Smart Hangar Management System Data Acquisition System
Keywords: Smart Hangar, Smart Management, Smart Warehouse, IoT Platform, IoT Dispatch, Converged Communication, IoT Data Acquisition
I IoT Data Acquisition Service
1.1 System Functions
This system is primarily based on IoT technology and is divided into the on-site infrastructure layer, basic support layer, business management layer, and business application layer.
The infrastructure layer includes multiple types of monitoring equipment such as environmental monitoring, fire monitoring, and water/electricity/gas monitoring. It adopts the Raytrans IDM IOT3500 2.5G IoT integrated service optical transmission equipment to collect and forward data from various sensing devices in real time. It also includes the network communication layer, which is mainly responsible for data transmission through IP networks based on the MQTT IoT protocol, including Lora networks, WiFi networks, Ethernet LANs, etc., to achieve network access and transmission.
The business management layer mainly consists of the IoT data acquisition service platform. The basic database is responsible for real-time storage and query of massive data, with extremely high storage and query performance. The IoT data acquisition service platform is responsible for real-time data display, event alarms, data monitoring, emergency response, and system integration. Various management application systems can be developed on this platform, such as environmental monitoring systems, remote intelligent water/electricity/gas monitoring and management systems, fire monitoring and early warning systems, and integration with GIS systems and information publishing platforms.
The business application layer mainly involves specific applications, such as online monitoring systems for power/fuel supply devices, environmental monitoring systems, remote intelligent water/electricity/gas monitoring and management systems, fire monitoring and early warning systems, and GIS visual big data centers.
Through the organic combination of the above four layers, automatic sampling and online monitoring of the hangar sensing layer can be achieved; hangar IoT monitoring data can be grasped and automatically transmitted to the system platform layer; the platform layer performs data aggregation, sorting, and comprehensive analysis; monitoring information is transmitted to the management application layer; the management application layer provides visual data monitoring display and data analysis functions.
It mainly provides capabilities such as device access, device management, and rule engine.
1.1.1 Device Access
The IoT data acquisition service software supports massive device connections to the cloud, achieving stable and reliable two-way communication between devices and the IoT data acquisition service software.
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Function
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Description
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Device Access
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Provides cross-platform porting guidance, supports device access based on multiple platforms, and provides device-side SDKs, drivers, etc., to help different devices and gateways connect easily.
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Message Communication
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Provides product and device message communication, facilitating management of communication between devices and the server side, and simplifying authorization operations.
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Using MQTT Protocol
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Provides device-side SDKs for multiple protocols, meeting both the real-time requirements of long connections and the low-power consumption requirements of short connections.
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Cloud-to-Cloud Integration
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Provides cloud-to-cloud integration SDKs to quickly build bridging services and establish two-way data channels between devices and the IoT data acquisition service software.
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1.1.2 Message Communication
The IoT data acquisition service software supports configuring rules through the following functions to achieve synchronization, transformation, filtering, and storage of communication messages between devices, servers, and the IoT data acquisition service software.
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Function
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Description
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Server-side Subscription
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Subscribe to one or multiple types of messages from all devices under a product. The server side can obtain subscribed messages through AMQP clients or Message Service (MNS) clients.
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Data Flow
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The IoT data acquisition service software flows the specified fields of specified messages to the destination according to the data flow rules configured by you, for storage and computing processing.
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Scene Linkage
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Configure simple rules to seamlessly flow device data to other devices, achieving device linkage.
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RRPC Communication
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Provides two communication modes, RRPC and PUB/SUB, to meet your needs in different scenarios. Among them, PUB/SUB is message routing based on Topics.
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Broadcast Communication
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Supports broadcast communication, i.e., sending messages to all devices under a specified product (devices do not need to subscribe to the broadcast Topic), or to all devices that have subscribed to a specified Topic. Devices that are online can receive broadcast messages sent by the server.
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1.1.3 Device Management
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Function
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Description
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Thing Model
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Provides device thing models to simplify device application development.
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Digital Twin
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Dynamically presents business models of the physical world in digital form, performing real-time collection, computation analysis, monitoring, and statistics of physical world entity information.
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Data Parsing
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Supports transparent transmission of data in binary format to your own server without saving device data, thereby ensuring data security and controllability.
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Tags
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Achieves classification and cross-product device management.
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Device Grouping
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Provides device grouping functionality, allowing cross-product device management through device groups.
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Advanced Search
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Quickly search for devices meeting specified conditions through SQL-like statements.
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Device Tasks
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Simultaneously initiate property settings, asynchronous service calls, and custom tasks to multiple devices.
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Provides a device shadow caching mechanism that decouples devices from applications, resolving the pain point of unreliable communication over unstable wireless networks.
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Provides the capability to store, download, and delete device files.
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Solves the problem of resource-constrained embedded devices being unable to obtain server time in real time.
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Implements management of sub-devices, the topological relationship between sub-devices and the gateway, and monitoring, operation, and maintenance of sub-devices.
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Implements cross-region, cross-instance, and cross-account device distribution, reducing device onboarding costs.
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Time Series Data Storage Management
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Provides in-instance data storage capability, facilitating user storage and real-time access to massive device data.
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1.1.4 Monitoring, Operation, and Maintenance
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Function
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Description
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Performs real-time monitoring and CloudMonitor alerting on metrics related to devices, messages, thing models, and rule engine.
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Displays a geographic heat map distribution of devices, allowing you to intuitively and quickly understand the status of all devices.
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Directly sends commands from the IoT data collection service software console to devices to debug device-side functions.
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Simulates real devices establishing connections with the IoT data collection service software, uses simulated data to test the communication functions between the IoT data collection service software and the device side, and locates problems.
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Displays server-side running logs and device local logs, helping you locate problems and perform fault analysis.
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Empowers devices with remote upgrade capability.
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Remotely updates device system parameters, network parameters, and other configuration information online.
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Processes alert information triggered by scene linkage rules in the rule engine.
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Enables remote connection to IoT data collection service software devices behind firewalls by establishing a WebSocket channel between the access end and the device end.
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1.1.5 Data Governance
Provides a metadata-driven one-stop data governance platform, realizing unified management of全域数据 (all-domain data), intelligent extraction of data standards, full lifecycle management of data from collection and access to application and request, dynamic tracking of data lineage, precise control of data security, and circulation and management of data assets, helping hangar management to develop, manage, share, and use data in a comprehensive, automated, and intelligent manner.
1.1.6 Big Data Visualization
(1). Multi-Type Data Source Configuration
Supports data modeling for offline computing; supports direct data pulling from data warehouses and data storage; uses SQL for offline data computation, with data automatically imported into intermediate tables.
(2). Data Exploration
Allows users to directly connect to their own data sources; supports multiple database types such as Generic SQL, Palo, ElasticSearch, and InfluxDB; uses interactive Notebooks for data exploration.
(3). Rich Chart Presentation
Supports basic charts as well as multiple chart types such as funnel charts, Sankey diagrams, box plots, pivot tables, and maps; flexible chart style configuration; time window linkage with a unified time region control that automatically pulls the latest data for display every day.
1.2 Hangar IoT Monitoring System Basic Database
Performs digital transformation of basic elements such as hangar sensing terminals, access terminals, IoT gateways, and monitoring points, establishing a hangar IoT monitoring basic big database to achieve standardized and unified hangar management data.
The hangar IoT monitoring basic big database is the data foundation for smart hangar management, including comprehensive data on the hangar environment, automatic control equipment, security, access control, refueling, and power supply. Basic data can be classified by data source into automatically collected data, manually entered data, and data obtained through integration with other systems. Automatically collected data refers to data obtained by the IoT data collection service platform through network integration with various IoT terminals, intelligent sensing systems, sensors, video surveillance systems, and other systems. The basic database is not static but is continuously developed and iterated. Manual input to the basic database is maintained and updated in real time by hangar staff through the platform, mobile APP, and other means to ensure data accuracy.
(1) People information: including basic information and extended information, hangar management and staff information (name, ID type and ID number, organization name and address, location, position (including family members), political affiliation, etc.);
(2) Location: establishment of a 2D, 2.5D, or 3D geographic information system map of the hangar;
(3) Events: referring to events related to hangar management, including infrastructure maintenance, emergency response incidents, personnel and vehicle dispatch events, etc.;
(4) Objects: various components of the hangar, including equipment, facilities, items, vehicles, etc.;
(5) Organization: referring to the hangar management organizational structure, teams, etc.
The hangar basic big database transforms disordered and complex data into ordered, highly summarized data. Based on the analysis models and prediction models set for the hangar, the IoT data collection service platform implements functions such as collection, update, processing, analysis, prediction, and decision-making for the hangar basic big database.