SANS.AI Documentation

Welcome to the official documentation for SANS.AI (Smart Airport Navigation System), the comprehensive AI-powered platform designed to optimize airport operations.

What is SANS.AI?

SANS.AI is a state-of-the-art software platform that leverages artificial intelligence, machine learning, and real-time data analytics to provide comprehensive airport management solutions. The system is designed to enhance operational efficiency, improve safety, and optimize resource allocation throughout airport facilities.

Key Capabilities

• Real-time Flight Tracking: Monitor all aspects of flight operations with comprehensive dashboards
• Predictive Analytics: Anticipate operational challenges with AI-powered predictions
• Enhanced Safety Monitoring: Improve airport safety through automated monitoring systems
• LiDAR Passenger Flow Analysis: Track and optimize passenger movements throughout terminals
• Resource Optimization: Maximize efficiency with intelligent gate assignments and resource allocation
• Intelligent Notifications: Stay informed with automated alerts and recommendations

System Architecture

SANS.AI is built on a modular, scalable architecture that can be deployed in various configurations depending on the size and needs of the airport. The core components include:

• Data Collection Layer: Integrates with flight systems, weather data, LiDAR sensors, and various other data sources
• AI Processing Engine: Advanced machine learning models that analyze data in real-time
• Insight Delivery System: Provides actionable recommendations and visualizations
• RESTful API: Allows for seamless integration with existing airport systems
• Security Framework: Ensures data protection and controlled access to sensitive information

Installation & Setup

This section covers the installation process for SANS.AI in various deployment scenarios. The platform can be deployed as a cloud-based service, on-premises solution, or hybrid setup.

System Requirements

Before installing SANS.AI, ensure your environment meets the following minimum requirements:

Cloud Deployment

SANS.AI can be deployed on major cloud platforms including AWS, Azure, and Google Cloud. Our recommended setup uses containerization with Kubernetes for optimal scalability.

For cloud deployments, we provide Terraform templates and Helm charts for streamlined setup:

# Clone the deployment repository
git clone https://github.com/sans-ai/deployment-cloud.git

# Navigate to your preferred cloud provider directory
cd deployment-cloud/aws  # or azure, gcp

# Configure environment variables
cp .env.example .env
# Edit .env file with your specific configuration

# Run the deployment script
./deploy.sh

On-Premises Deployment

For on-premises installations, we provide installation packages for supported operating systems:

# For Ubuntu/Debian-based systems
sudo apt-get update
sudo apt-get install sans-ai-server sans-ai-client

# For RHEL/CentOS systems
sudo yum install sans-ai-server sans-ai-client

# For Windows Server (using PowerShell as Administrator)
Install-Package -Name SansAiServer
Install-Package -Name SansAiClient

After installation, the configuration utility can be run to set up your specific environment:

# Run the configuration utility
sudo sans-ai-config

Initial Configuration

Upon first launch, you'll be guided through the initial configuration process, which includes:

1. Database Setup: Configure database connection and initialize schema
2. Administrator Account: Create the initial admin user
3. Integration Settings: Configure connections to airport systems
4. LiDAR Configuration (if applicable): Set up LiDAR sensor integration
5. Email/Notification Settings: Configure notification delivery methods
6. Security Parameters: Set up authentication methods and security policies

The configuration wizard will guide you through each step and validate settings as you proceed.

Dashboard Overview

The SANS.AI dashboard provides a comprehensive view of your airport operations and serves as the main interface for system interaction. This section outlines the key components and functionality of the dashboard.

Dashboard Layout

The dashboard is organized into several key sections:

• Navigation Bar: Access different modules and system functions
• Overview Panel: Key metrics and status indicators for immediate situational awareness
• Flight Status: Real-time information on all flights
• Terminal Status: Passenger flow and terminal utilization metrics
• Resource Allocation: Gate assignments and ground resource status
• Weather Conditions: Current and forecasted weather affecting operations
• Alerts Panel: Notifications requiring attention

Each user can customize their dashboard view based on their role and preferences using the Dashboard Customization feature.

Navigation

The main navigation menu provides access to the following modules:

• Dashboard: Main overview screen
• Flights: Detailed flight tracking and management
• Terminal: Passenger flow analysis and terminal operations
• Resources: Gate management and resource allocation
• Safety: Safety monitoring and alerts
• Analytics: Historical data analysis and reports
• Settings: System configuration and user preferences

User Interface Elements

The dashboard incorporates various UI elements designed for efficient airport operations management:

• Interactive Maps: Visual representation of terminals, gates, and runways
• Real-time Charts: Dynamic visualizations of operational metrics
• Status Cards: At-a-glance information about specific operational aspects
• Alert Notifications: Color-coded alerts for various severity levels
• Action Buttons: Quick access to common tasks and operations
• Search Functionality: Rapidly locate specific flights, gates, or resources

Personalization

Users can personalize their dashboard experience through several customization options:

• Layout Configuration: Arrange dashboard components
• Widget Selection: Add or remove information widgets
• Saved Views: Create and switch between role-specific layouts
• Color Themes: Choose between light and dark modes
• Notification Preferences: Configure alert thresholds and delivery methods

Flight Tracking

The Flight Tracking module is a core component of SANS.AI that provides comprehensive real-time monitoring and management of all flight operations. This sophisticated system integrates with existing flight data systems while adding powerful AI-driven analytics and visualization capabilities.

Flight Data Integration

SANS.AI connects with multiple flight data sources to provide a complete operational picture:

• ADS-B Data Feeds: Real-time aircraft positions and telemetry
• Airport Operational Database (AODB): Scheduled flight information
• Air Traffic Control Systems: Runway assignments and approach data
• Airline Systems: Gate assignments, aircraft details, and boarding status
• Weather Services: Current and forecasted conditions affecting flights

The system normalizes and correlates data from these disparate sources to create a single, unified view of flight operations.

Flight Monitoring Interface

The flight monitoring dashboard provides multiple views to accommodate different operational needs:

• Map View: Interactive spatial representation of flights and their status
• Timeline View: Chronological visualization of arrivals and departures
• List View: Tabular representation with detailed flight information
• Gate View: Gate-centric display showing occupancy and scheduling
• Runway View: Runway utilization and sequencing information

Flight Status Tracking

SANS.AI tracks flights through their complete lifecycle, from scheduling to completion, with detailed status information at each stage:

Anomaly Detection

The Flight Tracking module employs advanced AI algorithms to detect anomalies in flight operations:

• Deviation Detection: Identifying flights that deviate from expected patterns
• Conflict Prediction: Foreseeing potential conflicts in runway usage or gate assignments
• Fuel Issues: Detecting abnormal fuel consumption or low fuel situations
• Schedule Disruptions: Identifying cascading effects of delays or cancellations
• Unusual Flight Parameters: Flagging abnormal altitude, speed, or position data

When anomalies are detected, the system generates alerts with appropriate severity levels and recommended actions.

Operational Analytics

Beyond real-time tracking, the module provides operational analytics to help optimize flight operations:

• On-Time Performance: Analysis of departure and arrival punctuality
• Delay Patterns: Identification of recurring delay causes and trends
• Runway Utilization: Metrics on runway usage efficiency and capacity
• Gate Turnaround: Analysis of aircraft turnaround times by gate, airline, and aircraft type
• Weather Impact: Correlation between weather conditions and operational performance

Flight Search and Filtering

SANS.AI provides powerful search and filtering capabilities to quickly locate specific flights based on various criteria:

-- Example of advanced flight filtering syntax
FILTER flights WHERE
  origin = 'JFK' AND
  status = 'En Route' AND
  arrival_time BETWEEN '14:00' AND '16:00' AND
  aircraft_type IN ('B738', 'A320', 'E190')
ORDER BY arrival_time ASC

The system also supports natural language queries for users who prefer a more conversational interface:

Historical Data Access

The Flight Tracking module maintains a comprehensive history of all flight operations, allowing users to:

• Review Past Operations: Examine historical flight data for analysis or investigation
• Compare Periods: Analyze performance across different time periods
• Track Trends: Identify long-term patterns in operational performance
• Generate Reports: Create customized reports for management or regulatory purposes

Historical data can be accessed through the same interface used for real-time tracking, with additional filtering options for time periods and comparison features.

Predictive Analytics

The Predictive Analytics module is one of SANS.AI's most powerful features, leveraging advanced machine learning algorithms to anticipate operational challenges before they occur. This module transforms historical and real-time data into actionable forecasts that empower proactive decision-making.

Prediction Capabilities

SANS.AI provides predictive analytics across multiple operational domains:

• Delay Prediction: Forecasting flight delays based on multiple factors
• Congestion Forecasting: Predicting terminal and runway congestion
• Weather Impact Assessment: Anticipating operational impacts of forecasted weather
• Resource Needs: Projecting staffing and equipment requirements
• Passenger Flows: Forecasting passenger volume and movement patterns

Prediction Models

The system employs multiple AI and machine learning models optimized for different prediction tasks:

These models are continuously trained and updated using the latest operational data to ensure optimal predictive performance.

Prediction Accuracy and Confidence

All predictions include accuracy metrics and confidence levels to help operators assess reliability:

• Confidence Score: Percentage indicating prediction reliability
• Error Margins: Expected variation range for numerical predictions
• Contributing Factors: Key variables influencing the prediction
• Historical Accuracy: Performance metrics for similar past predictions

Visualization and Interpretation

Predictive insights are presented through intuitive visualizations designed for operational use:

• Heat Maps: Visual representation of predicted congestion areas
• Probability Distributions: Range of potential outcomes with likelihood
• Time-Series Forecasts: Projected trends over upcoming hours/days
• Decision Trees: Visual explanation of prediction logic for key decisions
• What-If Scenario Modeling: Interactive tools to test alternative actions

Actionable Recommendations

Beyond predictions, SANS.AI provides specific recommendations to address forecasted challenges:

• Staffing Adjustments: Personnel reallocation based on predicted demand
• Gate Reassignments: Optimized gate changes to prevent congestion
• Runway Configuration Changes: Proactive adjustment for weather conditions
• Resource Prioritization: Focus areas for equipment and staff resources
• Passenger Communication: Targeted messaging for potential disruptions

Custom Prediction Scenarios

Operators can create custom prediction scenarios to evaluate potential operational changes:

# Example of a custom prediction scenario configuration

scenario = Prediction.create_scenario({
    'name': 'Weather Impact Assessment',
    'base_conditions': {
        'time_range': ('2023-05-15T14:00:00', '2023-05-15T22:00:00'),
        'runways_active': ['18L', '18R', '17R'],
        'terminal_capacity': 'standard'
    },
    'variable_factors': {
        'weather': {
            'wind_direction': 180,  # degrees
            'wind_speed': 25,       # knots
            'visibility': 3,        # miles
            'precipitation': 0.2    # inches/hour
        }
    },
    'output_metrics': [
        'runway_throughput',
        'departure_delays',
        'arrival_delays',
        'gate_congestion'
    ]
})

results = scenario.run()
scenario.visualize()

Prediction API

For integration with external systems, SANS.AI provides a REST API to access predictions programmatically:

{
  "request": {
    "prediction_type": "delay_forecast",
    "parameters": {
      "flight_ids": ["UA123", "DL456", "AA789"],
      "prediction_window": "6h",
      "confidence_threshold": 70
    }
  },
  "response": {
    "predictions": [
      {
        "flight_id": "UA123",
        "scheduled_departure": "2023-05-15T15:30:00Z",
        "predicted_departure": "2023-05-15T16:15:00Z",
        "delay_minutes": 45,
        "confidence": 85,
        "contributing_factors": [
          {"factor": "weather", "weight": 0.6},
          {"factor": "inbound_aircraft_delay", "weight": 0.3},
          {"factor": "terminal_congestion", "weight": 0.1}
        ]
      },
      // Additional predictions...
    ],
    "timestamp": "2023-05-15T12:30:00Z"
  }
}

Model Training and Improvement

The predictive analytics system continuously improves through several mechanisms:

• Automated Retraining: Regular model updates incorporating new operational data
• Feedback Loop: Actual outcomes are compared to predictions to refine models
• Transfer Learning: Insights from similar airports are applied where relevant
• Feature Engineering: Ongoing development of new predictive variables
• Model Evaluation: Regular benchmarking of prediction performance

Model training statistics and performance metrics are available in the Analytics section of the dashboard.

Safety Features

SANS.AI incorporates comprehensive safety monitoring capabilities designed to enhance operational safety through automated detection, risk assessment, and proactive alerting. This module focuses on identifying and preventing potential safety incidents across all airport operations.

Safety Monitoring Systems

The safety module integrates multiple monitoring systems to provide full operational coverage:

• Runway Monitoring: Detection of runway incursions and traffic conflicts
• Aircraft Separation: Monitoring of proper aircraft spacing and separation
• Ground Movement: Tracking of vehicle and aircraft movements in ground areas
• Weather Safety: Assessment of weather-related safety risks
• Terminal Safety: Monitoring of passenger flow and terminal safety conditions

Runway Incursion Prevention

The runway monitoring system uses multiple data sources to detect and prevent runway incursions:

• Multi-sensor Fusion: Combining data from radar, ADS-B, and surface movement systems
• Pattern Recognition: Identifying unusual movement patterns that may indicate a risk
• Predictive Alerts: Warning of potential conflicts before they occur
• Taxiway Monitoring: Tracking aircraft and vehicle positions on taxiways adjacent to runways
• Runway Status Tracking: Real-time monitoring of runway occupancy and clearance status

When a potential runway incursion is detected, the system immediately generates high-priority alerts to relevant operators.

Aircraft Separation Monitoring

SANS.AI continuously monitors aircraft separation to identify potential conflicts:

Anomaly Detection System

A core component of the safety module is the anomaly detection system that identifies unusual patterns that may indicate safety concerns:

• Behavior Analysis: Identifying aircraft movements that deviate from normal patterns
• Communication Monitoring: Detecting missed or unusual communication patterns
• System Status Anomalies: Identifying unusual status indications from aircraft or ground systems
• Environmental Anomalies: Detecting unexpected weather patterns or environmental conditions
• Temporal Anomalies: Identifying unusual timing or sequencing in operations

The anomaly detection system employs multiple AI algorithms including isolation forests, autoencoders, and time-series anomaly detection to identify potential safety concerns.

Risk Assessment Framework

All detected safety concerns are evaluated through a comprehensive risk assessment framework:

# Example of the risk assessment calculation

def calculate_risk_score(event):
    # Base factors
    severity = determine_severity(event)  # 1-10 scale
    probability = determine_probability(event)  # 0.0-1.0
    
    # Contextual factors
    time_factor = 1.0
    if event.is_peak_hours():
        time_factor = 1.5
    
    weather_factor = 1.0
    if event.has_adverse_weather():
        weather_factor = 1.3
    
    visibility_factor = 1.0
    if event.visibility_below(3):  # miles
        visibility_factor = 1.2
    
    # Calculate composite risk score
    risk_score = (severity * probability) * time_factor * weather_factor * visibility_factor
    
    # Normalize to 0-100 scale
    normalized_score = min(100, risk_score * 10)
    
    return {
        'score': normalized_score,
        'severity': severity,
        'probability': probability,
        'contributing_factors': {
            'time': time_factor,
            'weather': weather_factor,
            'visibility': visibility_factor
        }
    }

Risk scores determine alert severity, notification routing, and recommended response actions.

Safety Alerts and Notifications

The safety module provides multi-level alerting based on the severity and urgency of detected issues:

• Critical Alerts: Immediate notification for high-risk situations requiring urgent action
• Warning Alerts: Notification of developing situations that may require intervention
• Advisory Notices: Information about conditions that warrant attention or monitoring
• System Alerts: Notification of safety monitoring system status or performance issues

Alerts can be delivered through multiple channels including dashboard notifications, mobile alerts, email, SMS, and integration with airport announcement systems.

Safety Analysis and Reporting

Beyond real-time monitoring, the safety module provides comprehensive analysis and reporting capabilities:

• Safety Trend Analysis: Identification of patterns in safety incidents and near-misses
• Risk Hotspot Mapping: Visual representation of high-risk areas and times
• Causal Factor Analysis: Identification of common contributing factors in safety events
• Compliance Reporting: Automated generation of safety reports for regulatory purposes
• Safety Performance Metrics: Tracking of key safety performance indicators over time

These analytical capabilities help airports identify systemic safety issues and implement targeted improvements to enhance overall safety performance.

LiDAR Integration

SANS.AI's LiDAR integration module leverages advanced light detection and ranging technology to provide comprehensive passenger flow analysis and terminal optimization capabilities. This cutting-edge feature transforms terminal operations through highly accurate, privacy-preserving monitoring of passenger movements.

LiDAR Hardware Requirements

The LiDAR integration requires specific hardware installations throughout terminal areas:

• LiDAR Sensors: High-resolution 3D LiDAR units (recommended models listed in appendix)
• Mounting Infrastructure: Ceiling or wall mounts at strategic coverage locations
• Edge Processing Units: Local processing hardware for preliminary data analysis
• Network Infrastructure: High-bandwidth, low-latency connectivity to the SANS.AI platform
• Power Supply: Reliable power distribution with backup capabilities

Our implementation team provides comprehensive site assessment and installation planning to optimize sensor placement and coverage.

Passenger Flow Monitoring

The LiDAR system provides comprehensive passenger flow insights:

• Real-time Density Tracking: Precise measurement of passenger density across terminal areas
• Movement Paths: Analysis of common movement patterns and flow dynamics
• Dwell Time Analysis: Measurement of time spent in different terminal zones
• Queue Monitoring: Real-time tracking of queue lengths and wait times
• Bottleneck Detection: Identification of congestion points and flow constraints

These insights are visualized through intuitive heatmaps, flow diagrams, and analytics dashboards designed for operational use.

Terminal Optimization

Based on LiDAR data, SANS.AI provides actionable insights for terminal optimization:

• Staff Allocation: Data-driven recommendations for personnel placement
• Facility Layout: Insights to optimize terminal configuration and signage
• Service Point Placement: Optimal locations for information desks, kiosks, and amenities
• Processing Capacity: Recommendations for security and check-in capacity adjustments
• Retail Insights: Foot traffic analysis for commercial area optimization

LiDAR Data Visualization

The LiDAR module provides multiple visualization options to represent passenger flow data:

• Density Heatmaps: Color-coded visualizations of passenger density
• Flow Vectors: Directional indicators showing movement patterns
• 3D Terminal Models: Three-dimensional representations with overlaid analytics
• Time-lapse Visualizations: Temporal analysis of changing flow patterns
• Predictive Visualizations: Forecasted passenger flows based on real-time data

Integration with Other Modules

The LiDAR module integrates with other SANS.AI components to provide enhanced functionality:

Data Management and Retention

SANS.AI handles LiDAR data according to configurable data management policies:

• Real-time Processing: Live data is processed in real-time for immediate insights
• Aggregated Storage: Historical data is stored in anonymized, aggregated format
• Retention Policies: Configurable data retention periods to meet organizational and regulatory requirements
• Access Controls: Role-based access to ensure appropriate data usage
• Privacy Safeguards: Technical measures to prevent individual identification

All data processing complies with relevant privacy regulations and industry best practices.

Performance Metrics

The LiDAR system provides key performance indicators to measure operational efficiency:

• Processing Throughput: Passengers processed per hour at key checkpoints
• Queue Wait Times: Average and maximum wait times by location and time
• Space Utilization: Passenger density relative to available space
• Flow Efficiency: Speed and smoothness of passenger movements through the terminal
• Service Point Performance: Utilization and efficiency of service locations

These metrics are tracked over time to identify trends and measure the impact of operational changes.

Resource Optimization

The Resource Optimization module helps airports maximize operational efficiency through intelligent allocation and management of gates, ground equipment, and personnel. This module uses advanced algorithms to optimize resource utilization while maintaining service levels and operational resilience.

Gate Management

SANS.AI provides sophisticated gate assignment and management capabilities:

• Intelligent Gate Assignment: Optimized allocation based on multiple factors
• Real-time Reassignment: Dynamic reallocation in response to operational changes
• Connection Optimization: Minimizing connection distances for passengers
• Utilization Balancing: Distributing usage to prevent congestion
• Aircraft Compatibility: Ensuring appropriate gate sizing and equipment

The gate optimization algorithm considers over 30 different factors to create an optimal assignment plan, including aircraft type, passenger connections, airline preferences, and historical performance.

Optimization Parameters

The resource optimization engine can be configured to prioritize different operational goals:

These optimization parameters can be adjusted dynamically to respond to changing operational priorities.

Ground Resource Management

Beyond gate assignments, SANS.AI optimizes the allocation and routing of ground resources:

• Ground Service Equipment: Optimized allocation and routing of GSE assets
• Baggage Handling: Efficient processing and routing of baggage
• Fueling Operations: Scheduling and optimization of aircraft fueling
• Catering Services: Coordination of food service operations
• Maintenance Resources: Allocation of maintenance equipment and personnel

The system tracks resource locations, availability, and utilization to ensure optimal allocation and reduce inefficiencies.

Personnel Optimization

SANS.AI provides data-driven insights for staff allocation and scheduling:

• Demand Forecasting: Prediction of staffing needs based on operational patterns
• Dynamic Allocation: Real-time adjustment of personnel deployment
• Skill Matching: Ensuring appropriate skill distribution across operational areas
• Work Balance: Equitable distribution of workload among teams
• Compliance Management: Adherence to work regulations and safety requirements

Optimization Algorithm

The resource optimization engine employs sophisticated algorithms including:

# Simplified example of optimization approach

def optimize_resources(flights, available_resources, constraints, optimization_goals):
    # Initialize optimization model
    model = OptimizationModel()
    
    # Define decision variables
    # For each flight and potential resource assignment
    assignments = {}
    for flight in flights:
        for resource in available_resources[flight.required_resource_type]:
            assignments[(flight.id, resource.id)] = model.add_binary_variable()
    
    # Add constraints
    # Each flight must be assigned exactly one resource
    for flight in flights:
        model.add_constraint(
            sum(assignments[(flight.id, r.id)] 
                for r in available_resources[flight.required_resource_type]) == 1
        )
    
    # Resources can't be double-booked
    for resource_type in available_resources:
        for resource in available_resources[resource_type]:
            for time_slot in time_slots:
                overlapping_flights = get_overlapping_flights(flights, time_slot)
                model.add_constraint(
                    sum(assignments[(f.id, resource.id)] for f in overlapping_flights) <= 1
                )
    
    # Add custom constraints
    for constraint in constraints:
        constraint.apply_to_model(model, assignments)
    
    # Define objective function based on weighted goals
    objective = 0
    for goal in optimization_goals:
        objective += goal.weight * goal.calculate_objective(flights, assignments)
    
    model.set_objective(objective, minimize=True)
    
    # Solve the optimization problem
    solution = model.solve()
    
    # Return optimized assignments
    return extract_assignments(solution, assignments)

The actual implementation uses advanced mixed-integer programming and constraint satisfaction techniques to efficiently solve complex resource allocation problems.

What-If Analysis

SANS.AI provides powerful what-if analysis capabilities for resource planning:

• Scenario Modeling: Testing alternative resource allocation strategies
• Disruption Simulation: Evaluating response to operational disruptions
• Capacity Planning: Analyzing the impact of infrastructure or resource changes
• Schedule Modifications: Assessing the impact of flight schedule changes
• Growth Projection: Planning for traffic growth scenarios

These capabilities help airports proactively plan for changes and develop robust resource strategies.

Integration with External Systems

The Resource Optimization module integrates with external systems to ensure coordinated operations:

• Airline Systems: Coordination with airline resource management platforms
• Ground Handler Systems: Integration with ground service provider systems
• A-CDM Platforms: Coordination with Airport Collaborative Decision Making systems
• FIDS/GIDS: Integration with Flight and Gate Information Display Systems
• Asset Management Systems: Coordination with equipment tracking systems

These integrations ensure that optimization decisions are coordinated across all stakeholders in the airport ecosystem.

Intelligent Notifications

The Intelligent Notifications module provides a sophisticated alerting system that delivers timely, relevant, and actionable information to airport stakeholders. This system employs AI-driven prioritization and context awareness to ensure that the right information reaches the right people at the right time.

Notification Types

SANS.AI generates various types of notifications based on operational conditions:

• Alerts: Time-sensitive information requiring immediate attention or action
• Warnings: Potential issues that may require monitoring or future action
• Updates: Important changes to operational status or resources
• Recommendations: AI-generated suggestions for operational improvements
• Informational: Context-providing information without immediate action requirements

Each notification type follows a specific format and prioritization protocol to ensure appropriate handling.

Context-Aware Delivery

The notification system intelligently determines notification delivery based on multiple factors:

• User Role: Matching notifications to relevant job responsibilities
• Current Activity: Awareness of user's active tasks and focus areas
• Location: Physical location within the airport (for mobile users)
• Device Type: Adapting notification format to the receiving device
• Priority Settings: User-configurable notification preferences

This context-awareness ensures that notifications are delivered in the most effective and least disruptive manner for each recipient.

Notification Delivery Channels

SANS.AI can deliver notifications through multiple channels:

The system can be configured to use multiple delivery channels for critical notifications to ensure receipt.

Intelligent Prioritization

SANS.AI employs a sophisticated prioritization system to manage notification importance:

// Example of notification prioritization logic

function calculateNotificationPriority(notification) {
  // Base score from notification type
  let baseScore = {
    'alert': 80,
    'warning': 60,
    'update': 40,
    'recommendation': 30,
    'informational': 20
  }[notification.type];
  
  // Adjust for operational impact
  let operationalMultiplier = 1.0;
  switch(notification.operationalImpact) {
    case 'critical':
      operationalMultiplier = 1.5;
      break;
    case 'significant':
      operationalMultiplier = 1.2;
      break;
    case 'moderate':
      operationalMultiplier = 1.0;
      break;
    case 'minor':
      operationalMultiplier = 0.8;
      break;
    case 'none':
      operationalMultiplier = 0.6;
      break;
  }
  
  // Time sensitivity factor
  let timeFactor = 1.0;
  const timeToAction = notification.timeToActionRequired; // in minutes
  if (timeToAction < 15) {
    timeFactor = 1.5;
  } else if (timeToAction < 60) {
    timeFactor = 1.2;
  } else if (timeToAction > 240) {
    timeFactor = 0.8;
  }
  
  // Calculate final priority
  let priorityScore = baseScore * operationalMultiplier * timeFactor;
  
  // Map to priority levels
  if (priorityScore >= 90) return 'critical';
  if (priorityScore >= 70) return 'high';
  if (priorityScore >= 50) return 'medium';
  if (priorityScore >= 30) return 'low';
  return 'informational';
}

This prioritization ensures that the most important notifications receive appropriate attention while preventing alert fatigue.

User Configuration

Users can configure notification preferences through the Notification Settings panel:

• Priority Thresholds: Minimum priority level for different delivery channels
• Working Hours: Specification of periods for receiving different notification types
• Focus Areas: Selection of operational areas for which notifications are relevant
• Delivery Preferences: Preferred channels for different notification types
• Notification Grouping: Settings for combining related notifications

These settings ensure that each user receives an appropriate and manageable flow of notifications.

Notification Content Generation

SANS.AI uses NLG (Natural Language Generation) techniques to create clear, concise notification content:

• Situation Summary: Concise explanation of the current condition
• Operational Impact: Description of how the situation affects operations
• Recommended Actions: Specific steps that should be taken
• Relevant Data: Key metrics and information related to the notification
• Time Sensitivity: Indication of when action is required

The system adjusts content detail based on the delivery channel, with more comprehensive information for dashboard alerts and email, and more concise content for SMS and mobile notifications.

Notification Management

SANS.AI provides tools for managing and tracking notifications:

• Notification Center: Central repository of all current and past notifications
• Acknowledgment Tracking: Monitoring of notification receipt and review
• Escalation Paths: Automatic escalation for unacknowledged critical notifications
• Notification Analytics: Tracking of notification volumes, response times, and effectiveness
• Notification History: Searchable archive of past notifications

These management tools ensure accountability and enable continuous improvement of the notification system.

API Reference

SANS.AI provides a comprehensive RESTful API that enables integration with external systems and development of custom applications. This section provides an overview of the API structure, authentication methods, and key endpoints.

API Authentication

SANS.AI API uses OAuth 2.0 for authentication and authorization. The following authentication flows are supported:

• Client Credentials: For server-to-server integration
• Authorization Code: For applications acting on behalf of users
• API Key Authentication: For simpler integrations with limited scope

Sample authentication request:

# Example OAuth 2.0 Client Credentials authentication

curl -X POST https://api.sans-ai.com/oauth/token \
  -H "Content-Type: application/x-www-form-urlencoded" \
  -d "grant_type=client_credentials&client_id=YOUR_CLIENT_ID&client_secret=YOUR_CLIENT_SECRET&scope=read:flights write:notifications"

API Structure

The API is organized into the following major resource categories:

Common Endpoints

The following are some commonly used API endpoints:

Flights

GET /api/v1/flights
GET /api/v1/flights/{flight_id}
GET /api/v1/flights/search?origin=JFK&status=delayed
GET /api/v1/flights/since?timestamp=2023-05-15T12:00:00Z

Gates

GET /api/v1/gates
GET /api/v1/gates/{gate_id}
GET /api/v1/gates/status
PUT /api/v1/gates/{gate_id}/assignment

Terminal

GET /api/v1/terminal/passenger-flow
GET /api/v1/terminal/congestion
GET /api/v1/terminal/lidar-heatmap
GET /api/v1/terminal/bottlenecks

Predictions

GET /api/v1/predictions/delays
GET /api/v1/predictions/congestion
POST /api/v1/predictions/what-if
GET /api/v1/predictions/anomalies

Request and Response Formats

All API requests and responses use JSON format. Here's an example response from the flights endpoint:

{
  "data": [
    {
      "id": "FL1234",
      "flight_number": "UA123",
      "origin": "JFK",
      "destination": "LAX",
      "scheduled_departure": "2023-05-15T14:30:00Z",
      "actual_departure": "2023-05-15T14:45:00Z",
      "scheduled_arrival": "2023-05-15T17:45:00Z",
      "estimated_arrival": "2023-05-15T18:00:00Z",
      "status": "en_route",
      "gate": "A12",
      "runway": "24L",
      "aircraft_type": "B738",
      "passengers": 162,
      "baggage_count": 143,
      "fuel": 78.4,
      "position": {
        "latitude": 39.8282,
        "longitude": -98.5795,
        "altitude": 35000,
        "heading": 270,
        "speed": 490
      },
      "links": {
        "self": "/api/v1/flights/FL1234",
        "airline": "/api/v1/airlines/UA",
        "origin_airport": "/api/v1/airports/JFK",
        "destination_airport": "/api/v1/airports/LAX"
      }
    },
    // Additional flight objects...
  ],
  "meta": {
    "count": 128,
    "page": 1,
    "per_page": 25,
    "pages": 6,
    "links": {
      "first": "/api/v1/flights?page=1&per_page=25",
      "last": "/api/v1/flights?page=6&per_page=25",
      "next": "/api/v1/flights?page=2&per_page=25",
      "prev": null
    }
  }
}

Rate Limiting

The API implements rate limiting to ensure system stability. Rate limits vary by endpoint and subscription tier:

Rate limit information is included in response headers:

X-RateLimit-Limit: 100
X-RateLimit-Remaining: 95
X-RateLimit-Reset: 1589547963

Error Handling

The API uses standard HTTP status codes and provides detailed error messages:

{
  "error": {
    "code": "resource_not_found",
    "message": "The requested flight FL9999 could not be found.",
    "details": "Flight with ID FL9999 does not exist in the system.",
    "status": 404,
    "request_id": "req_7f8g9h0j1k2l"
  }
}

API Clients and SDKs

SANS.AI provides official client libraries for popular programming languages:

• Python: pip install sansai-client
• JavaScript/Node.js: npm install sansai-client
• Java: Available via Maven Central (com.sansai:sansai-client)
• C#/.NET: Available via NuGet (SansAI.Client)
• Go: go get github.com/sansai/sansai-client-go

Example usage of the Python client:

from sansai.client import SansAIClient

# Initialize client
client = SansAIClient(
    client_id="YOUR_CLIENT_ID",
    client_secret="YOUR_CLIENT_SECRET"
)

# Get all active flights
active_flights = client.flights.list(status="active")

# Get terminal congestion prediction for next 3 hours
congestion = client.predictions.congestion(
    terminal="A",
    lookahead_hours=3
)

# Create a notification
client.notifications.create(
    type="alert",
    title="Gate Change Notice",
    message="Flight UA123 moved from Gate A12 to B15",
    severity="medium",
    recipients=["gate_ops", "terminal_a_staff"]
)

Frequently Asked Questions

This section addresses common questions about SANS.AI's functionality, implementation, and best practices.

Troubleshooting

This section provides solutions to common issues that may arise during the operation of SANS.AI.

System Connectivity Issues

If you experience connection problems between SANS.AI components or with external systems:

1. Verify network connectivity between all system components
2. Check that all required ports are open in your network firewall
3. Ensure API credentials and authentication tokens are valid and not expired
4. Review system logs for specific connection error messages
5. Verify that all integration endpoints are accessible and responding

Data Synchronization Issues

If you notice data inconsistencies or synchronization problems:

1. Check the status of all data integrations in the System Admin dashboard
2. Verify that source systems are providing accurate and timely data
3. Review data transformation rules for any potential conflicts
4. Check for schema changes in source systems that may affect data mapping
5. Ensure sufficient database resources are available for data processing

Performance Optimization

If you experience system slowdowns or performance issues:

1. Check system resource utilization (CPU, memory, disk I/O)
2. Review database query performance metrics
3. Ensure caching mechanisms are functioning correctly
4. Verify that data retention policies are properly implemented
5. Consider scaling resources if consistently high utilization is observed

LiDAR Calibration Issues

If LiDAR passenger flow analysis shows inaccurate results:

1. Run the LiDAR calibration utility from the maintenance dashboard
2. Verify that LiDAR sensors are properly positioned and not obstructed
3. Check that the terminal layout map matches the current physical layout
4. Ensure all LiDAR sensors have current firmware updates
5. Validate that sensor fusion settings are correctly configured

Contacting Support

If you cannot resolve an issue using the troubleshooting steps above, please contact our support team:

• Standard support: Monday-Friday, 8am-8pm ET via email at contact@kquika.com
• Premium support: 24/7 support via dedicated hotline at +1-888-SANS-AIR
• Emergency support: Available for critical operational issues at emergency@kquika.com

When contacting support, please provide the following information:

• Your system ID and environment (Production, Staging, etc.)
• Detailed description of the issue, including steps to reproduce
• Relevant error messages or screenshots
• System logs from the time of the incident
• Any troubleshooting steps already attempted