Smart Mine Safety Early Warning Solution

Based on Singdata Lakehouse, this solution unifies data from the six major subsystems of coal and non-coal mines, enabling cross-system correlation analysis and AI-driven proactive alerting — upgrading from "reactive alarming" to "predictive early warning," with PoC deployment completed in 6 weeks.

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1. Business Background

China produces approximately 4.7 billion tons of raw coal annually, accounting for nearly 50% of global output. Mining is a high-risk industry — between 2001 and 2022, coal mine water hazard accidents caused 4,667 cumulative deaths, and a single major gas explosion can result in direct losses exceeding 50 million RMB — with 200 to 500 workers operating underground each shift, making early warning failures a matter of mass casualties.

The fundamental contradiction in current mine digitalization: sensors generate massive amounts of data every second, but the six major subsystems operate independently, data silos are severe, and cross-system correlation analysis is virtually nonexistent.

Mandatory Policy Requirements (Mandatory from 2025)

Requirement	Regulatory Basis
CH₄ overrun: automatic power cutoff within 1 second, non-compliance results in shutdown	Coal Mine Safety Regulations, 2022 Edition
Safety monitoring data real-time reporting to provincial/national platforms	AQ 1029-2019
Sensor historical data retained ≥ 1 year	GB 16423-2020
Personnel underground location real-time queryable per shift	Smart Mine Construction Guidelines 2025

The global connected mine market is projected to reach $48.7 billion by 2033 (CAGR 13.4%). Among approximately 4,000 large-scale coal mines in China, only 30% have completed initial intelligentization — the data fusion layer is the largest gap.

Six Major Subsystems

Subsystem	Core Metrics	Collection Frequency	Safety Weight
Ventilation System	CH₄, CO, Wind Speed, Temperature	1–10s	★★★★★
Monitoring System	Personnel Location, Violation Behavior	1s/person	★★★★★
Transport System	Belt Deviation, Rope Tension, Vehicle Position	1–5s	★★★★☆
Power System	Three-Phase Voltage/Current, Power Factor	50ms	★★★★☆
Excavation System	Cutting Current, Support Pressure	100ms–1s	★★★☆☆
Dispatch System	Production Plans, Safety Events, Work Orders	Business-driven	★★★☆☆

Ventilation + Monitoring carry the highest weights: gas outburst × personnel presence = mass casualties. Data from these two systems is completely isolated in traditional architectures — this is the core entry point of this solution.

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2. Industry Pain Points

Quantified Accident Data

• From 2001 to 2022, China recorded 1,103 coal mine water hazard accidents with 4,667 deaths
• Roof collapse accidents account for approximately 30%–40% of total coal mine accidents, with underground mines being the hardest hit
• Gas accidents remain the greatest threat to coal mines, with frequent over-limit gas concentration incidents in outburst mines

Three Major Deficiencies of Traditional Solutions

① Data Silos — No Linkage

The six major subsystems are independently built by different vendors, with Modbus/OPC-UA/MQTT/proprietary protocols coexisting. Typical failure scenarios:

• ZONE-101 CH₄ concentration exceeds 1.0% alarm threshold → but power feeder switch does not automatically trip
• Personnel positioning shows 3 people in hazardous zone → but the ventilation system is unaware, and dispatch receives no notification

② Delayed Warning — Only Reactive

Existing systems only perform threshold alarming for the five major hazards (gas/fire/water/roof/dust) and have no trend prediction capability. CH₄ rising from 0.7% to 1.0% takes an average of 20–30 minutes — sufficient time to warn and evacuate — but traditional systems provide no proactive alerts.

③ Manual Dependency — Hard to Scale

Dispatch command relies on human experience; checking 2,000+ monitoring points per shift is impractical. Inspection personnel cannot cover the entire mine, and hazard identification is delayed.

Data Platform Pain Points

Problem	Manifestation
Protocol Heterogeneity	Multi-vendor systems with non-unified protocols, high integration cost
Insufficient Real-time Capability	Safety warnings require second-level response; traditional batch architectures cannot meet this
Difficult AI Deployment	Model training and inference are separated, iteration cycles are long, no unified Feature Store
Lack of Standardization	Each system has a different data model; cross-system analysis relies on manual effort

This core gap is precisely where Singdata Lakehouse excels — unified ingestion, real-time aggregation, SQL-embedded AI — one platform connecting six isolated islands.

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3. Solution

Overall Architecture

Data Model

Raw Layer (8 Tables)

-- Ventilation system: sensor data for CH₄/CO/wind speed/temperature, etc. CREATE TABLE mine_ventilation_sensors ( collected_at TIMESTAMP_NTZ NOT NULL, sensor_id STRING NOT NULL, mine_id STRING NOT NULL, zone_id STRING NOT NULL, sensor_type STRING NOT NULL, -- CH4/CO/CO2/WIND_SPEED/TEMP/HUMIDITY value DOUBLE NOT NULL, unit STRING NOT NULL, device_status STRING NOT NULL, PRIMARY KEY (collected_at, sensor_id) ) PARTITIONED BY (DAYS(collected_at)); -- Personnel positioning (UWB, 1 second/reading) CREATE TABLE mine_personnel_location ( collected_at TIMESTAMP_NTZ NOT NULL, person_id STRING NOT NULL, mine_id STRING NOT NULL, zone_id STRING NOT NULL, location_x DOUBLE NOT NULL, location_y DOUBLE NOT NULL, depth DOUBLE NOT NULL, is_in_danger_zone BOOLEAN NOT NULL DEFAULT FALSE, shift_id STRING NOT NULL, PRIMARY KEY (collected_at, person_id) ) PARTITIONED BY (DAYS(collected_at)); -- Safety event ledger (cross-system linkage event markers) CREATE TABLE mine_safety_events ( event_at TIMESTAMP_NTZ NOT NULL, event_id STRING NOT NULL, mine_id STRING NOT NULL, zone_id STRING NOT NULL, source_system STRING NOT NULL, event_level STRING NOT NULL, -- INFO/WARNING/ALARM/EMERGENCY is_cross_system BOOLEAN NOT NULL DEFAULT FALSE, related_event_ids STRING, PRIMARY KEY (event_at, event_id) ) PARTITIONED BY (DAYS(event_at));

Key Indexes

CREATE BLOOMFILTER INDEX idx_bf_vent_zone ON TABLE mine_ventilation_sensors(zone_id); CREATE BLOOMFILTER INDEX idx_bf_person_zone ON TABLE mine_personnel_location(zone_id); CREATE BLOOMFILTER INDEX idx_bf_safety_level ON TABLE mine_safety_events(event_level);

Three-Layer Data Pipeline

Layer 1: Aggregation-Layer Dynamic Table (one per subsystem, 5-minute refresh)

-- Ventilation metrics aggregation: statistics on gas indicators by zone over the last 10 minutes CREATE DYNAMIC TABLE mine_ventilation_metrics REFRESH INTERVAL 5 MINUTE AS SELECT zone_id, mine_id, MAX(CASE WHEN sensor_type = 'CH4' THEN value END) AS ch4_max_pct, MIN(CASE WHEN sensor_type = 'WIND_SPEED' THEN value END) AS wind_speed_min, CASE WHEN MAX(CASE WHEN sensor_type = 'CH4' THEN value END) >= 1.5 THEN 'CRITICAL' WHEN MAX(CASE WHEN sensor_type = 'CH4' THEN value END) >= 1.0 THEN 'HIGH' WHEN MAX(CASE WHEN sensor_type = 'CH4' THEN value END) >= 0.7 THEN 'MEDIUM' ELSE 'NORMAL' END AS ch4_risk_level FROM mine_ventilation_sensors WHERE collected_at >= CURRENT_TIMESTAMP() - INTERVAL 10 MINUTE GROUP BY zone_id, mine_id;

Layer 2: Cross-System Correlation Anomaly Detection Dynamic Table (core differentiator)

-- JOIN five aggregation-layer tables together; power is precisely correlated via zone_id + mine_id; outputs a composite risk rating CREATE DYNAMIC TABLE mine_cross_system_anomalies REFRESH INTERVAL 5 MINUTE AS SELECT v.zone_id, v.ch4_risk_level, pw.switch_status AS feeder_switch_status, -- Note alias, not pw.switch_status -- Composite risk rating: joint judgment across multiple systems CASE WHEN v.ch4_risk_level = 'CRITICAL' AND COALESCE(p.persons_in_danger, 0) > 0 AND pw.switch_status = 'CLOSED' THEN 'CRITICAL' WHEN v.ch4_risk_level IN ('CRITICAL','HIGH') AND COALESCE(p.persons_in_danger, 0) > 0 THEN 'HIGH' ... END AS overall_risk_level, -- Structured summary, used as input for both AI_CLASSIFY and AI_COMPLETE CONCAT_WS('; ', CASE WHEN v.ch4_risk_level != 'NORMAL' THEN CONCAT('CH4 concentration ', v.ch4_max_pct, '%') END, CASE WHEN COALESCE(p.persons_in_danger, 0) > 0 THEN CONCAT(p.persons_in_danger, ' persons in hazardous zone') END, CASE WHEN pw.switch_status = 'CLOSED' AND v.ch4_risk_level IN ('HIGH','CRITICAL') THEN 'Feeder switch not tripped (power cutoff linkage required)' END ) AS anomaly_summary FROM mine_ventilation_metrics v LEFT JOIN mine_personnel_metrics p ON v.zone_id = p.zone_id AND v.mine_id = p.mine_id LEFT JOIN mine_power_metrics pw ON v.zone_id = pw.zone_id AND v.mine_id = pw.mine_id LEFT JOIN mine_excavation_metrics ex ON v.mine_id = ex.mine_id AND ex.working_face_id = CONCAT('FACE-', SUBSTR(v.zone_id, 6)) LEFT JOIN mine_transport_metrics tr -- Precise zone_id correlation: belt anomalies only associated with the local zone, not cross-zone pollution ON v.zone_id = tr.zone_id AND v.mine_id = tr.mine_id AND tr.device_type = 'CONVEYOR' WHERE v.ch4_risk_level != 'NORMAL' OR v.co_risk_level != 'NORMAL' OR ...;

Layer 3: AI Classification + AI Alert Work Order Generation (CTE single-call)

-- CTE structure: AI_CLASSIFY called once + AI_COMPLETE called once, avoiding inconsistent results from multiple calls WITH anomalies AS ( SELECT * FROM mine_cross_system_anomalies WHERE overall_risk_level IN ('HIGH', 'CRITICAL') ), classified AS ( SELECT *, REGEXP_EXTRACT(AI_CLASSIFY('conn_dashscope:deepseek-v3', anomaly_summary, ARRAY('GAS','FIRE','ROOF','FLOOD','EQUIPMENT','COMPOUND')), '(?s)\{.*\}', 0) AS classify_json FROM anomalies ), completed AS ( SELECT *, REGEXP_EXTRACT(AI_COMPLETE('conn_dashscope:deepseek-v3', CONCAT('You are a mine safety engineer. Disaster type: ', COALESCE(GET_JSON_OBJECT(classify_json,'$.label'),'unknown'), ', anomaly: ', anomaly_summary, ...)), '(?s)\{.*\}', 0) AS complete_json FROM classified ) INSERT INTO mine_ai_safety_alerts (...) SELECT GET_JSON_OBJECT(classify_json, '$.label') AS disaster_type, CAST(GET_JSON_OBJECT(classify_json, '$.score') AS DOUBLE) AS disaster_confidence, GET_JSON_OBJECT(complete_json, '$.alert_title') AS alert_title, GET_JSON_OBJECT(complete_json, '$.immediate_actions') AS immediate_actions, CAST(GET_JSON_OBJECT(complete_json, '$.evacuation_required') AS BOOLEAN) AS evacuation_required, CAST(GET_JSON_OBJECT(complete_json, '$.power_cutoff_required') AS BOOLEAN) AS power_cutoff_required, ... FROM completed;

Three Major Linkage Scenarios

Scenario	Systems Involved	Trigger Condition	AI Response Recommendation
Gas-Personnel-Power Linkage	Ventilation + Monitoring + Power	CH₄ ≥ 1.0% + Personnel present + Feeder not tripped	Immediate power cutoff / evacuation / enhanced ventilation
Belt Failure — Production Chain Interruption	Transport + Dispatch	Belt deviation > 50mm + Shearer in operation	Shutdown for maintenance / adjust production plan
Goaf Spontaneous Combustion Warning	Ventilation + Dispatch	CO concentration trend rising (> 24ppm and rate > 2ppm/10min)	Grouting to seal / adjust ventilation mode

Pipeline A: Real-Time Data Processing Pipeline

The existing 5-minute Dynamic Table refresh is sufficient for most scenarios, but some safety events (sudden CH₄ rise, sharp wind speed drop) need to be detected within 1 minute. The real-time pipeline adds a high-frequency bypass on top of the existing 5-layer pipeline:

Sensors → MQTT Collection Gateway → mine_ventilation_sensors ↓ Change Tracking driven Studio Task (1-minute Cron) ↓ Single-point threshold HAVING filter mine_realtime_alerts ↓ Push: WebSocket / Dispatch Console / Mobile

Key Design Decisions:

• 1-minute window aggregation (not per-record triggering): avoids massive false positives from sensor jitter while keeping latency to ≤ 90 seconds
• Regulation thresholds hardcoded: CH₄ 0.75%/1.0%/1.5%, CO 24/50ppm, wind speed 0.25m/s, temperature 26/30°C are written directly into CASE logic, not dependent on external configuration
• ingestion_lag_ms monitoring: records the delay from sensor to database ingestion per record, used in Studio monitoring dashboards to detect collection pipeline health
• Division of responsibilities with Dynamic Table: the real-time pipeline handles single-point extreme value triggering; Dynamic Tables handle multi-sensor aggregation and cross-system correlation — the two are complementary

-- Real-time pipeline core: 1-minute window, keep only records exceeding thresholds INSERT INTO mine_realtime_alerts (...) SELECT sensor_id, sensor_type, MAX(value) AS current_value, alert_rule, ... FROM mine_ventilation_sensors WHERE collected_at >= CURRENT_TIMESTAMP() - INTERVAL 1 MINUTE GROUP BY mine_id, zone_id, sensor_id, sensor_type HAVING (sensor_type = 'CH4' AND MAX(value) >= 0.75) OR (sensor_type = 'CO' AND MAX(value) >= 24) OR (sensor_type = 'WIND_SPEED' AND MAX(value) < 0.25) OR (sensor_type = 'TEMP' AND MAX(value) >= 26);

Pipeline B: Offline Unstructured Data Processing Pipeline

Mines have accumulated large amounts of unstructured data: historical accident investigation reports, equipment maintenance records, geological survey archives, and safety regulation training materials. These are the most valuable safety knowledge assets, but they have long been stored in isolation as PDFs, Word documents, or scanned files, inaccessible for AI retrieval.

The offline pipeline transforms these documents into a searchable structured knowledge base:

PDF / Word / Images (Object Storage) ↓ OCR / Document Parsing mine_unstructured_docs (raw text + metadata) ↓ Studio Task (daily batch) ↓ AI_COMPLETE (mine safety expert Prompt) mine_doc_knowledge (structured knowledge: summaries / risks / lessons learned) ↓ Real-time alert AI enhancement (RAG retrieval of historical similar incident response plans)

Supported Document Types:

doc_type	Source	AI Extraction Focus
ACCIDENT_REPORT	Accident investigation reports	Incident process, root cause, lessons learned
MAINTENANCE_LOG	Equipment maintenance records	Failure modes, replaced parts, prevention recommendations
GEO_SURVEY	Geological survey archives	Stratum structure, fault locations, gas occurrence patterns
REGULATION	Regulations / standard documents	Applicable clauses, key thresholds, operational requirements
TRAINING	Training materials	Safe operating procedures, emergency response workflows

RAG Enhancement: When a new alert occurs, historical accident experience from the same zone and disaster type can be retrieved from mine_doc_knowledge and injected into the AI_COMPLETE Prompt to make the generated response recommendations more targeted.

-- Retrieve similar historical incidents, inject into alert AI Prompt SELECT summary, lessons_learned FROM mine_doc_knowledge WHERE mine_id = 'MINE-001' AND involved_zones LIKE '%ZONE-101%' AND disaster_types LIKE '%GAS%' ORDER BY processed_at DESC LIMIT 3;

Complete Technical Implementation Matrix

The following covers the complete technical solutions for each business scenario, all implemented using Singdata Lakehouse native capabilities — no external frameworks required.

Ventilation System Extension: CH₄ Concentration Trend Prediction

The current real-time pipeline only performs single-point threshold evaluation. The following Dynamic Table adds trend prediction, providing 20–30 minutes of advance warning during slow CH₄ rise:

CREATE DYNAMIC TABLE mine_ch4_trend REFRESH INTERVAL 5 MINUTE AS SELECT zone_id, mine_id, MAX(CASE WHEN sensor_type = 'CH4' THEN value END) AS ch4_current, (AVG(CASE WHEN sensor_type = 'CH4' AND collected_at >= CURRENT_TIMESTAMP() - INTERVAL 5 MINUTE THEN value END) - AVG(CASE WHEN sensor_type = 'CH4' AND collected_at <= CURRENT_TIMESTAMP() - INTERVAL 25 MINUTE THEN value END) ) / 25.0 AS ch4_slope_per_min, MAX(CASE WHEN sensor_type = 'CH4' THEN value END) + (AVG(CASE WHEN sensor_type = 'CH4' AND collected_at >= CURRENT_TIMESTAMP() - INTERVAL 5 MINUTE THEN value END) - AVG(CASE WHEN sensor_type = 'CH4' AND collected_at <= CURRENT_TIMESTAMP() - INTERVAL 25 MINUTE THEN value END) ) AS ch4_predicted_30min, CASE WHEN MAX(CASE WHEN sensor_type = 'CH4' THEN value END) + (AVG(CASE WHEN sensor_type = 'CH4' AND collected_at >= CURRENT_TIMESTAMP() - INTERVAL 5 MINUTE THEN value END) - AVG(CASE WHEN sensor_type = 'CH4' AND collected_at <= CURRENT_TIMESTAMP() - INTERVAL 25 MINUTE THEN value END) ) >= 1.0 THEN 'TREND_WARN' WHEN MAX(CASE WHEN sensor_type = 'CH4' THEN value END) + (AVG(CASE WHEN sensor_type = 'CH4' AND collected_at >= CURRENT_TIMESTAMP() - INTERVAL 5 MINUTE THEN value END) - AVG(CASE WHEN sensor_type = 'CH4' AND collected_at <= CURRENT_TIMESTAMP() - INTERVAL 25 MINUTE THEN value END) ) >= 0.75 THEN 'TREND_CAUTION' ELSE 'STABLE' END AS trend_level FROM mine_ventilation_sensors WHERE collected_at >= CURRENT_TIMESTAMP() - INTERVAL 30 MINUTE AND sensor_type = 'CH4' GROUP BY zone_id, mine_id;

Transport System Extension: Belt Full Lifecycle Health Score

Calculate a health score of 0–100 per shift, weighted by deviation (40%), temperature (30%), and current overload (30%), to drive maintenance scheduling:

CREATE DYNAMIC TABLE mine_belt_health_score REFRESH INTERVAL 30 MINUTE AS SELECT device_id, mine_id, ROUND( GREATEST(0, 100 - MAX(CASE WHEN metric_name = 'DEVIATION' THEN value ELSE 0 END) / 70.0 * 40) * 0.4 + GREATEST(0, 100 - GREATEST(0, MAX(CASE WHEN metric_name = 'TEMP' THEN value ELSE 0 END) - 50) * 2) * 0.3 + GREATEST(0, 100 - GREATEST(0, MAX(CASE WHEN metric_name = 'CURRENT' THEN value ELSE 0 END) - 85) / 85.0 * 30) * 0.3, 1) AS health_score, CASE WHEN health_score < 40 THEN 'CRITICAL' -- Immediate maintenance WHEN health_score < 65 THEN 'DEGRADED' -- Scheduled maintenance within 72h WHEN health_score < 80 THEN 'WARNING' -- Monitor closely ELSE 'HEALTHY' END AS health_status FROM mine_transport_sensors WHERE device_type = 'CONVEYOR' AND collected_at >= CURRENT_TIMESTAMP() - INTERVAL 480 MINUTE GROUP BY device_id, mine_id;

Monitoring System Extension: Personnel Abnormal Behavior Detection

Analyze trajectory for stationary duration to identify emergency states such as collapse or injury, linked with personnel risk rating to trigger search and rescue plans:

CREATE DYNAMIC TABLE mine_personnel_behavior REFRESH INTERVAL 3 MINUTE AS SELECT person_id, mine_id, zone_id, shift_id, MAX(collected_at) AS last_seen_at, SUM(CASE WHEN location_x = LAG(location_x) OVER (PARTITION BY person_id ORDER BY collected_at) AND location_y = LAG(location_y) OVER (PARTITION BY person_id ORDER BY collected_at) THEN 1 ELSE 0 END) OVER (PARTITION BY person_id) AS static_seconds, is_in_danger_zone, CASE WHEN static_seconds > 300 AND is_in_danger_zone THEN 'EMERGENCY' -- Stationary 5 min in danger zone WHEN static_seconds > 600 THEN 'ABNORMAL' -- Stationary 10 min in any zone ELSE 'NORMAL' END AS behavior_status FROM mine_personnel_location WHERE collected_at >= CURRENT_TIMESTAMP() - INTERVAL 10 MINUTE;

Power System Extension: Load Forecasting and Reactive Power Compensation Optimization

Use AI_COMPLETE to forecast the next hour's load based on historical same-period features (same weekday, same time slot), proactively scheduling reactive power compensation timing. Target power factor ≥ 0.92, saving 300,000–800,000 RMB per mine per year:

CREATE DYNAMIC TABLE mine_power_load_features REFRESH INTERVAL 30 MINUTE AS SELECT substation_id, mine_id, DAYOFWEEK(collected_at) AS day_of_week, HOUR(collected_at) AS hour_of_day, AVG(CASE WHEN metric_name = 'ACTIVE_POWER' THEN value END) AS avg_load_kw, MIN(CASE WHEN metric_name = 'POWER_FACTOR' THEN value END) AS min_pf FROM mine_power_sensors WHERE collected_at >= CURRENT_TIMESTAMP() - INTERVAL 10080 MINUTE GROUP BY substation_id, mine_id, DAYOFWEEK(collected_at), HOUR(collected_at);

Excavation System Extension: Equipment Predictive Health Management (PHM)

Based on feature vectors of current fluctuation, overload count, and hydraulic pressure, AI predicts remaining useful life (RUL) and automatically dispatches P1/P2 maintenance work orders:

CREATE DYNAMIC TABLE mine_excavation_features REFRESH INTERVAL 30 MINUTE AS SELECT device_id, device_type, mine_id, working_face_id, AVG(CASE WHEN metric_name = 'CURRENT' THEN value END) AS avg_current, STDDEV(CASE WHEN metric_name = 'CURRENT' THEN value END) AS current_stddev, MIN(CASE WHEN metric_name = 'PRESSURE' THEN value END) AS min_pressure, COUNT(CASE WHEN metric_name = 'CURRENT' AND value > 420 THEN 1 END) AS overload_count FROM mine_excavation_sensors WHERE collected_at >= CURRENT_TIMESTAMP() - INTERVAL 480 MINUTE GROUP BY device_id, device_type, mine_id, working_face_id;

When overload_count >= 3, AI_COMPLETE RUL prediction is triggered, outputting {"rul_hours": value, "priority": "P1/P2", "advice": "..."} and automatically writing to mine_maintenance_orders.

Dispatch System Extension: Intelligent Ventilation Network Optimization

Ventilation network computation: back-calculate required airflow from CH₄ concentration in each zone, AI outputs local fan adjustment plans written to the dispatch table. Target ventilation power consumption reduction of 15–20%, while ensuring CH₄ < 0.5% in all zones.

Mine-Wide: Group-Level Safety Management

Group-level unified workspace, real-time safety score ranking for each mine, prioritizing intervention at the mine with the lowest safety score:

CREATE DYNAMIC TABLE group_mine_safety_dashboard REFRESH INTERVAL 10 MINUTE AS SELECT mine_id, COUNT(CASE WHEN overall_risk_level = 'CRITICAL' THEN 1 END) AS critical_count, COUNT(CASE WHEN overall_risk_level = 'HIGH' THEN 1 END) AS high_count, SUM(persons_in_danger) AS total_persons_at_risk, GREATEST(0, 100 - COUNT(CASE WHEN overall_risk_level = 'CRITICAL' THEN 1 END) * 20 - COUNT(CASE WHEN overall_risk_level = 'HIGH' THEN 1 END) * 10 - SUM(persons_in_danger) * 5) AS safety_score FROM mine_cross_system_anomalies GROUP BY mine_id ORDER BY safety_score ASC;

Knowledge Base Retrieval: Full-Text Search + Vector Search + Hybrid Search

The accident reports, maintenance records, and geological archives accumulated over the years in a mine are the most valuable safety knowledge assets. Singdata natively supports full-text search and vector search, transforming these unstructured documents into a machine-searchable knowledge base to provide RAG enhancement for real-time alerts.

Full-Text Search: Dispatchers quickly locate relevant cases using natural language keywords, supporting Chinese IK word segmentation:

-- Scenario: after an alert triggers, the dispatcher searches "ZONE-101 gas power cutoff" for historical response experience SELECT doc_id, doc_type, summary, lessons_learned, MATCH(chunk_text, summary) AGAINST ('ZONE-101 gas power cutoff' IN NATURAL LANGUAGE MODE) AS relevance FROM mine_doc_knowledge WHERE mine_id = 'MINE-001' AND MATCH(chunk_text, summary) AGAINST ('ZONE-101 gas power cutoff' IN NATURAL LANGUAGE MODE) ORDER BY relevance DESC LIMIT 5; -- Full-text search index (established in setup.sql) CREATE FULLTEXT INDEX idx_ft_know_text ON TABLE mine_doc_knowledge(chunk_text, summary, key_findings, lessons_learned) WITH PROPERTIES ('analyzer' = 'ik_max_word');

Vector Search: Semantic similarity-based retrieval captures cases with "different wording but identical meaning," providing broader coverage than keyword search:

-- Scenario: vectorize the current alert summary, find the most semantically similar historical incidents WITH alert_vec AS ( SELECT AI_EMBEDDING('conn_dashscope:text-embedding-v3', anomaly_summary) AS q_vec FROM mine_ai_safety_alerts WHERE alert_id = 'ALERT-ZONE-101-xxx' ) SELECT k.doc_id, k.summary, k.lessons_learned, COSINE_SIMILARITY(k.content_vector, a.q_vec) AS similarity FROM mine_doc_knowledge k CROSS JOIN alert_vec a WHERE k.mine_id = 'MINE-001' AND k.doc_type = 'ACCIDENT_REPORT' ORDER BY similarity DESC LIMIT 5; -- Vector index (HNSW approximate nearest neighbor) CREATE VECTOR INDEX idx_vec_know_content ON TABLE mine_doc_knowledge(content_vector) WITH PROPERTIES ('metric_type' = 'cosine', 'index_type' = 'hnsw');

Hybrid Search: Full-text recall × vector re-ranking, optimizing both precision and recall:

-- Full-text search recalls Top-20, then re-ranked by vector semantics, weighted fusion outputs Top-5 WITH kw_results AS ( SELECT doc_id, chunk_id, summary, lessons_learned, MATCH(chunk_text, summary) AGAINST ('gas power cutoff evacuation') AS kw_score FROM mine_doc_knowledge WHERE mine_id = 'MINE-001' AND MATCH(chunk_text, summary) AGAINST ('gas power cutoff evacuation') LIMIT 20 ), reranked AS ( SELECT k.*, COSINE_SIMILARITY(d.content_vector, AI_EMBEDDING('conn_dashscope:text-embedding-v3', 'CH4 overrun personnel evacuation power cutoff response plan') ) AS vec_score FROM kw_results k JOIN mine_doc_knowledge d ON k.doc_id = d.doc_id AND k.chunk_id = d.chunk_id ) SELECT doc_id, summary, lessons_learned, kw_score * 0.3 + vec_score * 0.7 AS hybrid_score -- Higher weight on vector FROM reranked ORDER BY hybrid_score DESC LIMIT 5;

All three retrieval capabilities are expressed directly in SQL — no separate search engine (Elasticsearch/Milvus) required.

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4. Singdata Technical Advantages

Dynamic Table: Two-Level Real-Time Incremental Processing — Replacing the Flink + Batch Dual Stack

Mine sensors generate thousands of records per second; safety alerts cannot wait for batch processing windows. Singdata uses a single mechanism to cover both minute-level aggregation and second-level trend computation:

Layer	Refresh Interval	Purpose	Traditional Equivalent
Real-time Pipeline	1 min (Studio Cron)	Single-point threshold extreme value triggering	Flink CEP Rule Engine
Aggregation DT	5 min	Multi-sensor rolling aggregation + risk rating	Spark Structured Streaming
Anomaly DT	5 min	Cross-system correlation + composite rating	Independent correlation service
Trend Prediction DT	5 min	CH₄ slope + 30min prediction	Independent time-series prediction service

-- 4 time window types, same SQL paradigm, zero additional systems CREATE DYNAMIC TABLE mine_ch4_trend REFRESH INTERVAL 5 MINUTE AS ...; CREATE DYNAMIC TABLE mine_belt_health REFRESH INTERVAL 30 MINUTE AS ...; CREATE DYNAMIC TABLE mine_power_features REFRESH INTERVAL 30 MINUTE AS ...;

Change Tracking drives incremental computation — TB-scale historical data does not affect refresh performance. Traditional solutions require a separate stream processing cluster (Flink/Spark Streaming) plus a batch scheduling system. Singdata replaces both with declarative SQL.

AI_CLASSIFY + AI_COMPLETE: SQL-Embedded Two-Level AI Inference

Mine safety requires two fundamentally different AI capabilities, which Singdata chains together in a single CTE:

Level 1: AI_CLASSIFY Fast Classification (low latency, low cost)

Performs multi-class classification on anomaly summaries, outputting disaster type labels (GAS/FIRE/ROOF/FLOOD/EQUIPMENT/COMPOUND). Compared to using AI_COMPLETE to ask "what type of disaster is this," classification is faster, consumes fewer tokens, and is suited for high-frequency trigger scenarios:

AI_CLASSIFY( 'conn_dashscope:deepseek-v3', anomaly_summary, ARRAY('GAS', 'FIRE', 'ROOF', 'FLOOD', 'EQUIPMENT', 'COMPOUND') ) AS classify_json -- Returns {"label":"GAS","score":0.92}

Level 2: AI_COMPLETE Structured Response Plan Generation (deep reasoning)

Injects the disaster type + cross-system anomaly summary + RAG-retrieved historical accident experience together into the Prompt to generate a complete response plan. The CTE structure ensures each level is called exactly once, avoiding result inconsistencies from repeated calls:

WITH classified AS ( SELECT *, REGEXP_EXTRACT(AI_CLASSIFY(...), '(?s)\{.*\}', 0) AS classify_json FROM anomalies ), completed AS ( SELECT *, REGEXP_EXTRACT(AI_COMPLETE('conn_dashscope:deepseek-v3', CONCAT('Disaster type: ', GET_JSON_OBJECT(classify_json,'$.label'), '; Historical similar cases: ', rag_context, '; Current anomaly: ', anomaly_summary, ...)), '(?s)\{.*\}', 0) AS complete_json FROM classified ) -- All fields extracted from the same JSON, results fully consistent SELECT GET_JSON_OBJECT(complete_json, '$.alert_title') AS alert_title, GET_JSON_OBJECT(complete_json, '$.immediate_actions') AS immediate_actions, ... FROM completed;

Compared to a standalone AI platform: zero data movement, no additional latency, no ETL cost.

API Connection: Unified Multi-Model Management

All AI calls go through a single conn_dashscope connection entry, supporting runtime model switching, key rotation, and rate-limiting circuit breakers — without modifying any SQL logic:

-- Currently using DeepSeek-V3; switching to Qwen-Max only requires updating the Connection configuration CREATE API CONNECTION IF NOT EXISTS conn_dashscope TYPE ai_function PROPERTIES ( 'BASE_URL' = 'https://dashscope.aliyuncs.com/compatible-mode/v1', 'API_KEY' = '<your-api-key>' ); -- SQL references remain unchanged: AI_COMPLETE('conn_dashscope:deepseek-v3', ...) -- → AI_COMPLETE('conn_dashscope:qwen-max', ...)

Supports connecting to multiple LLMs (DashScope, DeepSeek, OpenAI-compatible interfaces), as well as domain fine-tuned mine safety proprietary models.

BloomFilter: Millisecond-Level Retrieval Across Tens of Millions of Monitoring Points

A single mine has 500–2,000 sensor points; medium-sized mine areas can reach tens of thousands; group-level can reach hundreds of thousands. BloomFilter indexes reduce point-query latency on zone_id/sensor_type/event_level from seconds to milliseconds — the foundation for high-frequency alert queries:

CREATE BLOOMFILTER INDEX idx_bf_vent_zone ON TABLE mine_ventilation_sensors(zone_id); CREATE BLOOMFILTER INDEX idx_bf_alert_level ON TABLE mine_ai_safety_alerts(overall_risk_level); CREATE BLOOMFILTER INDEX idx_bf_rt_level ON TABLE mine_realtime_alerts(threshold_level);

Full-Text Search: Fast Keyword Lookup in Accident Knowledge Base

Accident investigation reports, regulation clauses, and maintenance records accumulated over the years can be indexed with FULLTEXT INDEX, allowing dispatchers to search directly with Chinese keywords (e.g., "ZONE-101 gas power cutoff") — no separate Elasticsearch cluster required:

-- Full-text index (IK Chinese word segmentation, supports mine-specific terminology) CREATE FULLTEXT INDEX idx_ft_know_text ON TABLE mine_doc_knowledge(chunk_text, summary, key_findings, lessons_learned) WITH PROPERTIES ('analyzer' = 'ik_max_word'); -- Full-text search on safety alert response records CREATE FULLTEXT INDEX idx_ft_alert_actions ON TABLE mine_ai_safety_alerts(immediate_actions, alert_title) WITH PROPERTIES ('analyzer' = 'ik_max_word');

Why full-text search fits this scenario: Mine accident reports contain extensive industry terminology (e.g., "fully-mechanized mining face," "roof weighting," "goaf spontaneous combustion"). Exact keyword matching is more than 100× faster than LIKE fuzzy queries, with relevance ranking, helping dispatchers find the most relevant historical response plans in seconds.

-- After an alert triggers, automatically retrieve related historical experience (millisecond response) SELECT summary, lessons_learned, MATCH(chunk_text, summary) AGAINST ('CH4 overrun power cutoff evacuation' IN NATURAL LANGUAGE MODE) AS score FROM mine_doc_knowledge WHERE mine_id = 'MINE-001' AND MATCH(chunk_text, summary) AGAINST ('CH4 overrun power cutoff evacuation' IN NATURAL LANGUAGE MODE) ORDER BY score DESC LIMIT 5;

Vector Search: Semantic Similarity Search and RAG Enhancement

Keyword search depends on exact vocabulary matching and cannot handle cases with "different wording but identical meaning." Vector search is based on semantic vectors generated by AI_EMBEDDING — even with completely different phrasing, semantically similar content can be retrieved:

-- Vector index (HNSW algorithm, cosine similarity) CREATE VECTOR INDEX idx_vec_know_content ON TABLE mine_doc_knowledge(content_vector) WITH PROPERTIES ('metric_type' = 'cosine', 'index_type' = 'hnsw'); -- AI_EMBEDDING generates vectors (called in pipeline.sql Pipeline B) AI_EMBEDDING('conn_dashscope:text-embedding-v3', summary_text) AS content_vector

RAG Three-Step Process (entirely within SQL):

-- Step 1: Vectorize the current alert -- Step 2: Vector search for Top-K similar historical cases -- Step 3: Inject search results into AI_COMPLETE Prompt WITH query_vec AS ( SELECT AI_EMBEDDING('conn_dashscope:text-embedding-v3', anomaly_summary) AS vec FROM mine_ai_safety_alerts WHERE alert_id = 'ALERT-ZONE-101-xxx' ), top_k AS ( SELECT k.lessons_learned, COSINE_SIMILARITY(k.content_vector, q.vec) AS sim FROM mine_doc_knowledge k CROSS JOIN query_vec q WHERE k.doc_type = 'ACCIDENT_REPORT' ORDER BY sim DESC LIMIT 3 ) SELECT AI_COMPLETE('conn_dashscope:deepseek-v3', CONCAT('Historical similar incident experience:\n', GROUP_CONCAT(lessons_learned SEPARATOR '\n'), '\n\nBased on the above experience, provide response recommendations: ', anomaly_summary) ) AS rag_advice FROM top_k, mine_ai_safety_alerts WHERE alert_id = 'ALERT-ZONE-101-xxx';

Synergy with BloomFilter: Before vector search, use BloomFilter to filter by mine_id and doc_type to narrow the candidate set, then perform ANN search — reducing retrieval latency from seconds to hundreds of milliseconds.

Why a standalone vector database (Milvus/Pinecone) is not needed: Singdata natively supports the VECTOR field type and VECTOR INDEX. Vector search and structured filtering (mine_id, zone_id, doc_type) are completed in a single SQL statement, with no cross-system data synchronization required.

Lakehouse Unified Architecture: Eliminating Data Silos

Traditional mine six-system architectures are each independently operated; cross-system correlation analysis requires a custom integration layer. Singdata Lakehouse places all data in the same storage layer, making cross-system JOINs nearly indistinguishable in performance from single-table queries:

-- Six subsystems, one SQL completing cross-system correlation, no intermediate layer, no data copying FROM mine_ventilation_metrics v LEFT JOIN mine_personnel_metrics p ON v.zone_id = p.zone_id AND v.mine_id = p.mine_id LEFT JOIN mine_power_metrics pw ON v.zone_id = pw.zone_id AND v.mine_id = pw.mine_id LEFT JOIN mine_excavation_metrics ex ON ... LEFT JOIN mine_transport_metrics tr ON ...

The same platform supports real-time streaming (Dynamic Table), batch processing (INSERT SELECT), AI inference (AI_COMPLETE), and unstructured processing (offline Pipeline B) — unified operations and unified access control.

Studio: End-to-End Operations Automation

Studio incorporates the entire data pipeline into unified scheduling — no external scheduling tools (Airflow/DolphinScheduler) required:

Studio Capability	Usage in the Mine Solution
Task scheduled execution (Cron)	Real-time pipeline 1-min alerts + offline pipeline daily knowledge base updates
Workflow DAG orchestration	Dependency ordering for REFRESH DT → AI inference → alert write
API Connection management	DeepSeek key encrypted storage, runtime model switching
Runtime monitoring dashboard	DT refresh latency, AI inference success rate, ingestion_lag_ms visualization
SQL editor	Dynamic Table interactive debugging, AI_COMPLETE Prompt iteration

Technical Advantages Comparison Summary

Capability Dimension	Traditional Solution	Singdata Lakehouse
Real-time alert latency	5–15 minutes (batch window)	≤ 90 seconds (real-time pipeline) / 5 minutes (DT aggregation)
Cross-system correlation	Custom integration layer, months to deliver	SQL JOIN, delivered in days
AI response recommendations	Standalone AI platform + ETL sync	AI_COMPLETE embedded, zero data movement
Disaster type classification	Manual judgment / rule engine	AI_CLASSIFY millisecond-level classification
Historical knowledge retrieval	Manual archive search	Full-text search (IK segmentation) + vector search (HNSW) + Hybrid Search
RAG enhancement	Standalone vector DB (Milvus) + sync pipeline	SQL-embedded AI_EMBEDDING + VECTOR INDEX, zero external dependencies
Unstructured documents	Standalone document management, data isolated	Offline pipeline unified ingestion, knowledge accumulation
Operational complexity	Kafka + Flink + Spark + AI platform + ETL	Single Singdata + Studio
PoC delivery cycle	6–12 months	6 weeks

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5. Customer Value

ROI Estimation

Metric	Before	After	Improvement
Major safety incidents	Baseline	-50%+	Reduced compliance risk, fewer regulatory penalties
Unplanned downtime duration	Baseline	-40%+	At coal price 820 RMB/ton, each 1h reduction avoids losses of 200,000–800,000 RMB
Violation detection rate	~30% (manual inspection)	90%+	Violation-caused accident rate down 60%
Incident investigation cycle	3–7 days	< 1 day	System automatically correlates multi-system logs
PoC construction cycle	Traditional 6–12 months	6 weeks	SQL-first, no separate ETL + AI platform

Operational Efficiency Improvement

Direction	Improvement
Dispatch decisions	Data-driven replaces experience-based judgment; alert lead time increases from 0 to 15–30 minutes
Compliance reporting	Automatically generates regulatory-required reports; data retained ≥ 1 year
Maintenance planning	AI predicts remaining useful life of equipment, shifting from "emergency repairs" to "preventive maintenance"

Construction Cost Comparison with Traditional Solutions

Dimension	Traditional Solution (Distributed Systems)	Singdata Lakehouse
Number of systems	6+ independent systems	1 unified platform
Cross-system linkage	Requires custom integration layer	SQL JOIN natively supported
AI capability	Standalone AI platform + MLOps	AI_COMPLETE built-in
Operations staff	Dedicated ops required per system	Unified operations
Data retention cost	Distributed storage, high duplication cost	Unified Lakehouse, low storage cost

Full Capability Overview

This solution covers the core capabilities for the current phase and includes the technical foundation for the following advanced scenarios:

Scenario	Core Technology	Expected Value	Status
Six-system real-time early warning	Dynamic Table + cross-system JOIN	Alert lead time 15–30 min	Implemented
AI disaster classification + response recommendations	AI_CLASSIFY + AI_COMPLETE CTE	Response plans auto-generated	Implemented
Real-time pipeline (1-min window)	Change Tracking + Studio Cron	Latency ≤ 90s, regulation thresholds hardcoded	Implemented
Offline knowledge base + RAG	AI_COMPLETE + mine_doc_knowledge	Historical experience injected into Prompt	Implemented
Full-text search (accident knowledge base)	FULLTEXT INDEX + IK segmentation	Chinese keyword case lookup in seconds	Implemented
Vector search (semantic similarity)	VECTOR INDEX + AI_EMBEDDING + HNSW	RAG semantic enhancement, no standalone vector DB	Implemented
Hybrid Search	Full-text recall + vector re-ranking, weighted fusion	Optimized precision + recall	Implemented
CH₄ concentration trend prediction	Dynamic Table sliding window slope	Alert lead time +20–30 min	Extensible
Belt health scoring	Shift-aggregated weighted scoring	Unplanned downtime -40%	Extensible
Personnel abnormal behavior detection	Window function trajectory stationary analysis	Reduced search and rescue response time	Extensible
Power load forecasting	Historical features + AI_COMPLETE time-series prediction	Annual energy savings 300k–800k RMB/mine	Extensible
Equipment PHM (RUL prediction)	Feature engineering + AI prediction + auto work orders	Downtime reduced 50%	Extensible
Intelligent ventilation network optimization	Ventilation network computation + AI fan scheduling	Ventilation power consumption -15–20%	Extensible
Digital twin 3D visualization	3D data interface + real-time view DT	Dispatch decision time reduced to seconds	Planned
Mine safety domain large model	Corpus construction + SFT fine-tuning	AI recommendation accuracy +30–50%	Planned
Automated compliance reporting	AQ 1029 format + Studio push	Zero manual effort, zero missed reports	Planned
Multi-mine group management	Cross-workspace sync + safety scoring	Group accident rate -30%	Planned

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6. Quick Start

Prerequisites

1. Singdata Lakehouse workspace (schema already created)
2. DashScope / DeepSeek API Key (for AI_COMPLETE)

Execution Order

# Step 1: Create tables, indexes, Change Tracking (first run only) execute setup.sql # Step 2: Write test data execute test_data.sql # ⏱ Execute step 3 immediately after step 2 (within 8 minutes) # Step 3: Create Dynamic Tables and immediately REFRESH execute pipeline.sql # Contains CREATE DYNAMIC TABLE statements REFRESH DYNAMIC TABLE mine_ventilation_metrics; REFRESH DYNAMIC TABLE mine_transport_metrics; REFRESH DYNAMIC TABLE mine_excavation_metrics; REFRESH DYNAMIC TABLE mine_power_metrics; REFRESH DYNAMIC TABLE mine_personnel_metrics; REFRESH DYNAMIC TABLE mine_production_metrics; REFRESH DYNAMIC TABLE mine_cross_system_anomalies; # Step 4: Execute AI alert generation (replace API Key in pipeline.sql first) # Execute the INSERT INTO mine_ai_safety_alerts section of pipeline.sql

Validation Queries

-- 1. Aggregation layer: confirm ZONE-101 CH4 exceeds production cutoff threshold (≥ 1.0%) SELECT zone_id, ch4_max_pct, co_max_ppm, wind_speed_min, temp_max_celsius, ch4_risk_level, co_risk_level, temp_risk_level FROM mine_ventilation_metrics ORDER BY ch4_max_pct DESC; -- Expected: ZONE-101 ch4_max_pct=1.62 (CRITICAL), co_max_ppm=32, ch4_risk_level=CRITICAL -- 2. Cross-system anomaly layer: ZONE-101 CRITICAL (CH4 overrun + personnel present + feeder not tripped) SELECT zone_id, overall_risk_level, persons_in_danger, feeder_switch_status, anomaly_summary FROM mine_cross_system_anomalies ORDER BY overall_risk_level; -- Expected: ZONE-101 overall_risk_level=CRITICAL, persons_in_danger=3 -- anomaly_summary contains "CH4 concentration 1.62% [critical overrun]; 3 persons in hazardous zone; feeder switch not tripped" -- 3. Personnel risk check (3 in danger zone) SELECT zone_id, total_persons, persons_in_danger, personnel_risk_level FROM mine_personnel_metrics WHERE personnel_risk_level != 'NORMAL'; -- Expected: ZONE-101 persons_in_danger=3, personnel_risk_level=CRITICAL -- 4. Transport risk (ZONE-201 belt deviation) SELECT zone_id, device_id, belt_deviation_mm, transport_risk_level, device_status FROM mine_transport_metrics WHERE transport_risk_level != 'NORMAL'; -- Expected: ZONE-201 belt_deviation_mm=68, transport_risk_level=HIGH, device_status=ALARM -- 5. AI alert work orders SELECT alert_id, zone_id, overall_risk_level, disaster_type, disaster_confidence, alert_title, evacuation_required, power_cutoff_required, immediate_actions, responsible_team FROM mine_ai_safety_alerts ORDER BY alert_at DESC; -- Expected: HIGH/CRITICAL alerts have complete immediate_actions, disaster_type=GAS, -- evacuation_required=true, power_cutoff_required=true

Re-validation (When Test Data Has Expired)

If the previously written test data was inserted more than 10 minutes ago, Dynamic Tables will return empty results. Reset steps:

# Option A: Clear data only, keep table structure (recommended, faster) # Uncomment TRUNCATE statements in teardown.sql and execute, # then restart from step 2 # Option B: Full rebuild (drop all objects and re-run setup.sql) execute teardown.sql # DROP all tables and DTs execute setup.sql # Rebuild table structure # Then start from step 2

Clean Up Environment

execute teardown.sql # Executes in DROP mode by default (confirm TRUNCATE comments are not active or skip the TRUNCATE section)

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Related Documentation

AI Functions

Document	Description
AI Functions Overview	General introduction to AI functions, model selection, invocation methods, and billing
AI_CLASSIFY	Multi-label classification function; used in this solution for rapid disaster type classification (GAS/FIRE/ROOF/FLOOD/EQUIPMENT/COMPOUND) at lower cost than AI_COMPLETE
AI_COMPLETE	General LLM inference function; used in this solution to generate structured response plans (evacuation/power cutoff/emergency commands) and supports RAG injection of historical incident experience
AI_EMBEDDING	Text vectorization function; used in this solution to convert accident reports and alert summaries to semantic vectors, powering vector search and RAG enhancement
CREATE API CONNECTION	Create API Connection for unified LLM credential management, supporting runtime model switching without modifying SQL

Dynamic Table

Document	Description
Dynamic Table Introduction	Dynamic Table core concepts, incremental refresh mechanism, and comparison with Flink/Spark Streaming
Dynamic Table Development Guide	End-to-end example of creating, refreshing, and viewing history
CREATE DYNAMIC TABLE	Table creation syntax reference, including REFRESH INTERVAL, change_tracking, and other parameter descriptions
Dynamic Table Refresh Scheduling	Scheduled refresh configuration, controlling refresh frequency for each aggregation layer (5 min), health score layer (30 min), and group dashboard (10 min)
Using Studio to Develop and Monitor Dynamic Tables	Monitor Dynamic Table refresh latency and multi-layer pipeline health via Studio

Indexes

Document	Description
Index Overview	Use cases and selection comparison for various index types (BloomFilter / Inverted / Vector)
BloomFilter Index	BloomFilter working principles; used in this solution to accelerate equality queries on high-cardinality columns such as zone_id / event_level / sensor_type
CREATE BLOOMFILTER INDEX	BloomFilter index creation syntax reference
Inverted Index	Inverted index overview, supporting full-text search and IK Chinese word segmentation
CREATE INVERTED INDEX	Full-text index creation syntax; used in this solution for Chinese keyword search in accident reports and response records (supporting mine-specific terminology)
Vector Index	Vector index overview, supporting HNSW approximate nearest neighbor search and cosine similarity
CREATE VECTOR INDEX	Vector index creation syntax; used in this solution for semantic similarity search, synergizing with BloomFilter to narrow the candidate set
Vector Search and RAG Applications in Practice	End-to-end RAG practice including vectorization, retrieval, and Prompt injection, directly corresponding to the RAG enhancement pattern in this solution
Best Practices for Full-Text + Vector Hybrid Search with RRF	Hybrid Search best practices, corresponding to the full-text recall + vector re-ranking weighted fusion pattern in this solution
Lakehouse Index Best Practices Guide	Combined usage strategies for partition pruning + BloomFilter + full-text + vector indexes

Partitioned Tables

Document	Description
Partitioning and Bucketing	Partition table design concepts, including PARTITIONED BY (DAYS(...)) time partitioning usage
Partitioned Table Usage Guide	Partition table guidelines, including the constraint that composite primary keys must include the partition key (the basis for collected_at/event_at being first in the primary key for each table in this solution)

Window Functions

Document	Description
Window Functions Overview	Window function syntax, including OVER (PARTITION BY ... ORDER BY ...) usage
lag	Lag function; used in personnel behavior detection in this solution to compare previous and current positions to determine if stationary

JSON and Regular Expression Functions

Document	Description
get_json_object	Extract specified path fields from JSON strings; used in this solution to parse $.label, $.advice, and other fields returned by AI_CLASSIFY and AI_COMPLETE
regexp_extract	Regular expression extraction function; used in this solution with DOTALL mode (?s)\{.*\} to strip markdown code fences that may appear in LLM responses

Studio Tasks and Scheduling

Document	Description
Studio Task Development and Operations	Studio Task creation, deployment, and Cron scheduling; used in this solution for real-time pipeline 1-minute window triggering and offline knowledge base daily batch updates
Studio Task Development and Operations Best Practices	Studio Task best practices, including DAG dependency orchestration and runtime monitoring alert configuration