy sensors are operating normally with no evidence of faults or anomalies.

Explanation: The bedroom temperature sensor (bedroom_temp_sensor) reports values ranging from 21.47°C to 22.17°C in the early morning (C00) and 21.47°C to 21.68°C in the evening (C13). These values show smooth, gradual changes consistent with indoor thermal dynamics and do not exhibit stuck values, impossible jumps, or drift errors.

Explanation: The bedroom occupancy sensor (bedroom_occupancy) demonstrates correct state transitions. It switches to 'occupied' (raw=1) at 22:24:21, shortly after the bedroom light is turned on (22:23:00), indicating responsiveness to user activity. It subsequently returns to 'unoccupied' (raw=0) at 22:42:03 after the light is turned off and remains unoccupied until the end of the log window.

Explanation: There is no evidence of device failure such as error codes, repeated non-recovery loops, or physically impossible states. The gaps in occupancy logging during unoccupied periods are consistent with standard protocol optimization where unchanged states may not be reported frequently.

Explanation: Both sensors function within expected parameters for a smart home environment, confirming they are working normally.

Temperature readings (e.g., 21.72 C to 22.17 C) show natural, gradual fluctuations without stuck values.

Occupancy sensor correctly transitions to 'occupied' (22:24:21) correlated with light activation (22:23:00) and back to 'unoccupied' (22:42:03).

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。

C00 provides baseline normal operation data for the temperature sensor in the early morning hours.

No stuck values or impossible raw integer codes observed for bedroom_temp_sensor.

bedroom_occupancy transitions from unoccupied (raw=0) to occupied (raw=1) at 22:24:21 and back to unoccupied at 22:42:03, correlating with the activation of bedroom_light at 22:23:00 and subsequent deactivation/absence behavior.

Subsequent transitions at 23:12:03 and 23:42:03 further confirm responsive detection logic.

The analysis relies on limited time windows (early morning and late evening); continuous health over longer periods cannot be fully verified from these chunks alone.

Potential false-positive occupancy detection or sensitivity issue in bedroom_occupancy sensor. The sensor detected 'occupied' status multiple times while no other devices (like lights) were explicitly triggered during some intervals, or the duration of occupancy was very short before becoming unoccupied again, potentially indicating ghost detections or high sensitivity.

At 22:24:21, bedroom_occupancy becomes occupied. Bedroom light turns on at 22:23:00. This is consistent.

However, looking at the transition at 22:42:03, the room becomes unoccupied. Then at 23:12:03, it becomes unoccupied again after being occupied? No, wait. Let's look closer at C13.

22:24:21 Occupied -> 22:42:03 Unoccupied. Duration ~18 mins.

22:45:00 - 23:10:00: No bedroom_occupancy logs. Living room is occupied. Bedroom temp continues to report.

23:12:03: bedroom_occupancy = unoccupied. Previous state was not logged as occupied recently in this chunk? Actually, between 22:42:03 and 23:12:03, there are NO bedroom_occupancy logs. If it was unoccupied at 22:42:03, why log 'unoccupied' again at 23:12:03 unless it changed state? Or is it a periodic report?

If it's a periodic report, 23:12:03 reporting unoccupied is normal if it stayed unoccupied.

Let's re-examine A1 validity. Is there an anomaly? The prompt asks for faults. N1 seems strong. Is there any evidence of failure? No explicit errors. No stuck values. No impossible transitions.

Maybe the 'anomaly' is that we don't see enough data to prove long-term health? But the query asks if they work *normally* based on history.

Let's look for subtle anomalies. In C13, living_room_occupancy flips frequently. Bedroom_occupancy has fewer events. This might just be lower activity in the bedroom.

Is there a device fault? No direct evidence.

Therefore, A1 should likely be 'none' or describe a lack of sufficient evidence for anomaly, but the instructions say 'construct competing... hypotheses'. If no anomaly is evident, the anomaly hypothesis should reflect the absence of evidence for fault rather than inventing one.

Alternative A1: Sensor communication latency or missed reports. Between 22:42:03 and 23:12:03, there is a gap. If someone entered the room during this time, we wouldn't know. However, without logs of entry, we can't confirm a miss.

I will construct A1 as a 'potential missed event due to sparse reporting' which is a common sensor limitation/anomaly pattern in smart homes.

Sparse logging does not equal malfunction; the sensor may simply not report unchanged states periodically, which is standard protocol optimization.

No cross-device correlation fails occurred (e.g., motion detected by camera but not occupancy sensor).

bedroom_temp_sensor raw values (e.g., 2172, 2169, 2177) correspond to logical Celsius temperatures (21.72 C, etc.) showing realistic drift.

bedroom_occupancy state transitions (0->1 at 22:24:21, 1->0 at 22:42:03) align temporally with user activity (light on at 22:23:00) and subsequent absence.

Absence of explicit error codes, stuck value loops, or impossible physical states (e.g., negative absolute temperature).

Logs covering the mid-day or peak activity hours to verify continuous operation.

Battery level or connectivity signal strength indicators for the sensors.

Correlation with visual data (cameras) or acoustic data to validate occupancy detection accuracy.

No stuck values or impossible raw integer codes observed for bedroom_temp_sensor.

Subsequent transitions at 23:12:03 and 23:42:03 further confirm responsive detection logic.

At 22:24:21, bedroom_occupancy becomes occupied. Bedroom light turns on at 22:23:00. This is consistent.

22:24:21 Occupied -> 22:42:03 Unoccupied. Duration ~18 mins.

If it's a periodic report, 23:12:03 reporting unoccupied is normal if it stayed unoccupied.

Is there a device fault? No direct evidence.

The investigator correctly interprets Matter-scaled temperature values (centi-degrees) and avoids false alarms based on raw integer magnitude.

The analysis correctly identifies that the bedroom sensors (temperature and occupancy) show no direct evidence of fault (no stuck values, impossible transitions, or error codes).

The 'anomaly hypothesis' regarding sparse logging is correctly identified as a protocol behavior/limitation rather than a device failure, which is consistent with standard smart home operation.

Cross-device correlation (light vs. occupancy) supports the health of the occupancy sensor during the active period in C13.

No further data collection is required to determine if the devices are working *normally* within the scope of the provided logs.

No stuck values or impossible raw integer codes observed for bedroom_temp_sensor.

Subsequent transitions at 23:12:03 and 23:42:03 further confirm responsive detection logic.

At 22:24:21, bedroom_occupancy becomes occupied. Bedroom light turns on at 22:23:00. This is consistent.

22:24:21 Occupied -> 22:42:03 Unoccupied. Duration ~18 mins.

If it's a periodic report, 23:12:03 reporting unoccupied is normal if it stayed unoccupied.

Is there a device fault? No direct evidence.

No stuck values or impossible raw integer codes observed for bedroom_temp_sensor.

Subsequent transitions at 23:12:03 and 23:42:03 further confirm responsive detection logic.

At 22:24:21, bedroom_occupancy becomes occupied. Bedroom light turns on at 22:23:00. This is consistent.

22:24:21 Occupied -> 22:42:03 Unoccupied. Duration ~18 mins.

If it's a periodic report, 23:12:03 reporting unoccupied is normal if it stayed unoccupied.

Is there a device fault? No direct evidence.

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

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

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

## Deterministic Signals
{
  "room_activity": {
    "living_room": 401,
    "bedroom": 357,
    "kitchen": 298,
    "bathroom": 59,
    "entrance": 14
  },
  "attribute_frequency": {
    "MeasuredValue": 866,
    "Occupancy": 222,
    "OnOff": 18,
    "StateValue": 5,
    "LockState": 4,
    "CurrentPositionLiftPercent100ths": 3,
    "OperationalStatus": 3,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 222,
  "temperature_patterns": [],
  "value_format_hints": {
    "temperature": "MeasuredValue is shown as Celsius with the raw integer preserved in parentheses.",
    "occupancy": "0=unoccupied, 1=occupied",
    "lock_state": "enumerated lock state rendered as label + raw code"
  }
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-19T00:00:00",
    "end_ts": "2026-06-19T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4

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

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

## Triage
{
  "primary_task_profile": "device-health",
  "secondary_task_profile": "none",
  "query_anchor": {
    "target_rooms": [
      "bedroom"
    ],
    "target_devices": [
      "bedroom_temp_sensor",
      "bedroom_occupancy"
    ],
    "target_question": "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_values",
    "missing_reports",
    "invalid_state_transitions"
  ],
  "why_these_chunks": [
    "C00 provides baseline normal operation data for the temperature sensor in the early morning hours.",
    "C13 contains the only occurrence of 'bedroom_occupancy' transitioning to 'occupied', allowing verification of its responsiveness and interaction with other devices (light) to confirm health."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Bedroom temperature sensor and occupancy sensor are functioning normally. The temperature readings show expected natural fluctuations within a comfortable range, and the occupancy sensor correctly transitions between occupied and unoccupied states in response to human presence.",
      "evidence": [
        "bedroom_temp_sensor reports valid temperature values ranging from 21.47 C to 22.17 C with smooth, gradual changes consistent with indoor thermal dynamics (C00, C13).",
        "No stuck values or impossible raw integer codes observed for bedroom_temp_sensor.",
        "bedroom_occupancy transitions from unoccupied (raw=0) to occupied (raw=1) at 22: