Power-aware smart navigational aid operating system and operating method thereof

Abstract

An operating method of a power-aware smart navigational aid operating system is disclosed. The operating method is an operating method of a power-aware smart navigational aid operating system including service blocks implemented through a microservice architecture and independently operated in a container environment. The operating method comprises the steps in which: an operating module determines an operating mode of the power-aware smart navigational aid operating system according to a power supply state; and the operating module selects service blocks to be controlled from among the service blocks in the determined operating mode.

Description

Power-sensitive smart navigational aid operating system and method of operating the same

[0001] The present invention relates to a microservices architecture for effectively managing multiple services within a smart navigational aid and the operation thereof.

[0002] Modern Aids to Navigation (ATONs) operate by integrating complex hardware modules (sensors, power supplies, communication devices, etc.). An Open Source Software (OSS)-based operational framework is essential for the efficient management of such systems. However, existing OSS projects can suffer from degraded system performance and stability due to issues such as memory leaks and system shutdowns. These problems can be particularly significant in Smart Aids to Navigation, which require high availability; furthermore, given the nature of ATONs that rely solely on solar power to maintain their energy supply, power management is a critical issue.

[0003] The objective of the present invention to solve the aforementioned problems is to provide a power-aware operational framework that includes predictive maintenance and power management functions utilizing Microservices Architecture (MSA) and Machine Learning (ML).

[0004] The main difference from existing technologies is that it provides a more efficient and reliable power management strategy capable of addressing power supply variability and system reliability issues.

[0005] The power-aware operational framework of the present invention considers power supply variability and system stability by introducing a microservices architecture in which each service can operate independently, and optimizes energy consumption by dynamically adjusting the system's operating mode according to power conditions. Furthermore, it ensures continuous system operation by enabling the prediction and response to hardware failures in advance through machine learning-based predictive maintenance functions.

[0006] A method for operating a power-aware smart navigational aid operating system according to one aspect of the present invention for achieving the above-mentioned purpose, comprising service blocks implemented through a microservices architecture and said service blocks that operate independently in a container environment, wherein the method comprises: a step in which an operating module determines an operating mode of the power-aware smart navigational aid operating system according to a power supply status; and a step in which the operating module selects service blocks to be controlled among said service blocks in the determined operating mode.

[0007] A power-aware smart navigational aid operating system according to another aspect of the present invention is a power-aware smart navigational aid operating system comprising service blocks implemented through a microservices architecture and said service blocks that operate independently in a container environment, the power-aware smart navigational aid operating system comprising: a processor; and an operating module that operates independently in a container environment executed by said processor, wherein the operating module determines an operating mode of the power-aware smart navigational aid operating system according to a power supply state and selects service blocks to be controlled among said service blocks in said operating mode.

[0008] According to the present invention, in terms of power management optimization, there is an advantage of adjusting the performance mode of the system according to the power supply situation through a dynamic power management strategy.

[0009] In terms of enhancing system stability, since each service operates independently in a container environment, there is an advantage in that the entire system is not affected by issues such as memory leaks.

[0010] In terms of increasing operational efficiency, predictive maintenance functions utilizing machine learning can detect and respond to hardware failures in advance, offering the advantage of minimizing system downtime.

[0011] FIG. 1 is a diagram showing a microservices architecture for a power-aware smart navigational aid operating system according to an embodiment of the present invention.

[0012] FIG. 2 is an example of service operation in high-performance mode in a microservices architecture for a power-aware smart navigational aid operating system according to an embodiment of the present invention.

[0013] FIG. 3 is an example of service operation in low-performance mode in a microservices architecture for a power-aware smart navigational aid operating system according to an embodiment of the present invention.

[0014] FIG. 4 is a flowchart illustrating the operation method of a power-aware smart navigational aid operating system according to an embodiment of the present invention.

[0015] Figure 5 is an exemplary configuration diagram of a computing device that performs the method of Figure 4.

[0016] The terms used in this specification are used merely to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as “comprising” or “having” are intended to indicate the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0017]

[0018] FIG. 1 is a diagram showing the microservices architecture of a power-aware smart navigational aid operating system according to an embodiment of the present invention.

[0019] Referring to FIG. 1, the power-aware smart navigational aid operation framework according to an embodiment of the present invention has several functional modules implemented through a microservices architecture (MSA).

[0020] Each function of the Microservices Architecture (MSA) consists of independent container-based service blocks (10–17), which run in a lightweight container environment. This container-based architecture is designed to enable independent deployment, scaling, and restarting of individual services, ensuring that the entire system is not affected by issues such as memory leaks.

[0021] A container-based sensor data collection service block (10) collects data from various sensors in real time and uses this data to evaluate the status of hardware and establish a power management strategy.

[0022] A container-based local database service block (11) stores collected sensor data locally, for example, using MongoDB. The database is containerized and managed independently, increasing the efficiency of data access and processing. MongoDB is a NoSQL (non-relational) database and is a document-oriented database optimized for storing and managing large-scale data. MongoDB stores data in BSON (Binary JSON) documents similar to JSON, rather than in tables and rows, which allows for the flexible storage of various data structures.

[0023] A container-based remote transmission service block (12) transmits sensor data remotely using, for example, the MQTT (Message Queuing Telemetry Transport) protocol. This enables stable and efficient transmission of data and minimizes data loss. Here, MQTT is a lightweight message transmission protocol. MQTT provides an efficient method for communication between remote devices and is a protocol designed to operate reliably even in a network environment with limited bandwidth.

[0024] A container-based fault diagnosis service block (13) uses a machine learning (ML) algorithm to predict and diagnose hardware failures in advance. This prediction model learns from past data to analyze current data, detects abnormal hardware conditions, sends a warning to an administrator, or automatically resolves the problem.

[0025] The container-based computing resource monitoring / diagnostic service block (14) uses software programs such as Prometheus and Grafana to monitor the status of the system in real time and diagnose resource usage. It detects potential problems in the system in advance and sends notifications to administrators or responds automatically based on this. Prometheus and Grafana are open-source monitoring and visualization tools that provide a solution for monitoring and analyzing the performance and status of the system in real time when used together. Prometheus is, for example, an open-source system for monitoring and alerting, and is primarily used to collect and store time-series data (data that changes over time). To collect time-series data, Prometheus stores time-series data collected from various sources, such as servers, applications, and databases. Time-series data can be collected in the form of metrics, for example. To support various data sources, Prometheus uses a pull model that collects data via HTTP requests, for example, and can automate data collection by installing Exporters on multiple monitoring targets. For example, server resources, application performance, and database status are monitored in real time through Exporters. For alert systems, Prometheus can trigger alerts when system status exceeds a specific threshold by setting alerting rules, for instance. Alerts can be sent via various channels, such as email and Slack. Prometheus provides a unique query language called PromQL (Prometheus Query Language), which allows for the analysis of time-series data and the establishment of complex monitoring rules based on conditions.Grafana is an open-source dashboard and visualization tool for visualizing and analyzing data, capable of loading and visualizing data from various sources such as Prometheus. To support diverse visualizations, Grafana provides various visualization options—such as graphs, charts, gauges, and bars—that show data changes over time, making it easy to understand monitored data. For dashboard creation, Grafana allows users to build customizable dashboards to monitor multiple metrics in real time. Dashboards can be easily configured using a drag-and-drop method, making them user-friendly. For integration with various data sources, Grafana can integrate with multiple sources—such as ElasticSearch, InfluxDB, and MySQL—in addition to Prometheus, enabling the management of data from heterogeneous databases on a single screen. For alert and notification integration, Grafana allows users to set custom alerts and send real-time notifications via channels such as email, Slack, and webhooks. Collaboration between Grafana and Prometheus is possible in such a way that Prometheus focuses on collecting and storing data, while Grafana visualizes the data collected by Prometheus and provides it to the user. In this way, the computing resource monitoring / diagnosis service block (14) can effectively implement real-time system status monitoring, resource usage diagnosis, pre-problem detection, and warning functions through the collaboration of a monitoring tool (monitoring software program) such as Prometheus and a visualization tool (visual software program) such as Grafana.

[0026] The MSA operation module (17) operates to control the activation and deactivation of service blocks (10-14) according to a predicted SoC-based policy based on climate and SoC (State of Charge) prediction data received from the climate and SoC prediction service block (16).

[0027] A lightweight virtual operating system (20–26) existing for each container-based service block is an operating system with a lighter structure than a traditional virtual machine (VM) and executes a corresponding container-based service block independently. An example of such a lightweight virtual operating system is the Linux container (LXC).

[0028] A lightweight virtual operating system manager (30) is a tool for managing multiple lightweight virtual operating systems. The lightweight virtual operating system manager (30) automatically manages and coordinates multiple containers to support efficient resource usage and can dynamically adjust the number of containers as needed.

[0029] The main operating system (40) is an operating system that provides a user-friendly interface and various packages, and, for example, can be Ubuntu, one of the Linux distributions.

[0030] The smart navigational aid platform (50) may be hardware that collects and processes data, including a storage device, a communication module, and a processor (CPU, NPU, and GPU, etc.).

[0031]

[0032] FIG. 2 is an example of service operation in high-performance mode in a microservices architecture for a power-aware smart navigational aid operation system according to an embodiment of the present invention, and FIG. 3 is an example of service operation in low-performance mode in a microservices architecture for a power-aware smart navigational aid operation system according to an embodiment of the present invention.

[0033] Referring to FIGS. 2 and 3, in the operation of a Smart Aid to Navigation for power-aware dynamic management, the power supply relies primarily on solar energy, so the power situation can be highly variable. Accordingly, an embodiment of the present invention provides a function that can dynamically adjust the operating mode of the system according to the power supply status (e.g., the power supply status of solar power). The operating mode according to the embodiment includes a high-performance mode, a medium-performance mode, and a low-power mode. In the high-performance mode, as shown in FIG. 2, for example, a sensor data collection service block (10) can collect 12 types of sensor data using 12 types of sensors, and a remote transmission service block (12) can provide 3 types of remote transmission services. In the low-power mode, as shown in FIG. 3, for example, a sensor data collection service block (10) can provide 4 types of sensor data collection services using 4 types of sensors, and a remote transmission service block (12) can provide 1 type of remote transmission service.

[0034] In high-performance mode, the system operates all service blocks at maximum performance when sufficient solar power is predicted. In this mode, all sensors are activated, and data collection and transmission occur in real time. Although this mode consumes more energy, it focuses on collecting and analyzing accurate data by fully utilizing the system's performance.

[0035] In Medium Performance Mode, when solar power is uncertain, the system keeps only essential service block(s) active while deactivating non-essential service block(s). This mode ensures that critical services continue to operate while reducing energy consumption. For example, core sensor data is still collected, but non-essential services or additional analytical tasks are temporarily suspended (deactivated).

[0036] In low-power mode, when solar power is insufficient, the system switches to a low-power mode that consumes minimal energy. In this mode, only service block(s) related to core sensors and communication modules are activated, while all other service blocks are deactivated. In this way, the system minimizes energy consumption to extend battery life and maintains only basic navigation assistance functions. Here, core sensors are essential for monitoring the surrounding environment and conditions to ensure the navigational aid functions safely and effectively; examples include GPS sensors, environmental sensors (weather sensors, illuminance sensors, wind speed and direction sensors), wave and current sensors, power monitoring sensors, radar reflectors, and collision avoidance sensors. Examples of service block(s) related to these core sensors include container-based sensor data collection service blocks (10), container-based climate and SoC prediction service blocks (16), and container-based power control modules (15). Additionally, examples of service block(s) related to communication modules include remote transmission service blocks (12).

[0037]

[0038] Below, we will describe the selection and policy of the service blocks to be controlled in each dynamically adjusted operating mode.

[0039] The present invention proposes a policy for dynamically selecting service block(s) to be controlled based on the importance and resource usage of each service block during system operation. As part of a power management strategy, the system controls each service by comprehensively considering the service importance set by the user and the usage of hardware (HW) resources (CPU, GPU usage, and power consumption of related hardware).

[0040] In an embodiment, the system can set the importance of each service block(s), and high-importance services are maintained even in the event of a solar power shortage. Among service block(s) with the same or no importance set, priority is determined based on resource usage.

[0041] In an embodiment, regarding hardware (HW) resource usage monitoring, the CPU and GPU occupancy rates of each service block and the power consumption of the related hardware are monitored in real time, and service blocks with high resource usage may be subject to deactivation when there is a power shortage.

[0042] In the embodiment, regarding the selection of services to be controlled, parameters for determining the service control priority may include the importance of each service block, hardware resource usage (CPU, GPU occupancy), and the power consumption of the corresponding service block(s). By comprehensively evaluating these parameters, the service blocks to be disabled are determined according to the power situation.

[0043] Table 1 below is an exemplary table showing the calculated control order of services.

[0044] Service Block Importance HW Power CPU / GPU Usage Control Rank S1 1 70 30 10 S2 28 0 30 9 S3 3 100 40 7 S4 39 0 20 8 S5 3 100 50 6 S6 4 ​​90 40 5 S7 4 100 30 4 S8--6 0 2 S9--4 0 3 S10-100 50 1

[0045] The factors for evaluating the priority of a service are as follows: 1. Importance (I) of service block(s): Represents the importance of each service set by the user. The importance can be set as an integer value from 1 to 5, where 1 represents the lowest importance and 5 represents the highest importance.

[0046] 2. Hardware (HW) Resource Usage (R): The HW resource usage of each service can be calculated as the weighted sum of CPU share () and GPU share (). For example, it can be defined as shown in Equation 1 below, reflecting the relative importance of CPU and GPU shares. Here, α and β are the weights of CPU and GPU shares.

[0047]

[0048] 3. Power Consumption (P): Refers to the power consumed by each service. This value can be measured as absolute power consumption (in watts).

[0049] 4. Service Priority Score (S): The priority score of service i is calculated as a weighted sum of the importance, HW resource usage, and power consumption of each service block(s). This can be expressed by Equation 2 below.

[0050]

[0051] γ is the weight of service importance, δ is the weight of hardware (HW) resource usage, and ε is the weight of power consumption.

[0052] 5. Service Ranking: Calculate the S value for each service, then sort the services based on this value to determine the control priority. The higher the S value, the more likely the service is to be maintained, and the lower the value, the higher the priority for deactivation in case of power shortage.

[0053] Regarding fault detection and response, an embodiment of the present invention enhances the reliability of Smart ATONs by integrating a machine learning-based fault detection and prediction system. This system analyzes historical operational data to identify signs of hardware failure and predicts failures in advance based on this analysis. When a failure is predicted, the system immediately initiates a response process. For example, it automatically restarts the container where the problem occurred or, if the problem cannot be resolved, restores it to a previous stable state. Furthermore, it supports rapid response by transmitting detailed information about the failure to the administrator in real time.

[0054] Regarding the scalability and flexibility of the system, embodiments of the present invention are designed with system scalability in mind. New sensors or service modules can be easily added, and existing modules can be expanded or modified into different containers. Furthermore, the flexibility of the system is maximized to adapt to various environmental conditions. For example, the system's operation can be adjusted according to various climate conditions, and a flexible power management strategy capable of responding to various power supply scenarios is provided.

[0055] Thus, the present invention provides a power-aware operational framework for maximizing efficiency and reliability in the operation of smart ATONs. This framework addresses the major problems faced by modern smart marine navigation aids and enables stable operation in various environments.

[0056]

[0057] FIG. 4 is a flowchart illustrating the operation method of a power-aware smart navigational aid operating system according to an embodiment of the present invention.

[0058] Referring to FIG. 4, the power-aware smart navigational aid operating system according to an embodiment of the present invention includes service blocks (10 to 17) implemented through a microservices architecture, and the containerized service blocks that operate independently in a container environment. The method of operating such an operating system largely includes the step (S410) in which an operating module determines the operating mode of the power-aware smart navigational aid operating system according to the power supply status, and the step (S420) in which the operating module selects service blocks to be controlled among the service blocks in the determined operating mode.

[0059] In the embodiment, the operating mode determined in step (S410) includes a high-performance mode in which all service blocks (10 to 17) are operated at maximum performance in an environment where sufficient solar power is predicted, a medium-performance mode in which essential service blocks among the service blocks (10 to 17) are activated and non-essential service blocks are deactivated in an environment where solar power is uncertain, and a low-power mode in which only service blocks related to core sensors and communication modules among the service blocks are activated in an environment where solar power is insufficient. Here, whether solar power is sufficient, uncertain, or insufficient can be determined by the operating module (17) through, for example, prediction data provided by the climate and SoC prediction service block (16) among the service blocks, that is, data predicting the climate state and SoC state.

[0060] In an embodiment, the step of selecting the service blocks to be controlled (S420) includes selecting the service blocks to be controlled based on the importance of each service block and the resource usage of each service block. In this case, if the importance is the same, the service blocks to be controlled may be selected according to the priority determined by the resource usage.

[0061] In the embodiment, the resource usage of each service block may include CPU, GPU occupancy, and power consumption of related hardware.

[0062] In an embodiment, the step of selecting the service blocks to be controlled (S420) may be to select the service blocks to be controlled based on parameters including the importance of each service block, resource usage, and power consumption of each service block.

[0063] In an embodiment, the step of selecting the service blocks to be controlled (S420) involves selecting the service blocks to be controlled according to a service priority score, and the service priority score may be calculated as a weighted sum of the importance, resource usage, and power consumption of each service block.

[0064]

[0065] FIG. 5 is an exemplary configuration diagram of a computing device for performing the method of FIG. 4.

[0066] Referring to FIG. 5, the computing device (500) is the entity that performs each step in the method of FIG. 4, whereby the service blocks implemented through the microservices architecture are the entity. That is, the computing device (500) controls, executes, and / or manages the service blocks (10–17) illustrated in FIG. 1 to 3, the lightweight virtual operating system (20–26), the lightweight virtual operating system manager (30), the main operating system (40), and the smart navigational aid integration platform (50).

[0067] To this end, the computing device (500) includes a processor (510), memory (520), input / output device (530), power supply device (540), communication device (550), storage device (560), and a system bus (570) connecting the above (510 to 560).

[0068] The processor (510) is a component that serves as the core of the computing device and interprets and executes given commands. In the case of a smart navigational aid operating system, it performs roles such as collecting sensor data, processing data, and executing operating algorithms, and provides the performance required for real-time control and data processing. Additionally, the processor (510) controls, executes, and manages the operation of the components (10–17, 20–26, 30, 40, and 50) illustrated in FIGS. 1 to 3.

[0069] Memory (520) is a device that temporarily stores data required when a processor processes a task. In a smart navigational aid system, memory serves to quickly access and store data and provides a temporary space for processing sensor data, logging information, etc. Memory (520) includes volatile memory and / or non-volatile memory.

[0070] The input / output device (530) serves as an interface with a user or an external system. For example, a navigational aid operator can check the status of the system or input necessary control commands through this input / output device. Additionally, it provides an interface with various sensors or actuators to collect environmental information and output necessary control commands. The input device includes a keyboard, a touchscreen, etc. The output device includes a speaker, a display device, etc.

[0071] The power supply unit (540) is a device that supplies power to a computing device and includes a battery charged by solar power and a battery management system. In a smart navigational aid operation system, an efficient and stable power supply is important, and long-term operation is supported through a battery backup system or power supply utilizing renewable energy as needed.

[0072] The communication device (550) serves to transmit and receive data through a connection with an external network. In the case of a smart navigational aid system, it can transmit status data or receive remote control commands by communicating with a central management system. Generally, technologies such as wireless communication and satellite communication are used.

[0073] The storage device (560) is a device used to store long-term data. It stores the operational history, status information, event logs, etc. of the navigational aid so that they can be used for future analysis and problem solving.

[0074] The system bus (570) is a communication path that connects all components, such as the processor (510), memory (520), input / output device (530), and communication device (550). Through this, data is exchanged between the components, enabling the smart navigational aid system to operate smoothly.

[0075]

[0076] The embodiments disclosed in this specification should be considered in an exemplary sense for the sake of illustration rather than in a limiting sense. The scope of the invention is defined by the claims, not by the foregoing description, and all variations within the scope of the claims should be interpreted as being included in the invention.

[0077] The present invention has industrial applicability in the field of power-aware operating frameworks.

Claims

1. A method of operating a power-aware smart navigational aid operating system comprising service blocks implemented through a microservices architecture and operating independently in a container environment, wherein The operating module determines the operating mode of the power-aware smart navigational aid operating system according to the power supply status; and The above-determined operation module selects service blocks to be controlled among the service blocks in the above-determined operation mode. A method of operating a power-cognitive smart navigational aid operating system including 2. In Paragraph 1, The above operating mode is, A high-performance mode that operates all of the above service blocks at maximum performance in an environment where sufficient solar power is predicted; An intermediate performance mode that, in an environment where solar power is uncertain, activates essential service blocks among the above service blocks and operates non-essential service blocks in a deactivated state; and In a low-power mode where only the service blocks related to core sensors and communication modules among the above service blocks are activated in an environment with insufficient solar power. A method of operating a power-cognitive smart navigational aid operating system including 3. In Paragraph 1, The step of selecting service blocks to be controlled from among the above service blocks is, A method for operating a power-aware smart navigational aid operating system, comprising the step of selecting the service blocks to be controlled based on the importance of each of the service blocks and the resource usage of each service block.

4. In Paragraph 3, The step of selecting service blocks to be controlled from among the above service blocks is, A method for operating a power-aware smart navigational aid operating system, comprising the step of selecting the service blocks to be controlled according to the priority determined by the resource usage when the importance is the same.

5. In Paragraph 3, A method of operating a power-aware smart navigational aid operating system in which the resource usage of each service block includes CPU, GPU occupancy and power consumption of related hardware.

6. In Paragraph 1, The step of selecting service blocks to be controlled from among the above service blocks is, A method for operating a power-aware smart navigational aid operating system, comprising the step of selecting the service blocks to be controlled based on parameters including the importance of each service block, resource usage, and power consumption of each service block.

7. In Paragraph 1, The step of selecting service blocks to be controlled from among the above service blocks is, The above-mentioned control target service blocks are selected according to the service priority score, and A method of operating a power-aware smart navigational aid operating system in which the above service priority score is calculated as a weighted sum of the importance, resource usage, and power consumption of each service block.

8. In a power-aware smart navigational aid operating system comprising service blocks implemented through a microservices architecture and operating independently in a container environment, processor; and It includes an operation module that operates independently in a container environment executed by the above processor, and A power-aware smart navigational aid operating system characterized by the above-described operating module determining the operating mode of the power-aware smart navigational aid operating system according to the power supply status, and selecting service blocks to be controlled among the service blocks in the determined operating mode.

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