触发安全警报，临时禁用 Keypad 输入，通知住户

A stove was activated and remained on for over an hour while the house occupancy sensors reported zero occupants, creating a severe fire hazard in an empty home.

Explanation: Analysis of Chunk C05 reveals that at timestamp 2026-06-29T06:41:33, the 'kitchen_cook_surface' state changed to True (ON).

Explanation: At this exact moment and for the subsequent 31 minutes until 07:12:00, all occupancy sensors (living_room, master_bedroom, second_bedroom, bathroom, entrance) consistently reported a value of 0 (empty), indicating no one was present to monitor the cooking activity.

Explanation: The temperature data, despite exhibiting physically impossible absolute values (>2000°C) indicative of sensor scaling errors or drift, shows a clear, sustained linear increase starting immediately after the stove turned on. This relative trend confirms active heating energy input into the environment without human supervision.

Explanation: While the door lock sequence in C06 suggests potential tampering, the unattended cooking event poses an immediate and catastrophic property damage risk due to the likelihood of smoke, flame, or gas accumulation in an unmonitored kitchen.

Event log entry at 06:41:33: 'kitchen_cook_surface | OnOff.OnOff = True'.

Concurrent state logs from 06:41:33 to 07:12:00 showing 'living_room_occupancy', 'master_bedroom_occupancy', etc., all equal to 0.

Temperature readings rising steadily from ~2500 to ~2530 during the period the cook surface was ON while the house was reported empty.

Immediately send a voice alert to the homeowner's mobile device warning of unattended cooking and instructing them to return home or dispatch help.

Trigger emergency services (Fire Department) if video feeds show smoke, flames, or heat signatures consistent with combustion, given the high confidence in the unattended cooking scenario.

Dispatch a security responder or trusted neighbor to verify the status of the residence and extinguish any potential fire.

Unattended cooking event creating a severe fire hazard under the guise of system error.

At 06:41:33, the kitchen_cook_surface turns ON despite all occupancy sensors reporting '0' (empty house).

Temperature readings immediately begin a sustained, linear climb consistent with heating (e.g., rising from ~2500 to ~2560), confirming energy input into the environment.

The cook surface remains ON for over 30 minutes (until 07:12) without any subsequent occupancy detection, allowing unmonitored heating to continue.

Even if the absolute numbers are scaled errors, the relative increase indicates a real thermal event occurring in an empty house.

If the entire data stream is corrupted by a firmware bug, the correlation between the specific action (stove ON) and the subsequent data trend might be coincidental rather than causal.

The temporal alignment of the device state change (Stove ON) with the start of the thermal ramp-up is the strongest evidence against total system corruption; a glitch would likely affect all fields randomly, not specifically escalate during a power-on event.

Forced entry or unauthorized access attempt at the front door.

At 07:49:30, a manual lock operation occurs on the front door.

Immediately after (07:49:32), the door contact flips to False (open), suggesting the door was forced open or removed.

The door contact flips back to True (closed) within seconds (07:49:34), followed by an automatic re-locking event.

This rapid sequence (Open -> Close -> Lock) without a corresponding 'entering' occupancy event at the entrance sensor initially suggests mechanical manipulation (prying) rather than standard entry.

The sequence could represent a user fumbling with the lock and accidentally pulling the door slightly before it latched, though the speed makes this less likely.

The high ambient temperature in the home could be causing expansion of components leading to spurious contact signals.

The combination of Manual Unlock -> Contact Open -> Contact Close -> Auto Lock creates a 'tamper' signature distinct from normal use where Occupancy should transition 0->1 upon entry.

The exact timestamp correlation: KitchenCookSurface=ON at 06:41:33 coincides precisely with the start of the steep temperature rise, implying the stove is the heat source despite the empty house status.

The specific lock sequence at 07:49:30-07:49:35 showing Manual Op -> Door Open -> Door Closed -> Auto Lock, which deviates from standard user behavior patterns.

Video footage from the kitchen to visually confirm smoke, flames, or lack of activity.

Raw voltage/current draw data from the kitchen circuit breaker to verify the stove is actually consuming power.

History of the front door lock's previous states to determine if the 'manual unlock' was part of a normal routine or an anomaly.

Verify if the temperature spike started before or exactly when the stove turned ON (C02/C03 are critical to establish causality vs pre-existing error).

Check for any occupancy events in the kitchen or hallway during the unattended cooking window (C02-C04) to rule out a person being present but not detected.

Review door lock history immediately preceding C06 to confirm if the 'manual unlock' was an anomaly or part of a routine.

The current analysis focuses heavily on C00/C01 (sensor failure) and C05/C07 (fire risk), with some attention to C06 (door tampering). However, the causal link between the stove activation and the temperature rise is not fully established without seeing the data in the intervals immediately prior to 06:41 (C02, C03).

We must determine if the temperature was already spiking at 2200°C before the stove turned on. If yes, it's purely a sensor bug. If no, the stove triggered it.

Missing chunks C02, C03, and C04 are required to fill the gap between the initial anomalous readings and the specific stove activation event to validate the 'unattended cooking' hypothesis definitively.

## Query
以下是刚刚发生的事件。请判断这是否构成安全威胁，并说明理由。

## 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": 404,
    "master_bedroom": 520,
    "second_bedroom": 336,
    "kitchen": 303,
    "bathroom": 48,
    "entrance": 230
  },
  "attribute_frequency": {
    "MeasuredValue": 1156,
    "Occupancy": 645,
    "OnOff": 15,
    "StateValue": 7,
    "LockState": 5,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentPositionLiftPercent100ths": 1
  },
  "lock_state_flips": 5,
  "occupancy_events": 645,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-29T00:00:00",
    "end_ts": "2026-06-29T01:20:00",
    "event_count": 80,
    "rooms": {
      "living_room": 20,
      "master_bedroom": 19,
      "second_bedroom": 19,
      "kitchen": 16,
      "bathroom": 3,
      "entrance": 3
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "second_bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "master_bedroom_occupancy"
    ],
    "alarmish_count": 15,
    "preview_lines": [
      "[2026-06-29T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2261",
      "[2026-06-29T00:00:00] master_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2275",
      "[2026-06-29T00:00:00] second_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2271",
      "[2026-06-29T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2278",
      "[2026-06-29T00:00:00] living_room_oc

## Query
以下是刚刚发生的事件。请判断这是否构成安全威胁，并说明理由。

## Triage
{
  "latent_task_profile": "composite-safety",
  "focus_rooms": ["kitchen", "living_room", "entrance", "master_bedroom"],
  "focus_devices": ["kitchen_temp_sensor", "kitchen_cook_surface", "front_door_lock", "front_door_contact", "master_bedroom_occupancy", "living_room_temp_sensor"],
  "focus_chunk_ids": ["C00", "C01", "C05", "C06", "C07"],
  "suspected_patterns": [
    "abnormal_temperature_spike",
    "unattended_heating_device",
    "inconsistent_occupancy_and_entry_events"
  ],
  "why_these_chunks": [
    "C00/C01: Initial data shows nonsensical temperature readings (~2200-2400), indicating sensor calibration failure or device malfunction that invalidates subsequent thermal analysis.",
    "C05: Critical safety window where the kitchen cook surface turns ON while all occupancy sensors report '0' (empty house), creating a high fire risk scenario.",
    "C06: Contradictory events at the entrance show a manual unlock followed by an automatic re-lock and door contact flip, suggesting potential forced entry, tampering, or system glitch requiring immediate verification.",
    "C07: Temperature anomalies persist into this chunk with continued empty-house heating activity, escalating the fire hazard established in C05."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "System malfunction with sensor calibration drift and false occupancy triggers.",
      "evidence": ["All temperature sensors report values >2000°C (e.g., 2261, 2533) which is physically impossible for indoor environments, indicating a scale error or unit conversion failure.", "Occupancy sensors intermittently report '1' in the master bedroom while other rooms remain '0', possibly due to pet movement or sensor noise rather than human presence."],
      "weaknesses": ["The pattern of temperature readings increasing steadily over time (from ~2200 to ~2550) mimics heat accumulation from an active source, not random noise. The coincidence of the stove turning ON at 06:41 while temperatures were already anomalous suggests a causal link between the device state and the reading magnitude, even if the magnitude is wrong.", "A single person moving between rooms could explain intermittent occupancy changes, but cannot explain why *all* temperature sensors across the house simultaneously display impossibly high values that correlate with cooking activity."]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id":


CH-04-FP: 家长在玄关操作门锁

The front door lock and contact sensor reported a logical contradiction (door locked but contact open) immediately following a manual lock operation, indicating a potential mechanical failure or sensor malfunction rather than an intrusion.

Explanation: Step 1: Analyze the critical event in C08 at 18:07:00 where 'front_door_lock' executed a Manual LockOperation, setting the state to Locked (2).

Explanation: Step 2: Identify the immediate anomaly at 18:07:02 where 'front_door_contact' reported StateValue = False (Open), creating a physical impossibility if the lock successfully engaged the latch, suggesting either the lock failed to close the bolt, the contact sensor is faulty, or someone tampered with the door mechanism.

Explanation: Step 3: Evaluate subsequent logs (C09-C14) which show consistent occupancy patterns, authorized cooking activities, and eventual proper auto-locking of the door at 22:38:28 without further conflicts, ruling out sustained forced entry and pointing towards an isolated hardware or sensor error.

Explanation: Step 4: Conclude that while the temperature drop was noted, it correlates with window opening events (C12) and lacks evidence of HVAC failure, leaving the security sensor conflict as the primary high-confidence anomaly.

At 18:07:00-18:07:02: LockState=Locked followed immediately by DoorContact=False (Open).

At 22:38:28: Door automatically locked and Contact reported True (Closed) correctly, confirming the system can function properly under normal conditions.

Inspect the front door lock mechanism for jamming, broken bolts, or misalignment.

Test and replace the front door magnetic contact sensor if the lock mechanism appears physically sound.

Does not fully explain the specific security sensor conflict where the door lock engaged but the contact sensor immediately reported 'open' (False).

Forced Entry or Tampering Attempt: An intruder may have entered via the front door, manually unlocked/relocked it to bypass a latch or create noise, triggering a false negative on the magnetic contact sensor, followed by movement into the house.

In C08, 'front_door_lock' shows Manual LockOperation at 18:07:00, followed immediately by 'front_door_contact' StateValue = False (Open) at 18:07:02 despite the lock engaging,

Occupancy sensors in 'entrance', 'kitchen', and 'kids_room' activate within seconds of this door anomaly sequence,

The temperature drop (from ~25°C to ~23°C) correlates with windows being opened (C12: kids_room_window lift=100%) which could be part of an environmental manipulation or simply coincidental ventilation after entry.

The sequence occurs during evening hours when residents are typically home; the subsequent activity (cooking, AC usage) looks consistent with normal household behavior rather than intrusion.

No additional forced entry indicators like glass breakage or alarm triggers are explicitly logged beyond the sensor conflict.

HVAC/System Failure leading to Abnormal Cooling: The significant temperature drop across all zones without a corresponding increase in fan/AC cycle events suggests a system failure rather than user action, potentially compromising comfort or safety if extreme.

Temperature drops from ~25°C (C00) to ~23°C (C08) and remains low in C12/C16,

Despite 'living_room_ac' turning ON in C08, temperatures continue to trend downward or stabilize at lower levels rather than rising as expected for cooling,

Correlation with window opening (C12) exacerbates heat loss.

A 2-degree drop is within normal diurnal variance for summer evenings in many climates; it does not necessarily indicate a critical failure.

The AC activation suggests the system is responding to conditions, albeit perhaps insufficiently.

The timestamped sequence in C08: LockOperation (Manual) at 18:07:00 followed by DoorContact=False at 18:07:02 indicates a logical contradiction between mechanical locking and physical closure status.

Discrepancy between total activity volume: High occupancy in primary living/sleep areas vs very low occupancy in secondary bedrooms ('second_bedroom': 24), suggesting either a shift in family routine or unauthorized access to restricted zones.

Window covering adjustment (100% open) in C12 occurring simultaneously with sustained high occupancy and temperature fluctuations.

Historical data regarding typical daily occupancy patterns for this specific household to distinguish between 'late night sleepover' and 'unusual absence from bedrooms'.

Detailed logs of the HVAC controller's output (fan speed, compressor status) to verify if the AC unit was actually running effectively or just cycling off.

Video footage or motion detection specifics to determine if the person triggering the door lock/contact sequence was inside or outside the perimeter at that exact moment.

Verify HVAC controller output logs (fan/compressor status) in C09 and C10 to confirm AC failure vs. window-related cooling.

Cross-reference occupancy sensor data with specific motion detection timestamps in C14 to determine if the late-night activity aligns with authorized resident movement or intrusion patterns.

The investigation correctly identified the door lock/contact conflict (C08) as a critical anomaly but relied heavily on C12 and C16 for context regarding temperature and occupancy trends.

Evidence is insufficient because it lacks granular temporal data from the middle of the night (C09-C11, C13-C15) to establish a continuous timeline between the door anomaly and the subsequent high occupancy.

Specifically, C09-C11 are needed to see if the 'unauthorized access' hypothesis holds up immediately after the door event, while C14 is crucial to verify the nature of the late-night movement before concluding it was a planned sleepover.

The temperature analysis also needs detailed HVAC logs (likely in C09/C10 based on sequential logging) to rule out system failure definitively.

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": 233,
    "master_bedroom": 292,
    "kids_room": 293,
    "kitchen": 153,
    "dining_room": 147,
    "entrance": 159,
    "second_bedroom": 24,
    "master_bathroom": 24
  },
  "attribute_frequency": {
    "Occupancy": 712,
    "MeasuredValue": 582,
    "OnOff": 13,
    "StateValue": 6,
    "LockState": 3,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1
  },
  "lock_state_flips": 3,
  "occupancy_events": 712,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-18T11:55:00",
    "end_ts": "2026-06-18T12:40:00",
    "event_count": 80,
    "rooms": {
      "living_room": 12,
      "master_bedroom": 19,
      "kids_room": 18,
      "kitchen": 9,
      "dining_room": 9,
      "entrance": 9,
      "second_bedroom": 2,
      "master_bathroom": 2
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "kids_room_temp_sensor",
      "kitchen_temp_sensor",
      "master_bedroom_occupancy",
      "kids_room_occupancy"
    ]

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Triage
{
  "latent_task_profile": "behavior-sequence | composite-safety",
  "focus_rooms": ["entrance", "kitchen", "living_room", "kids_room", "master_bedroom"],
  "focus_devices": ["front_door_lock", "front_door_contact", "occupancy_sensor (multiple rooms)", "temperature_sensor (multiple rooms)"],
  "focus_chunk_ids": ["C08", "C12", "C00", "C16"],
  "suspected_patterns": [
    "Occupancy mismatch: High occupancy events in 'kids_room' and 'living_room' persist late into the night (C12, C15), while other bedrooms show low activity, suggesting potential unauthorized access or a family member staying up unusually late.",
    "Door state anomaly: A manual lock operation occurred in C08 followed immediately by the door contact sensor reporting open (False), indicating the door was unlocked/reopened after being secured.",
    "Temperature drift: Room temperatures dropped significantly from ~25°C to ~22°C over the period, potentially indicating HVAC failure or windows left open during high occupancy."
  ],
  "why_these_chunks": [
    "C08 contains the critical security event sequence of locking/unlocking the front door with conflicting sensor states.",
    "C12 shows the latest timestamped occupancy data revealing active presence in the children's room and living area late at night, which deviates from typical sleep patterns for these zones.",
    "C00 establishes the baseline temperature readings (~25°C) to calculate the magnitude of the subsequent cooling trend observed across chunks.",
    "C16 shows a reduction in total event count and activity levels near the end of the log, helping to define the transition from the anomalous active period to a quieter state."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Extended Family Gathering or Sleepover: The high occupancy in the kids' room and living area late at night, combined with active kitchen use (cooking surface on) and window opening, suggests a planned family event where children are staying up late or sleeping in common areas.",
      "evidence": ["High occupancy counts in 'kids_room' (293) and 'living_room' (233) vs low in guest rooms ('second_bedroom': 24),", "Activity continues late into the log (C12, C16 timestamps show presence past 21:00 and 23:00),", "User-initiated device state changes (turning on AC, lights, cooking surface) indicate authorized human control."],
      "weaknesses": ["Does not fu


INS-05-FP: 住户换了新手机/手表解锁

虽然用了不常用的RFID方式解锁，但进门后最终开灯、正常活动，行为模式与住户一致

No safety threats detected; the initial sensor data gap was a transient error that resolved shortly after normal household activity resumed.

Explanation: Step 1: Analyzed the temporal sequence in C01 showing cooktop off (18:50), window open (18:50:30), and kitchen temp sensor reporting 'None' (18:51).

Explanation: Step 2: Verified subsequent chunks (C01-C03) which show the kitchen_temp_sensor resuming valid temperature readings at 18:55:00 and remaining stable through the end of the log.

Explanation: Step 3: Confirmed that no other safety indicators (e.g., prolonged high heat, unauthorized access, or persistent sensor failure) were present, attributing the brief outage to a transient glitch during routine ventilation.

Kitchen temperature sensor recovered valid readings ('2295') at 18:55:00, just 4 minutes after the data loss event.

All other room sensors operated normally throughout the entire 6-hour period, ruling out systemic network failures.

The dishwasher activated at 19:36:00, confirming continued normal kitchen usage without incident.

Monitor the kitchen window contact for consistent closing events during future cooking activities.

Check physical connections of the kitchen temperature sensor if intermittent gaps persist in future logs.

Potential safety hazard involving an unmonitored fire risk; the cooktop was turned off while the window remained open (or failed to close), and the primary temperature sensor lost connectivity exactly when it should have been monitoring for post-cooling hazards.

At 18:50:30, kitchen window contact reported OPEN (False).

At 18:51:00, kitchen temp sensor reported 'None' (data loss) despite other room sensors reporting normally, creating a blind spot for ~4.5 hours until the log ends or resets.

No confirmation of window closing or cooktop re-activation in the log between 18:51:00 and 19:36:00.

The temperature value before failure (2301) is within normal ambient range, reducing immediate overheating probability.

The dishwasher activation at 19:36 suggests someone entered the kitchen, which might have triggered the sensor recovery or manually verified conditions.

Hardware malfunction or tampering where the kitchen temperature sensor has physically disconnected or been bypassed, rendering the system blind to thermal anomalies in the kitchen specifically.

Sudden transition from 'MeasuredValue = 2301' to 'MeasuredValue = None' at 18:51:00.

Consistent absence of any new temperature measurements from the kitchen_temp_sensor for the remainder of the chunk (over 45 minutes of logs).

Contrast with living_room, master_bedroom, and second_bedroom sensors continuing to report values every 5 minutes without interruption.

Could be a legitimate temporary network dropout that resolved just outside the observed window or upon human interaction at 19:36.

The precise temporal sequence: Cook Off (18:50:00) -> Window Open (18:50:30) -> Temp Sensor Data Loss (18:51:00). This clustering of events points to a specific incident at the moment of cooking cessation.

The isolation of the anomaly: Only the kitchen_temp_sensor fails; all other sensors in the house operate normally, ruling out a general router outage or power cut.

Did the window sensor ever report a return to 'True' (closed) state?

Was there any manual override or status update regarding the kitchen temperature sensor between 18:51:00 and 19:36:00?

What is the historical reliability of the kitchen_temp_sensor compared to others?

Verify if the kitchen window sensor returned to 'True' (closed) state after the initial 'False' report.

Check for any manual status updates or reconnection events for the kitchen_temp_sensor between 18:51 and 19:36.

Confirm the operational history of the kitchen_temp_sensor to distinguish between a one-time glitch and chronic failure.

The analysis correctly identified the temporal clustering of events in C01 but stopped too early. To validate the 'Fire_Safety_Monitoring_Gap' hypothesis, we need to confirm the duration of the data loss by reviewing subsequent chunks (C02, C03) to see when/if the sensor recovered or if it remained offline until the dishwasher event at 19:36. The current evidence leaves ambiguity on whether the sensor was permanently disconnected during this period or if the log simply cut off before recovery.

The hypothesis regarding the window sensor closing is unverified; checking later logs is essential to rule out an open window scenario where ventilation was left active, which contradicts the safety assessment.

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": 147,
    "master_bedroom": 148,
    "second_bedroom": 85,
    "kitchen": 81,
    "entrance": 75,
    "bathroom": 12
  },
  "attribute_frequency": {
    "MeasuredValue": 294,
    "Occupancy": 241,
    "OnOff": 7,
    "StateValue": 2,
    "OperationalState": 1,
    "CurrentPositionLiftPercent100ths": 1,
    "LockState": 1
  },
  "lock_state_flips": 1,
  "occupancy_events": 241,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-10T17:55:00",
    "end_ts": "2026-06-10T18:45:00",
    "event_count": 80,
    "rooms": {
      "living_room": 21,
      "master_bedroom": 21,
      "second_bedroom": 13,
      "kitchen": 13,
      "entrance": 10,
      "bathroom": 2
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "second_bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "entrance_occupancy"
    ],
    "alarmish_count": 34,
    "preview_lines": [
      "[2026-06-10T17:55:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2328",
      "[2026-06-10T17:55:00] master_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2319",
      "[2026-06-10T17:55:00] second_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2335",
      "[2026-06-10T17:55:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2338",
      "[2026-06-10T17:56:03] living_room_occupancy | OccupancySe

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Triage
{
  "latent_task_profile": "device-health | single-event-safety",
  "focus_rooms": ["kitchen"],
  "focus_devices": ["kitchen_cook_surface", "kitchen_window_contact", "kitchen_temp_sensor"],
  "focus_chunk_ids": ["C01"],
  "suspected_patterns": [
    "Cooking surface turned off while temperature data is still being reported, suggesting potential end-of-use state.",
    "Kitchen window sensor reported 'False' (open) at the same timestamp the cooktop was turned off, which may indicate an open window during cooking or a sensor fault.",
    "Temperature sensor in kitchen reported 'None' immediately after reporting a value, indicating a potential hardware disconnection or communication failure."
  ],
  "why_these_chunks": [
    "Chunk C01 contains the specific deterministic signals regarding the cooktop state change ('OnOff.OnOff = False'), the window contact state ('BooleanState.StateValue = False'), and the critical telemetry loss ('MeasuredValue = None'). These events form a unique anomaly cluster not present in other chunks where sensors continue to report valid values consistently."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Routine household activity where cooking was completed, sensors briefly glitched due to power fluctuation or network packet loss, and the kitchen window was opened for ventilation before being closed.",
      "evidence": [
        "Cook surface turned off at 18:50:00 indicating end of use.",
        "Window sensor reported 'False' (open) shortly after cooking stopped, consistent with ventilation habits.",
        "Temperature sensor recovered valid readings ('2295') 3 minutes later, suggesting a transient error rather than permanent failure.",
        "Dishwasher activated at 19:36:00, indicating continued normal kitchen usage."
      ],
      "weaknesses": [
        "The gap between the open window state and subsequent data is short, but no explicit 'closed' event is logged immediately after.",
        "Other rooms show perfectly stable temperature telemetry during the same period, making an isolated kitchen sensor fault less likely than a systemic issue affecting only the kitchen zone."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Potential safety hazard involving an unmonitored fire risk; the cooktop was turned off while the window remained open (or failed to close), and the p


只在儿童房一个房间内发生，kids_room_occupancy=1，有小孩在场

Evidence indicates a potential unauthorized entry via the front door while the resident remained asleep in the master bedroom.

Explanation: The log shows a critical event at 16:07:00 where a manual lock operation (Type=1) was followed immediately by the door state changing to '2' and contact becoming 'False', indicating the door was opened or breached from the outside.

Explanation: During this specific breach window (16:07), the master bedroom occupancy sensor continued to read '1', suggesting the primary resident was unaware of the intrusion and likely sleeping.

Explanation: While temperature readings show values in the thousands (e.g., 2533), these are consistent across all rooms and appear to be raw integer encoding errors (likely millikelvin or scaled units) rather than physical sensor failure, as the trend shows a steady decline correlating with time, which is physically impossible for standard sensors to fluctuate wildly without an environmental cause like infiltration.

Explanation: The high-frequency entrance occupancy events (~every 4 minutes) combined with zero activity in adjacent living areas create a disjointed pattern inconsistent with normal household circulation, further supporting the theory of an intruder lingering near the threshold.

Timestamp 16:07:00 sequence: LockOperation(LockOperationType=1) -> DoorLock.LockState = 2 -> front_door_contact = False.

Master bedroom occupancy remained 1 throughout the 16:07 incident, implying the resident was incapacitated or asleep.

Consistent pattern of entrance occupancy triggers every ~4 minutes despite no corresponding activity in common areas.

Immediately dispatch security personnel or law enforcement to verify occupancy and secure the premises.

Check physical locks and door mechanisms for signs of forced entry or tampering.

Review camera footage from the entrance/hallway if available to identify any persons present during the lock flip event.

Temperature values fluctuate irregularly between sensors (e.g., Kitchen jumps from 2387 to 2403).

Occupancy sensors toggle between 0 and 1 rapidly in some logs, which may indicate calibration issues rather than human movement.

Drift typically causes gradual deviation, not synchronized drops across multiple rooms unless there is a shared environmental factor like a power surge affecting sensors.

The correlation between lock flips and specific time points suggests intentional action rather than random noise.

Undetected intruder entering via the front door while the primary resident sleeps in the master bedroom.

At 16:07, a manual lock operation occurred immediately followed by a contact state change (False), indicating the door was opened from the outside or manipulated.

Despite the door being open/manipulated, the master bedroom occupancy remained 1 (resident unaware).

High-frequency 'entrance_occupancy = 1' events occur regularly even when the door lock state is stable, suggesting an entity moving in/out or lingering near the threshold without triggering other alarms.

Temperature patterns show a consistent drop across all rooms, possibly due to external air infiltration through an unlocked/forced entry point.

The system did not trigger an alarm on the single lock flip, potentially due to a delayed response or configuration setting.

It is possible the resident left the house briefly and returned, though the sustained master bedroom occupancy makes this less likely.

Sensor failure leading to 'ghost occupancy' where the entrance sensor falsely reports presence despite no human traffic matching the volume expected for that frequency.

Entrance occupancy triggers every ~4 minutes regardless of visible context.

Other rooms (Living, Second Bed, Bath) remain empty (0), creating a disjointed spatial picture.

Temperature anomalies (fluctuating values) suggest sensor instability.

This hypothesis does not explain the specific timestamp of the manual lock operation at 16:07, which implies a physical interaction beyond simple sensor error.

The total event counts in structured signals (424 occupancy vs 13 OnOff) suggest real activity, not just static drift.

The sequence at 16:07 showing 'LockOperation(LockOperationType=1)' followed immediately by 'DoorLock.LockState = 2' and 'front_door_contact = False' indicates an unauthorized opening event.

The simultaneous maintenance of 'master_bedroom_occupancy = 1' during the door breach event suggests the resident was asleep or incapacitated and unaware of the intrusion.

The high frequency of entrance occupancy events (every ~4 mins) combined with zero activity in adjacent living spaces creates a pattern inconsistent with normal household circulation.

Camera footage or video feed from the entrance/hallway to confirm if anyone was present at the door during the lock flip.

Log data for the HVAC system to verify if the temperature drop was caused by system failure or external infiltration.

History of previous lock state changes to determine if the 16:07 event was anomalous compared to baseline user behavior.

Signal strength or battery level logs for the entrance sensor to rule out communication errors causing false positives.

Verify the 'Temperature anomalies (fluctuating values)' mentioned in Hypothesis N2. The Triage output references temperature data to support sensor drift hypotheses, but the Investigator Output cites specific impossible values (e.g., 2387) that contradict standard Celsius readings unless it's a raw integer signal error. We need to confirm if this is a data parsing error or actual sensor failure.

Cross-reference the 'manual lock operation' at 16:07 with the 'History of previous lock state changes'. The Investigator notes this as a key discriminative point but lacks historical context to determine if this was an anomaly or routine behavior. This requires access to logs outside the current 12-hour window or a longer baseline within the available chunks if they contain prior history.

The analysis is highly on-topic and logically structured, effectively identifying two competing hypotheses (Intrusion vs. Sensor Failure).

However, the evidence regarding temperature is suspiciously specific yet potentially flawed ('2387 degrees'). If the provided chunks contain raw data showing these numbers, we must verify if this indicates a system parsing bug rather than physical reality before concluding 'sensor instability'.

The conclusion about the 'unauthorized opening' relies heavily on the interpretation of the 16:07 event without historical baseline data included in the current chunk set. While the logic holds for the immediate timeframe, claiming 'anomaly' strictly requires knowing what normal looks like for *this* user over time.

No additional chunks are needed from the existing list (C00-C12) as they cover the full 12-hour sequence required to establish the trend and the specific incident. The insufficiency lies in the *type* of external checks needed (historical baselines, HVAC logs), not missing time windows.

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": 240,
    "master_bedroom": 292,
    "second_bedroom": 169,
    "kitchen": 153,
    "entrance": 154,
    "bathroom": 24
  },
  "attribute_frequency": {
    "MeasuredValue": 582,
    "Occupancy": 424,
    "OnOff": 13,
    "StateValue": 4,
    "LockState": 3,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentPositionLiftPercent100ths": 1
  },
  "lock_state_flips": 3,
  "occupancy_events": 424,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-01T11:55:00",
    "end_ts": "2026-06-01T12:55:00",
    "event_count": 80,
    "rooms": {
      "living_room": 15,
      "master_bedroom": 25,
      "second_bedroom": 14,
      "kitchen": 12,
      "entrance": 12,
      "bathroom": 2
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "second_bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "master_bedroom_occupancy",
      "entrance_occupancy"
    ],
    "alarmish_count": 30,
    "preview_lines": [
      "[2026-06-01T11:55:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2533",
      "[2026-06-01T11:55:00] master_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2521",
      "[2026-06-01T11:55:00] second_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2539",
      "[2026-06-01T11:55:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2528",
      "[2026-06-01T11:56:00] master

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Triage
{
  "latent_task_profile": "behavior-sequence | composite-safety",
  "focus_rooms": ["master_bedroom", "entrance", "living_room", "second_bedroom"],
  "focus_devices": ["master_bedroom_occupancy", "entrance_occupancy", "living_room_occupancy", "front_door_lock", "temperature_sensor"],
  "focus_chunk_ids": ["C00", "C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08", "C09", "C10", "C11", "C12"],
  "suspected_patterns": [
    "High-frequency occupancy toggling in bedrooms vs consistent low activity elsewhere",
    "Correlation between door lock state flips and high alarm counts",
    "Gradual temperature drop across all rooms without HVAC intervention signals",
    "Potential 'ghost occupancy' or sensor drift given constant occupancy=1 readings despite low event counts in other rooms"
  ],
  "why_these_chunks": [
    "All chunks (C00-C12) are required to establish the full temporal sequence of the suspected anomaly, specifically tracking the consistency of occupancy sensors over time.",
    "Chunks C00-C06 show the initial baseline and the onset of irregular temperature drops and occupancy patterns.",
    "Chunk C11 is critical for cross-referencing the final door lock state flip against the preceding occupancy data to determine if it was a user action or an anomaly trigger.",
    "The uniform presence of temperature sensors in every chunk allows for a system-wide thermal trend analysis."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Resident is home, sleeping in the master bedroom, with intermittent trips to the entrance (likely checking mail or retrieving items) and no one in common areas.",
      "evidence": ["Master bedroom occupancy consistently reads 1 throughout all chunks.", "Entrance occupancy reads 1 at regular intervals (~4 minutes).", "Living room, second bedroom, bathroom occupancy are consistently 0.", "Temperature readings show natural diurnal cooling trends without HVAC intervention signals."],
      "weaknesses": ["The frequency of entrance 'occupancy' events is high for a sleeping resident but could be explained by routine checks.", "No activity in living room or kitchen contradicts typical evening routines, but this can happen during sleep hours."]
    },
    {
      "id": "N2",
      "description": "System sensor drift causing false positive temperature fluctuations and erratic occupancy toggling.",
      "evidence": ["Tempera


开窗前住户先在同一房间活动并开灯，是主动行为

The kitchen temperature sensor exhibits a pattern of data loss ('None') that correlates precisely with the activation and deactivation of high-heat cooking appliances, indicating a hardware failure or calibration drift caused by thermal stress.

Explanation: Temporal Correlation: The sensor reported 'None' exactly at 18:47:00, which is 3 minutes after the cook surface was activated at 18:46:30, and again at 19:10:00 shortly before it was turned off, suggesting the anomaly is triggered by the appliance's operation.

Explanation: Isolation of Failure: While the kitchen sensor failed, all other room sensors (living_room, master_bedroom, kids_room) continued to report valid temperatures during these exact timestamps, ruling out a global network outage, power failure, or malicious signal injection affecting the entire system.

Explanation: Recurring Pattern: The issue persisted across two distinct operational cycles (start-up and shut-down phases), confirming a consistent causal link between the heat generation from the cook surface and the sensor's inability to transmit data, rather than a random glitch.

[2026-06-22T18:46:30] kitchen_cook_surface | OnOff.OnOff = True followed immediately by [2026-06-22T18:47:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = None

[2026-06-22T19:09:00] kitchen_cook_surface | OnOff.OnOff = False followed by [2026-06-22T19:10:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = None while other sensors remained stable

Immediately inspect the physical placement of the kitchen temperature sensor relative to the cook surface to ensure it is not exposed to direct radiant heat.

Replace or recalibrate the kitchen temperature sensor as its reliability is compromised during active cooking scenarios, posing a safety risk for fire detection or ventilation control.

Could be a software bug in the sensor driver triggered by specific voltage spikes from the induction/stove unit, though this presents the same risk as hardware failure.

Coordinated malicious interference or environmental sabotage targeting the kitchen monitoring system.

Precise Timing: The anomalies occur at distinct moments when the environment changes state (cooking starts/stops, window opens), suggesting an external trigger linked to these events.

Selective Targeting: Other critical sensors in adjacent living areas remain unaffected throughout the same time windows.

Lacks evidence of signal injection or physical tampering logs; the pattern is more consistent with a device reacting to its environment (heat/electrical noise) than being hacked remotely.

The recovery of data suggests the system isn't permanently disabled, making a sophisticated remote hack less likely than a physical component issue.

The exact temporal alignment of 'MeasuredValue = None' with the activation of the 'kitchen_cook_surface' (18:46:30 -> 18:47:00) strongly implies a causal link between heat generation/sensor exposure and data loss.

The persistence of valid temperature readings in all other rooms (living_room, master_bedroom, kids_room) during the exact same timestamps rules out a global network outage or power failure.

The recurrence of the anomaly upon re-engagement of the cooking environment (19:09:00 -> 19:10:00) confirms the issue is tied to the operational state of the kitchen appliance, not a one-time glitch.

Raw telemetry or firmware logs from the kitchen_temp_sensor to check for error codes (e.g., over-range, connection timeout, EMI detection) around the failure times.

Power consumption logs for the kitchen circuit to rule out brownouts causing the sensor reset.

Physical inspection notes regarding sensor placement relative to the cook surface and potential direct heat exposure.

Verify if the anomaly persists during other high-heat events not involving cooking (e.g., boiling water in a pot without activating the cook surface).

Check for simultaneous voltage drops or brownouts on the kitchen circuit breaker logs to rule out power-induced resets.

Review firmware error codes from the sensor specifically for 'over-range' or 'EMI' triggers rather than generic communication loss.

The current analysis correctly identifies the temporal correlation between cooking events and data loss but relies heavily on Chunk C09. To confirm this is a hardware failure specific to heat stress versus a network glitch triggered by appliance startup, adjacent chunks showing the state immediately before activation (C08) and immediately after deactivation/recovery (C10) are required.

The hypothesis of 'malicious interference' (A2) is weakly supported; more context on network traffic spikes during these exact windows (found in surrounding chunks) would strengthen or refute this before concluding it's purely environmental.

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": 236,
    "master_bedroom": 291,
    "kids_room": 293,
    "kitchen": 153,
    "entrance": 155,
    "dining_room": 146,
    "second_bedroom": 24,
    "master_bathroom": 24
  },
  "attribute_frequency": {
    "Occupancy": 713,
    "MeasuredValue": 582,
    "OnOff": 13,
    "StateValue": 4,
    "LockState": 3,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1
  },
  "lock_state_flips": 3,
  "occupancy_events": 713,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-22T11:55:00",
    "end_ts": "2026-06-22T12:40:00",
    "event_count": 80,
    "rooms": {
      "living_room": 12,
      "master_bedroom": 19,
      "kids_room": 18,
      "kitchen": 9,
      "entrance": 9,
      "dining_room": 9,
      "second_bedroom": 2,
      "master_bathroom": 2
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "kids_room_temp_sensor",
      "kitchen_temp_sensor",
      "kids_room_occupancy",
      "entrance_occupancy"
    ],

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Triage
{
  "latent_task_profile": "device-health | single-event-safety",
  "focus_rooms": ["kitchen"],
  "focus_devices": ["kitchen_temp_sensor", "kitchen_cook_surface", "kitchen_light"],
  "focus_chunk_ids": ["C09"],
  "suspected_patterns": ["sensor_data_loss", "sequence_anomaly"],
  "why_these_chunks": ["Chunk C09 contains a critical data anomaly where the kitchen temperature sensor reports 'None' immediately after the cook surface and light are activated, suggesting a potential sensor failure or calibration drift during active cooking, which poses a safety risk."]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Intermittent sensor communication glitch or temporary data packet loss during high network load.",
      "evidence": ["The kitchen_temp_sensor reports 'None' at specific timestamps (18:47:00, 19:10:00) while other room sensors report valid temperatures consistently.", "Data recovery occurs shortly after the gaps (e.g., at 18:50:00 and 19:10:00 later in the log), suggesting a transient error rather than permanent failure."],
      "weaknesses": ["This hypothesis fails to account for the correlation between the data loss and active cooking events (cook surface/light activation). Normal glitches are typically random and not synchronized with user actions."],
      "threat_type": null
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Kitchen temperature sensor hardware failure or calibration drift induced by heat stress from active cooking appliances.",
      "threat_type": "sensor_data_loss | safety_risk",
      "evidence": ["Temporal Correlation: The first data loss event ('None') occurs exactly 3 minutes after the cook surface is activated (18:46:30 -> 18:47:00).", "Recurring Pattern: A second 'None' reading appears shortly before the cook surface is turned off (19:09:00 -> 19:10:00), coinciding with the window contact opening.", "Contextual Relevance: Unlike other rooms which maintain continuous readings, the kitchen sensor specifically fails during periods of elevated thermal load generated by cooking equipment.", "Duration: The sensor remains non-responsive for short intervals (approx. 2-3 mins) despite continuous polling attempts implied by subsequent valid reads."],
      "weaknesses": ["Could be a software bug in the sensor driver triggered by specific voltage spikes from the induction/stove unit, though this pr


活动从卧室开始（住户起床），路径合理（卧室→客厅→厨房→卧室），最终回到卧室

Multiple temperature sensors are reporting physically impossible values (>2500) consistent with raw ADC counts or unit conversion errors rather than actual thermal readings, indicating a firmware or configuration failure.

Explanation: The temperature readings across all zones (Kitchen, Living Room, Bedrooms) start at ~2276 and linearly increase to >2500 over 7 hours. If these were Celsius, the house would be melting; if millidegrees, they represent ~2.5°C which is too low for a heated home but the magnitude of change (linear drift without HVAC correlation) suggests a deterministic data error rather than environmental heating.

Explanation: The perfect synchronization of this anomaly across disparate rooms with different physical characteristics rules out local heat sources and points to a system-wide software issue or shared bus error.

Explanation: At C06/C07, the kitchen sensor drops to 'None' while others remain high, suggesting intermittent hardware communication loss or power cycling in addition to the persistent data scaling bug.

Temperature values exceed realistic limits (e.g., 2532) while occupancy logs show normal human activity patterns starting around 06:13.

Values exhibit a non-stochastic, linear progression typical of raw counter overflow or incorrect multiplier application in code, not natural thermal variance.

Simultaneous appearance of 'None' data for one sensor while others continue reporting inflated values indicates partial hardware failure alongside the logical error.

Immediately reset the affected temperature sensors via the device manager or gateway interface to clear stuck states.

Check the device firmware version and update if an older build known to have ADC conversion bugs is installed.

Verify the schema definition for 'MeasuredValue' on the server side to ensure no recent configuration changes altered the unit scaling factor.

A residential environment would not reach nearly 2500 m°C (2.5°C) overnight without significant HVAC activity, yet no 'SystemMode' changes or high occupancy events correlate with this rise.

The absolute baseline (~2276) is physically inconsistent with typical indoor ambient temperatures if the unit were standard Celsius; however, assuming m°C, the magnitude of change is suspiciously linear and synchronized across disparate zones without differential thermal gradients expected in real homes.

Sensor Calibration/Unit Conversion Failure: Temperature sensors are outputting raw ADC counts or millidegrees instead of normalized degrees Celsius, indicating a firmware bug or configuration drift.

Values exceed realistic physical limits for standard reporting (e.g., 2491 vs expected ~20-30). If interpreted as °C, the house is melting; if m°C, the resolution implies raw data leakage.

Perfect correlation between unrelated rooms (Kitchen, Living Room, Bedrooms) suggests a shared system-level error rather than independent environmental phenomena.

No corresponding logical state changes (lights off, windows closed) to justify such uniform thermal spikes.

Cannot rule out a hypothetical scenario where the entire building was subjected to extreme industrial heat sources, though the log context (residential) makes this highly improbable.

Data Pipeline Malfunction: The ingestion system is misinterpreting timestamped telemetry packets, merging multiple readings into single entries or applying an incorrect scaling factor during serialization.

The 'MeasuredValue' attribute frequency is 1156, matching the number of temperature logs, suggesting the anomaly is inherent to the specific stream being analyzed.

The data remains stable and 'clean' until C06/C07 where it drops to 'None', implying the sensor itself may be functional but the reporting logic failed earlier.

Does not explain why the 'Occupancy' stream reports valid binary data while the 'Temperature' stream reports invalid scalar data simultaneously.

The simultaneous presence of extremely high temperature values (>2400) and zero occupancy in living areas (00:00 - 06:12) creates a contextual conflict; humans do not generate enough heat to raise a whole house to 2500+ units without leaving traces on occupancy counters or triggering alarms.

The linear progression of values (e.g., Kitchen: 2272 -> 2482) lacks the stochastic noise expected in real-world thermal sensing, pointing to a deterministic software error.

The definition of the 'MeasuredValue' unit in the schema documentation.

Historical data from previous 24-hour cycles to establish if this is a recurring calibration event or a one-time glitch.

Logs from the gateway/server side to determine if the values were generated by the sensor or injected/malformed during transmission.

Verify unit definition schema for 'MeasuredValue' across all devices.

Check historical data from previous cycles to distinguish recurring calibration drift from one-time glitch.

Cross-reference gateway/server logs to determine if values originate at the sensor or are malformed during ingestion.

The analysis correctly identifies the anomaly (likely raw ADC counts misinterpreted as Celsius) and focuses on the relevant time window (early morning baseline).

However, evidence is currently insufficient because it relies heavily on chunks C00-C07. To confirm this is a persistent hardware/firmware issue rather than a transient network event, we need to inspect later chunks (e.g., C08, C12, C20) to see if the anomalous pattern stabilizes or resolves after potential reboots or maintenance windows.

The current hypothesis about 'Data Pipeline Malfunction' requires validation against server-side logs which cannot be inferred from the provided client-side log chunks alone; these external checks must be explicitly initiated.

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": 518,
    "kids_room": 521,
    "kitchen": 303,
    "dining_room": 221,
    "second_bedroom": 48,
    "master_bathroom": 57,
    "entrance": 228,
    "second_bathroom": 1
  },
  "attribute_frequency": {
    "MeasuredValue": 1156,
    "Occupancy": 1092,
    "OnOff": 26,
    "StateValue": 7,
    "LockState": 5,
    "CurrentPositionLiftPercent100ths": 3,
    "SystemMode": 1,
    "OperationalState": 1
  },
  "lock_state_flips": 5,
  "occupancy_events": 1092,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-25T00:00:00",
    "end_ts": "2026-06-25T01:10:00",
    "event_count": 80,
    "rooms": {
      "living_room": 18,
      "master_bedroom": 18,
      "kids_room": 18,
      "kitchen": 14,
      "dining_room": 3,
      "second_bedroom": 3,
      "master_bathroom": 3,
      "entrance": 3
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "kids_room_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Triage
{
  "latent_task_profile": "device-health | composite-safety",
  "focus_rooms": [
    "kitchen",
    "living_room"
  ],
  "focus_devices": [
    "kitchen_temp_sensor",
    "living_room_temp_sensor",
    "master_bedroom_temp_sensor",
    "kids_room_temp_sensor",
    "kitchen_cook_surface",
    "kitchen_window_contact"
  ],
  "focus_chunk_ids": [
    "C00",
    "C01",
    "C02",
    "C03",
    "C04",
    "C05",
    "C06",
    "C07"
  ],
  "suspected_patterns": [
    "Thermal drift anomaly: Temperature sensors report values >2000 (likely millidegrees or raw ADC) instead of standard Celsius, indicating a calibration failure or sensor malfunction starting early morning.",
    "Data integrity loss: Sudden appearance of 'None' in temperature readings at C06 and C07 suggests sensor disconnection or power loss.",
    "Contextual conflict: Despite high occupancy counts in living/kitchen areas, temperature data remains erratic rather than stabilizing around ambient levels.",
    "State transition without actuation: Kitchen cook surface turns off while window contact opens during periods of high thermal reporting."
  ],
  "why_these_chunks": [
    "C00-C05 establish the baseline pattern of inflated temperature readings (>2200 to >2500) persisting throughout the night and morning.",
    "C06 is critical as it shows the first instance of 'None' data for kitchen_temp_sensor, marking the potential onset of hardware failure or communication breakdown.",
    "C07 contains key contextual events (cook_surface Off, window_contact False/Open) occurring simultaneously with continued anomalous temperature reporting, suggesting the system is reacting to faulty sensor inputs rather than actual environmental changes.",
    "Subsequent chunks (C08+) show stabilization of the *pattern* (consistent high numbers) but likely not resolution of the underlying sensor fault, confirming a persistent device health issue."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The temperature sensors are calibrated in millidegrees Celsius (m°C), and the house is experiencing a gradual, uniform heating trend due to external weather or internal load.",
      "evidence": ["All sensors report values consistently increasing from ~2276 to ~2491 over time.", "Values across all rooms remain statistically similar within a small variance (<5 units)."],
      "weaknesses": ["A residential environment wou


CH-02-FP: 家长陪同孩子在厨房

家长也在活动（living_room occupancy），不是孩子独自操作灶具

Systematic sensor data corruption causing phantom occupancy events and erratic temperature readings indicates a firmware loop or calibration failure rather than physical intrusion.

Explanation: Occupancy sensors report '1' (occupied) with unnaturally high frequency (every 10-20 mins) across multiple rooms without corresponding door entry/exit events or light changes, suggesting a software reporting loop.

Explanation: Temperature sensors exhibit non-linear fluctuations and sudden spikes (e.g., Master Bedroom jumping from 2258 to 2269 at 21:30 while the general trend was cooling), which contradicts standard thermal physics and indicates sensor noise or drift.

Explanation: Critical actuator events like the Kitchen Window opening to 100% at 21:03 occurred while the room remained flagged as occupied, and subsequent lighting controls did not correlate with this state change, further pointing to disjointed sensor logic.

Timestamp 21:03:02 shows 'kids_room_window' lifting to 100% while 'kids_room_occupancy' remains 1, indicating a lack of environmental correlation typical of manual human activity.

Master bedroom temperature fluctuates erratically between 2254°C and 2269°C over short intervals, breaking the consistent cooling curve established by the HVAC activation.

Front door contact state changed to True (closed) and lock auto-locked at 22:36, yet entrance occupancy continued to log positive events in C06, creating a logical contradiction regarding occupant presence.

Reboot the smart home hub and all affected sensors to clear potential firmware loops.

Replace the battery-powered occupancy sensors in the living room and kids' room if rebooting fails to stabilize the signal.

Calibrate temperature sensors or replace them if erratic variance persists after system reset.

Appliance usage events (Cook Surface ON at 18:25, Dishwasher ON at 19:40) align with typical meal preparation and cleanup times.

The frequency of occupancy toggling is extremely high for a single human resident; sensors report '1' every 10-20 minutes in the same room without clear movement context.

Temperature readings exhibit non-linear fluctuations that do not match standard thermal mass cooling curves (e.g., Kitchen jumps from 2267 to 2268 between 20:40 and 20:45).

Sensor calibration drift or firmware loop causing phantom occupancy detection.

Room Activity counts are disproportionately high for Living Room (151) and Master Bedroom (148) compared to physical likelihood of continuous re-detection within 6 hours.

Timestamps show repetitive 'Occupancy = 1' events with short intervals (e.g., C00: 18:00 -> 18:02 -> 18:07 -> 18:09) suggesting rapid cycling rather than distinct human entry/exit.

Temperature data shows erratic variance (e.g., Master Bedroom drops 2334->2321->2330->2318) inconsistent with ambient cooling, indicating potential sensor noise or reporting artifacts.

No explicit alarm triggers were generated despite the high event count, suggesting the system logic may be filtering these as noise rather than security breaches.

Covert surveillance or automated probing via window/lift devices.

Discrete interaction with specific devices outside of normal occupancy logic: Kids Room Window Covering set to 100% (fully open) at 21:03:02 while occupancy is still detected.

Front door lock manual operation at 18:09 followed immediately by door contact state change, but no corresponding long-term absence in entrance occupancy logs.

Sudden loss of temperature data points (Kitchen: None at 18:26 and 18:56) coinciding with appliance activity, potentially indicating sensor tampering or power interruption.

The timing of the window opening (21:03) occurs late in the log period when house appears settled, and the window covers returning to closed position would require another event not seen in this chunk set.

The statistical improbability of 148+ occupancy events in the Kids Room within 6 hours for a single dwelling indicates either sensor malfunction or an unlogged automated agent triggering the sensor repeatedly.

The specific sequence of 'Kids Room Window' lifting to 100% at 21:03:02 combined with persistent occupancy signals suggests a deliberate environmental change not explained by standard HVAC behavior.

Irregular temperature spikes (e.g., Living Room rising from 2253 to 2266 between 20:35 and 21:00) contradict the established cooling trend initiated at 18:09, pointing to external heat sources or sensor errors.

Physical verification of whether occupants moved between rooms during the reported 'toggles' to rule out legitimate multi-room navigation.

Historical baseline of occupancy event rates for this specific hardware model to determine if the current frequency is abnormal.

Log entries for the specific timestamp ranges where temperature data returned 'None' to assess if it was a network dropout or sensor failure.

Status of the 'kids_room_light' which turned OFF at 21:03:02, same minute the window opened, to see if lighting control correlates with the window actuation.

Verify specific temperature timestamps in C03 and C05 to confirm the erratic fluctuations mentioned in A1 (Master Bedroom spikes) and the cooling trend continuity.

Cross-reference occupancy event IDs in C03 and C05 with the 'Kids Room Window' actuation time (21:03:02) to determine if the sensor toggling persists before or after the window state change.

Check network connectivity status in missing chunks to rule out packet loss as the cause for 'None' temperature readings noted by the Investigator.

The current evidence is insufficient because the Investigator's hypothesis A1 relies on specific timestamped anomalies (temperature spikes and erratic variance) that must be verified against the raw log data in the identified chunks.

Chunk C03 is critical to verify the transition point of the 'cooling trend' and the initial frequency of occupancy toggles described in Triage.

Chunk C05 is required to isolate whether the 'window opening at 21:03' correlates with a spike in occupancy events or if it causes a drop in them; this distinction determines if the anomaly is behavioral or mechanical.

The lack of explicit alarm triggers makes the high event count ambiguous; without granular data from these specific chunks, we cannot confirm if the system is logging noise or missing actual intrusion patterns.

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": 151,
    "master_bedroom": 148,
    "kids_room": 148,
    "kitchen": 82,
    "dining_room": 74,
    "entrance": 79,
    "second_bedroom": 12,
    "master_bathroom": 12
  },
  "attribute_frequency": {
    "Occupancy": 388,
    "MeasuredValue": 294,
    "OnOff": 13,
    "StateValue": 3,
    "LockState": 2,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1
  },
  "lock_state_flips": 2,
  "occupancy_events": 388,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-03T17:55:00",
    "end_ts": "2026-06-03T18:30:00",
    "event_count": 80,
    "rooms": {
      "living_room": 17,
      "master_bedroom": 16,
      "kids_room": 15,
      "kitchen": 11,
      "dining_room": 7,
      "entrance": 11,
      "second_bedroom": 2,
      "master_bathroom": 1
    },
    "top_devices": [
      "kitchen_temp_sensor",
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "kids_room_temp_sensor",
      "master_bedroom_occupancy",
      "entrance_occupancy"
    ],

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Triage
{
  "latent_task_profile": "behavior-sequence",
  "focus_rooms": [
    "kids_room",
    "living_room",
    "master_bedroom",
    "entrance"
  ],
  "focus_devices": [
    "kids_room_occupancy",
    "living_room_occupancy",
    "master_bedroom_occupancy",
    "entrance_occupancy",
    "kitchen_temp_sensor",
    "living_room_temp_sensor",
    "master_bedroom_temp_sensor",
    "kids_room_temp_sensor"
  ],
  "focus_chunk_ids": [
    "C00",
    "C01",
    "C02",
    "C03",
    "C04",
    "C05",
    "C06",
    "C07",
    "C08"
  ],
  "suspected_patterns": [
    "High-frequency occupancy toggling in living areas (kids_room, living_room) suggests sensor sensitivity issues or rapid movement patterns.",
    "Systematic temperature drift across all rooms (e.g., Kitchen: 2332 -> 2260; Living Room: 2323 -> 2251) indicates a potential HVAC malfunction or calibration error rather than environmental changes.",
    "Disproportionate event count for occupancy sensors vs. other device types (LockState=2, OnOff=13) implies the primary system activity is motion detection logic.",
    "Consistent 'alarmish_count' (~40-46 per chunk) despite lack of explicit alarm triggers suggests a high baseline noise floor or misconfigured thresholds."
  ],
  "why_these_chunks": [
    "All chunks are required to trace the continuous timeline of the suspected temperature drift and occupancy frequency anomalies.",
    "Chunks C00-C03 establish the initial high temperature baseline and frequent room entry events.",
    "Chunks C04-C08 confirm the progressive cooling trend and sustained high occupancy event rates, distinguishing between a one-time glitch and a systemic behavioral pattern.",
    "The uniform distribution of events across chunks rules out localized hardware failure and points to a global configuration or environmental issue affecting the entire house."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "High-traffic household evening routine with active HVAC cooling cycle.",
      "evidence": [
        "Occupancy logs show consistent presence in living areas (Living Room: 151, Kids Room: 148) vs. low activity in secondary rooms (Second Bedroom: 12).",
        "HVAC activation at 18:09 (SystemMode=3) followed by gradual temperature drop across all sensors from ~23°C to ~22.5°C over 6 hours.",
        "Appliance usage events (Cook Surface ON at 18:25, Dishwasher ON at 19:40) align w


The observed high-frequency sensor polling is consistent with standard passive monitoring and routine household activity patterns, showing no evidence of intrusion or malfunction.

Explanation: Analysis of chunks C01-C04 confirms that temperature and occupancy reports occur at regular intervals (e.g., :00, :03) which aligns with the Investigator's hypothesis N2 (Sensor polling loop artifact) rather than an external script (A1).

Explanation: The occupancy data shows logical transitions corresponding to real-world events: Bedroom occupancy drops at 20:09, 20:39, and 21:39, followed by lights turning off and locks engaging around 22:00-22:05, indicating a normal sleep sequence where occupants leave the bedroom to settle in or use other facilities before locking up.

Explanation: Environmental sensors report gradual thermal drift (e.g., Living Room cooling from ~22.8°C to ~22.4°C) without any actuator intervention (HVAC off), which is expected behavior for evening hours when heating/cooling systems are typically set back, ruling out sensor malfunction or unexplained anomalies.

Consistent correlation between 'Occupancy=0' events in the bedroom and subsequent door lock operations at 22:00:55.

Gradual, physically plausible temperature decreases across all rooms over the 6-hour period without sudden spikes or flatlining indicative of failure.

Continue routine monitoring; no immediate intervention required.

Archive logs as part of standard historical baseline maintenance.

```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Standard evening household routine with passive monitoring and sleep transition.",
      "evidence": [
        "High frequency 'Occupancy=1' events in living_room correlate with active kitchen usage (dishwasher On/Off, cooking surface changes) between 19:45-20:00.",
        "Bedroom occupancy transitions to '0' at 20:09, 20:39, 21:39, suggesting occupants leaving the room for bathroom or other activities before settling down.",
        "Systematic temperature drift (Living ~22°C -> 21.5°C; Bedroom ~21.7°C -> 21.4°C) indicates cooling systems are off or running at low power as expected during evening hours."
    ],
    {
      "id": "N2",
      "description": "Sensor polling loop artifact where high frequency readings mimic human activity.",
      "evidence": [
        "Temperature sensors report values every 5 minutes consistently regardless of actual thermal changes.",
        "Occupancy sensor reports '1' repeatedly even when no state-change event (like door contact) occurs, which is typical behavior for a sensor confirming continuous presence rather than detecting new entries."
    ]
  },
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Covert surveillance or unauthorized data exfiltration via automated sensor polling.",
      "threat_type": "Surveillance / Espionage",
      "evidence": [
        "The frequency of 'MeasuredValue' reports (221 total) vastly exceeds normal HVAC control cycles, creating a detailed log of minute-by-minute environmental conditions.",
        "In C01-C03, Temperature and Occupancy sensors fire almost simultaneously (within seconds) at regular intervals (e.g., :00, :03, :20, :30), creating a rigid, machine-generated heartbeat pattern inconsistent with organic human movement.",
        "The specific granularity (e.g., distinguishing bedroom occupancy drop at 20:09 vs 20:39) suggests an intent to track precise behavioral schedules rather than just system status."
    ],
    {
      "id": "A2",
      "description": "Device malfunction or misconfiguration causing false positive occupancy alerts.",
      "threat_type": "Operational Anomaly",
      "evidence": [
        "Bedroom occupancy toggles between 0 and 1 frequently (e.g., 20:09->0, 20:39->0, 21:39->0) while temperature sensors remain stable, suggesting the occupant may have left but the sensor logic is stuck or being polled aggressively.",
        "At 22:00:30, bedroom occupancy becomes 1 immediately after lights turn on, but prior logs show it was 0 until then, indicating a potential glitch in the wake-up logic or a phantom trigger."
    }
  ],
  "most_discriminative_evidence": [
    "The rigid temporal alignment of temperature and occupancy reports (occurring at fixed 5-minute or sub-minute intervals across all chunks) suggests an automated script or compromised device rather than natural user interaction.",
    "The discrepancy between the high volume of 'MeasuredValue' signals (221) and the low count of actual state-changing events like 'LockState' (2) or 'OnOff' (12) implies the system is actively interrogating the environment passively."
  ],
  "missing_information": [
    "Network traffic logs to determine if these signals originate from a local gateway or an external IP address.",
    "Historical baseline data to compare current polling frequency against standard operating procedures.",
    "Physical verification of sensor placement to rule out line-of-sight interference causing erratic occupancy toggles."
  ]
}
```

Verify network traffic logs to rule out external IP intrusion.

Compare current polling frequency against historical baseline data.

The analysis is on-topic and correctly identifies the discrepancy between high-frequency sensor polling and low state-change events.

Evidence is insufficient because the investigation relies entirely on chunks C01-C03. The available log covers 6 hours but includes chunks C00, C01, C02, C03, and C04.

Without reviewing C00 (likely early morning/late night) and potentially C04 (if it exists in the full dataset or if C04 is needed to verify the pattern's continuity), we cannot confirm if this behavior is a constant anomaly or an isolated incident.

The Investigator correctly noted missing information regarding network logs and baselines; these must be requested before finalizing the threat assessment.

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": 145,
    "bedroom": 98,
    "kitchen": 81,
    "bathroom": 12,
    "entrance": 8
  },
  "attribute_frequency": {
    "MeasuredValue": 221,
    "Occupancy": 100,
    "OnOff": 12,
    "StateValue": 3,
    "LockState": 2,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentPositionLiftPercent100ths": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 2,
  "occupancy_events": 100,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-24T17:55:00",
    "end_ts": "2026-06-24T19:20:00",
    "event_count": 80,
    "rooms": {
      "living_room": 33,
      "bedroom": 20,
      "kitchen": 20,
      "bathroom": 3,
      "entrance": 4
    },
    "top_devices": [
      "living_room_temp_sensor",
      "kitchen_temp_sensor",
      "bedroom_temp_sensor",
      "living_room_occupancy",
      "bedroom_occupancy",
      "bathroom_occupancy"
    ],
    "alarmish_count": 19,
    "preview_lines": [
      "[2026-06-24T17:55:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2334",
      "[2026-06-24T17:55:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2223",
      "[2026-06-24T17:55:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2335",
      "[2026-06-24T18:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2335",
      "[2026-06-24T18:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2215",
      "[2026-06-24T18:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2390",
      "[2026-06-24T18:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-24T18:05:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2331",

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Triage
{
  "latent_task_profile": "behavior-sequence",
  "focus_rooms": ["living_room", "bedroom"],
  "focus_devices": ["living_room_occupancy", "bedroom_occupancy", "living_room_temp_sensor", "bedroom_temp_sensor"],
  "focus_chunk_ids": ["C01", "C02", "C03"],
  "suspected_patterns": [
    "sustained occupancy without corresponding temperature regulation adjustments",
    "disproportionate sensor polling frequency in occupied zones compared to activity logs"
  ],
  "why_these_chunks": [
    "C01-C03 show continuous 'Occupancy=1' events in living/bedrooms while temperature readings remain static or drift slightly, suggesting passive monitoring rather than active thermal interaction.",
    "The high frequency of occupancy toggles in these specific chunks indicates a sequence of presence detection that warrants verification against actual human activity (e.g., sleeping vs. awake) to rule out sensor malfunction or environmental anomalies."
  ]
}

## Investigator
```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Standard evening household routine with passive monitoring and sleep transition.",
      "evidence": [
        "High frequency 'Occupancy=1' events in living_room correlate with active kitchen usage (dishwasher On/Off, cooking surface changes) between 19:45-20:00.",
        "Bedroom occupancy transitions to '0' at 20:09, 20:39, 21:39, suggesting occupants leaving the room for bathroom or other activities before settling down.",
        "Systematic temperature drift (Living ~22°C -> 21.5°C; Bedroom ~21.7°C -> 21.4°C) indicates cooling systems are off or running at low power as expected during evening hours."
    ],
    {
      "id": "N2",
      "description": "Sensor polling loop artifact where high frequency readings mimic human activity.",
      "evidence": [
        "Temperature sensors report values every 5 minutes consistently regardless of actual thermal changes.",
        "Occupancy sensor reports '1' repeatedly even when no state-change event (like door contact) occurs, which is typical behavior for a sensor confirming continuous presence rather than detecting new entries."
    ]
  },
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Covert surveillance or unauthorized data exfiltration via automated sensor polling.",
      "threat_type": "Surveillance / Espionage",
      "evidence": [
        "The frequency of 'MeasuredValue' reports (221 t


1分钟内多次灯光开关 可能原因：智能家居系统被入侵/远程控制；设备自动化规则冲突；或电路问题 也可能是恐慌信号（住户通过反复开关灯试图引起注意）

通知住户检查是否有人误操作或系统异常

Systemic sensor data integrity failure due to unit conversion error (missing decimal point), causing physically impossible temperature readings and triggering false positive alarm states.

Explanation: Analysis of all provided chunks (C00-C04) reveals that temperature values from all sensors consistently display integers in the range of 2100-2399 (e.g., 2324, 2227).

Explanation: These values are mathematically equivalent to realistic room temperatures (~21.2°C - 23.9°C) only when divided by 100, indicating a systematic formatting or transmission error where the decimal point is omitted or the value is scaled by 100 instead of 1.

Explanation: The Triage output explicitly notes 'Temperature values are scaled by a factor of 10' (implied as 100 based on data magnitude) and 'consistent alarmish counts', confirming that the monitoring system interprets these raw integer values as critical overheating events (>2300°C), leading to false safety alerts.

Explanation: While an automated lock operation occurred at 23:11, the log indicates `OperationSource=Auto` coinciding with lights turning off and occupants remaining inside, suggesting this was likely a routine automation rather than a security breach, making the sensor data error the primary anomaly.

Log entries show `living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2324` at 17:55:00, which is physically impossible for Celsius without scaling correction.

The pattern persists across all rooms (Living Room, Bedroom, Kitchen) and throughout the entire 6-hour window, ruling out individual sensor hardware failure.

Triage output confirms 'consistent alarmish counts' despite no visible fire events, directly correlating the anomaly to the malformed temperature data.

Immediately disable automatic alarm triggers based on temperature readings until the data feed is corrected.

Contact the IoT vendor or facility IT to inspect the sensor driver code or MQTT message configuration to locate the missing decimal point or incorrect unit multiplier.

Verify historical logs to determine if this calibration error is new or has been present since installation.

Standard Residential Evening Routine

Cannot explain why the single lock flip is flagged as significant by the triage logic.

Sensor Calibration Failure leading to False Positive Alarms

Triage output explicitly notes 'Temperature values are scaled by a factor of 10'.

Raw values like 2324°C are physically impossible, indicating a unit conversion bug (Celsius instead of milli-Celsius).

The triage system mentions 'consistent alarmish counts' despite no visible alarm triggers, implying the backend interpreted these massive numbers as critical overheating events.

No actual fire or life-safety event occurred; the threat is purely to the reliability of the monitoring system.

Covert Access Attempt Detected via Lock State Flip

Structured signals report exactly 1 'lock_state_flips'.

Chunk C04 shows `front_door_lock | Event: LockOperation(LockOperationType=0...)` followed immediately by `DoorLock.LockState = 1`. In many systems, Type 0 can be 'Unlock' or 'Flip', and State 1 often means 'Locked' or 'Unlocked' depending on convention. If the resident was already inside (high occupancy until 23:11), a sudden lock operation could indicate a tamper attempt or unauthorized re-locking/unlocking sequence.

The timing (23:11) coincides with lights going off and the user potentially leaving, making a forced lock/unlock sequence suspicious.

The log entry includes `OperationSource=Auto`, which suggests it might be a scheduled routine or automated response to the door opening, reducing the likelihood of manual intrusion.

Without knowing the definition of LockState 0 vs 1, we cannot confirm if this represents a breach or a normal cycle.

The universal presence of temperature values in the range of 2100-2399 across all sensors, which mathematically correspond to ~21-24°C when divided by 100, strongly points to a systemic data formatting error rather than a physical phenomenon.

The correlation between the 'Alarmish' status in the Triage Output and the temperature data confirms that the anomaly is being actively detected by the security logic, likely due to out-of-bounds numerical values triggering threshold breaches.

Definition of LockState values (0 and 1) and LockOperationType 0 to determine if the lock flip represents a breach or a routine action.

The source code or configuration of the temperature sensor driver to verify the missing decimal point or unit conversion logic.

Historical logs to see if this scaling error started recently or has been present since installation.

Standard Residential Evening Routine

Verify the specific definition of LockState (0 vs 1) and LockOperationType to confirm if the single flip at 23:11 is anomalous or routine.

Cross-reference historical logs to determine if the temperature scaling error (values * 100) is a new occurrence or long-standing baseline behavior.

The analysis correctly identifies the temperature data anomaly (scaling error) as the primary issue. However, evidence is insufficient regarding the 'Lock State Flip' mentioned in Hypothesis A2. The Investigator notes ambiguity about whether this was an automated action or a breach; without checking Chunk C05 (which may contain the immediate aftermath or context around 23:11-23:15) or system configuration docs, this cannot be ruled in/out.

The Triage output mentions chunks C00-C04, but only 5 chunks were provided in the Available Chunk IDs list. Since the query covers a 6-hour window and we have inspected up to C04, checking C05 is necessary to ensure no anomalies occurred after the last inspected chunk before the end of the window.

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": 147,
    "bedroom": 147,
    "kitchen": 78,
    "bathroom": 12,
    "entrance": 3
  },
  "attribute_frequency": {
    "MeasuredValue": 220,
    "Occupancy": 156,
    "OnOff": 5,
    "StateValue": 2,
    "OperationalState": 1,
    "CurrentPositionLiftPercent100ths": 1,
    "LockState": 1
  },
  "lock_state_flips": 1,
  "occupancy_events": 156,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-20T17:55:00",
    "end_ts": "2026-06-20T19:05:00",
    "event_count": 80,
    "rooms": {
      "living_room": 29,
      "bedroom": 29,
      "kitchen": 19,
      "bathroom": 3
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "bedroom_occupancy",
      "living_room_occupancy",
      "bathroom_occupancy"
    ],
    "alarmish_count": 31,
    "preview_lines": [
      "[2026-06-20T17:55:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2324",
      "[2026-06-20T17:55:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2227",
      "[2026-06-20T17:55:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2319",
      "[2026-06-20T17:58:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
      "[2026-06-20T17:59:00] living_room_occupancy | OccupancySensing.Occupancy = 1",
      "[2026-06-20T18:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2327",
      "[2026-06-20T18:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2227",
      "[2026-06-20T18:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2323",
      "[2026-06-20T18:02:57] bathroom_occupancy | OccupancySensing.Occupancy = 0",

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Triage
{
  "latent_task_profile": "single-event-safety | composite-safety",
  "focus_rooms": ["living_room", "bedroom"],
  "focus_devices": ["living_room_temp_sensor", "bedroom_temp_sensor"],
  "focus_chunk_ids": ["C00", "C01", "C02", "C03", "C04"],
  "suspected_patterns": [
    "Temperature values are scaled by a factor of 10 (e.g., 23.24°C reported as 2324), indicating a potential sensor calibration or data transmission issue.",
    "High frequency of temperature readings from living room and bedroom sensors relative to occupancy events suggests either continuous logging errors or anomalous sensor behavior.",
    "Consistent 'alarmish' counts across chunks despite no explicit alarm triggers in previews, suggesting the system is flagging the data anomalies mentioned above."
  ],
  "why_these_chunks": "All chunks (C00-C04) must be inspected because the temperature anomaly appears consistently across the entire 6-hour window. The pattern of high-frequency temperature logs with suspiciously precise decimal-like integer values (ending in even numbers) persists regardless of time of day or room activity, indicating a systemic sensor/logic error rather than a transient event."
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Systematic Data Scaling/Transmission Error",
      "evidence": ["All temperature values are integers ending in even digits (e.g., 2324, 2227) consistent with a Celsius * 100 scaling factor.", "Values show gradual cooling trends over time (23xx -> 22xx -> 21xx), which is physically plausible for an unoccupied or cooling home at night.", "The pattern persists across all rooms and sensors simultaneously, suggesting a central data processing pipeline issue rather than individual sensor failure."],
      "weaknesses": ["Does not explain the specific anomaly of 'Alarm' flags if the system expects raw decimals.", "Does not account for the sudden lock state flip without explicit user interaction logs."]
    },
    {
      "id": "N2",
      "description": "Standard Residential Evening Routine",
      "evidence": ["High occupancy counts (156 events) indicate residents were present.", "Kitchen appliances (cooker off, dishwasher on) suggest meal preparation/cleanup.", "Lights turned off and front door locked at 23:11 indicates residents leaving the house."],
      "weaknesses": ["Fails to address the illogical format of the temperature readings.", "Cannot e