Battery-powered data sink gateway energy optimization method

Abstract

The present application relates to the battery-powered data convergence gateway energy optimization method based on, comprising the following steps: step 1: after the power consumption management application starts, periodically sends task state query request to each business application, inquires the current state of each business application;Step 2: the power consumption management application will receive the feedback of all business applications, and the current state of each business application and the sleep time are calculated to control the system to enter the sleep state and the sleep time;Step 3: even if the system is in sleep state, the system still listens to wake-up events, such as RTC clock, serial port and other wake-up events;The present application has the advantages of being more intelligent, based on adaptive sleep mechanism and dynamic energy management strategy.

Description

Technical Field

[0001] This invention belongs to the field of data aggregation gateway technology, specifically relating to an energy optimization method for battery-powered data aggregation gateways. Background Technology

[0002] With the vigorous development of the social economy and the continuous expansion of power grid coverage, more stringent requirements have been placed on the monitoring and management of transmission lines. Online monitoring devices for transmission lines play a crucial role in ensuring grid safety, improving operation and maintenance efficiency, and reducing operation and maintenance costs. However, currently, multiple online monitoring devices deployed on transmission towers each upload data to a cloud platform via wireless communication modules. This approach not only significantly increases construction costs but also leads to inefficient resource utilization. To address these issues, a data aggregation gateway has emerged. It integrates and manages various devices and data, and is deployed on transmission towers, employing a power supply method combining solar energy and batteries. While data aggregation gateways possess the capability to actively or passively collect data, ensuring its real-time performance and reliability, they also face high energy consumption issues due to the large number of connected devices and frequent tasks. This directly impacts the runtime of single-battery powered devices. To extend the device's operating time, increasing battery capacity is commonly used. However, larger capacity batteries often require larger photovoltaic panels, which not only increases the weight and size of the device, creating additional difficulties for high-altitude installation and maintenance, but also increases equipment costs, hindering widespread adoption. Therefore, it is essential to provide a more intelligent energy optimization method for battery-powered data aggregation gateways based on adaptive sleep mechanisms and dynamic energy management strategies. Summary of the Invention

[0003] (a) Technical issues

[0004] In view of the above-mentioned existing technology, this application mainly addresses the following technical problems:

[0005] 1. Currently, multiple online monitoring devices deployed on transmission towers each upload data to the cloud platform via wireless communication modules, which significantly increases construction costs and leads to inefficient use of resources;

[0006] 2. Data aggregation gateways face high energy consumption issues due to the large number of connected devices and frequent tasks, which directly affects the operating time of single-battery powered devices;

[0007] 3. Extending equipment operating time by increasing battery capacity often requires larger photovoltaic panels, which increases the weight and size of the equipment, adds extra difficulties to high-altitude installation and maintenance, and increases equipment costs.

[0008] (II) Technical Solution

[0009] The purpose of this invention is to overcome the shortcomings of the prior art and provide a more intelligent energy optimization method for battery-powered data aggregation gateways based on an adaptive sleep mechanism and dynamic energy management strategy.

[0010] The objective of this invention is achieved as follows: an energy optimization method for a battery-powered data aggregation gateway, the method comprising the following steps:

[0011] Step 1: After the power management application starts, it periodically sends task status query requests to each business application to inquire about the current status of each business application;

[0012] Step 2: The power management application will receive feedback from all business applications, and after comprehensively calculating the current status and sleep time of each business application, control the system to enter the sleep state and the sleep time.

[0013] Step 3: Even if the system is in hibernation, it still listens for wake-up events.

[0014] Further, step 1 specifically involves: during the power management cycle, sending a task status query request to each service application to inquire about the current status of each service application; if the service application is in a working state, it reports the working status and continues to execute the task; if the service application is in an idle state, it reports the idle status and the expected sleep time.

[0015] Furthermore, the expected sleep time in step 1 is specifically defined as follows: the expected sleep time T of the business application is defined as the following function: ,in, The interval between business executions; This represents the time since the last business transaction.

[0016] Furthermore, the system's sleep time in step 2 should be set to the shortest time interval between all business programs entering the idle state and the expected wake-up time of the first business program.

[0017] Furthermore, step 2 specifically involves defining the system's sleep time. For the following functions: ,in, The current time; This is the set of expected wake-up times, which are obtained by adding the current time to the expected sleep time of the business program. For set The minimum value in; Indicates if If the condition is met, return 0; otherwise, return 0. That is, if Then it will not enter hibernation; if The system will then go into hibernation. Unit of time.

[0018] Furthermore, step 3 specifically includes the following steps:

[0019] Step 3.1: When a wake-up event occurs, the system enters the working state;

[0020] Step 3.2: Upon receiving the event, the business application determines whether it can execute the task.

[0021] Step 3.3: After the task is completed, the business application feeds back its current status and expected sleep time to the power management application;

[0022] Step 3.4: Based on the feedback from the business applications, the power management application controls the system to enter the next round of sleep mode and the sleep time.

[0023] Furthermore, the judgment rule for the business application to determine whether it can execute the task in step 3.2 is as follows: the system sets the execution interval of the business, and if the detection interval has not been reached, the execution will be postponed; for serial port wake-up, the business application will determine whether the event is related to its own task.

[0024] Furthermore, in step 3.2, if there are multiple business applications that meet the conditions, the business application with the shortest sleep time will be selected to perform the task based on the sleep time.

[0025] The battery-powered data aggregation gateway energy optimization system adopts the battery-powered data aggregation gateway energy optimization method described above to dynamically control the operating status of the system and reduce overall power consumption. The system includes a gateway system, which includes a power management application and several service applications. The power management application and the service applications communicate with each other through shared memory.

[0026] Furthermore, the power management application periodically queries the task status of the business application, and the business application responds to its current status when queried.

[0027] The power management application performs comprehensive analysis based on the status information received from the business application, thereby reasonably controlling the system's sleep time;

[0028] The power management application readjusts the system state based on the status information fed back by all the service applications, and enters the next sleep or task state.

[0029] (III) Beneficial Effects

[0030] 1. This invention proposes an energy optimization method for battery-powered data aggregation gateways based on an adaptive sleep mechanism. It optimizes the energy consumption of battery-powered devices and reduces overall power consumption by dynamically controlling the operating status of the system, thereby improving the real-time performance and reliability of data.

[0031] 2. The method of the present invention solves the problems of high power consumption and short running time of data aggregation gateway when multiple types of online monitoring devices are connected. Attached Figure Description

[0032] Figure 1 This is a system architecture diagram of the hibernation mechanism of the present invention.

[0033] Figure 2 This is a flowchart of the process of the present invention. Detailed Implementation

[0034] The present invention will be further described below with reference to the embodiments and / or accompanying drawings.

[0035] Example 1

[0036] like Figure 1-2 As shown, an energy optimization method for a battery-powered data aggregation gateway includes the following steps:

[0037] Step 1: After the power management application starts, it periodically sends task status query requests to each business application to inquire about the current status of each business application;

[0038] In this invention, step 1 specifically involves: during the power management cycle, a task status query request is sent to each business application to inquire about the current status of each business application. If the business application is in a working state, the working status is reported and the task continues to be executed. If the business application is in an idle state, the idle status and the expected sleep time are reported (e.g., each temperature sensor collects data once every five minutes, and the interval for the temperature collection service is 300 seconds).

[0039] The expected sleep time is defined as follows: The expected sleep time T of the business application is defined as the following function: ,in, The interval between business executions; This represents the time since the last business transaction.

[0040] Step 2: The power management application will receive feedback from all business applications, and after comprehensively calculating the current status and sleep time of each business application, control the system to enter the sleep state and the sleep time.

[0041] In this invention, the system's sleep time should be set to the shortest time interval between all business programs entering the idle state and the expected wake-up time of the first business program. Specifically, the system's sleep time is defined. For the following functions: ,in, The current time; This is the set of expected wake-up times, which are obtained by adding the current time to the expected sleep time of the business program. For set The minimum value among all business programs in the system, that is, the earliest time for the hibernation to end; Indicates if If the condition is met, return 0; otherwise, return 0. That is, if Then it will not enter hibernation; if The system will then go into hibernation. Unit of time.

[0042] Step 3: Even if the system is in hibernation, it still listens for wake-up events, such as RTC clock and serial port wake-up events.

[0043] In this invention, step 3 specifically includes the following steps:

[0044] 3.1: When a wake-up event occurs (RTC clock wake-up or receipt of serial port data), the system enters the working state;

[0045] 3.2: Upon receiving an event, the business application determines whether it can execute the task: The system sets the execution interval for the business; if the time interval has not been reached, execution will be postponed. For serial port wake-up, the business application will determine whether the event is related to its own task; for example, if the system is woken up by a temperature sensor, the temperature sensor's business will be executed first; if there are multiple business applications that meet the conditions, the business application with the shortest sleep time will be selected to execute the task based on the sleep time.

[0046] 3.3: After the task is completed, the business application feeds back its current status (idle or continuing to work) and the expected sleep time to the power management application;

[0047] 3.4: The power management application controls the system to enter the next round of sleep mode and the sleep time based on the feedback results from business applications.

[0048] This invention relates to an energy optimization method for battery-powered data aggregation gateways. In practice, this invention proposes an energy optimization method for battery-powered data aggregation gateways based on an adaptive sleep mechanism. It optimizes energy consumption in battery-powered devices by dynamically controlling the operating status of the system to reduce overall power consumption and improve data real-time performance and reliability. This method solves the problem of high power consumption and short operating time of data aggregation gateways when multiple types of online monitoring devices are connected. Through the implementation of energy management strategies, while ensuring the real-time performance and reliability of connected data, it reduces the average power consumption of the entire device, extends the operating time of the device under single-battery power, reduces equipment costs, and improves product reliability and competitiveness. This invention has the advantages of being more intelligent, based on an adaptive sleep mechanism, and employing a dynamic energy management strategy.

[0049] Example 2

[0050] like Figure 1-2 As shown, the battery-powered data aggregation gateway energy optimization system adopts the battery-powered data aggregation gateway energy optimization method described above to dynamically control the operating status of the system and reduce overall power consumption. The system includes a gateway system, which includes a power management application and several service applications. The power management application and the service applications communicate with each other through shared memory.

[0051] In this invention, the gateway system includes a power management application and several service applications. The power management application and the service applications communicate with each other via shared memory. The power management application periodically queries the task status of the service applications. When queried, the service applications respond with their current status: working or idle. If in working state, the service application continues its task; if in idle state, it responds with the idle state and sleep time. The power management application performs comprehensive analysis based on the status information received from the service applications to rationally control the system's sleep time. When the system is in sleep state, it continuously listens for wake-up events. If a wake-up event is triggered (RTC clock or serial port), the system immediately exits sleep state. The power management application readjusts the system state based on the status information from all service applications and enters the next sleep or task state.

[0052] This invention relates to an energy optimization method for battery-powered data aggregation gateways. In practice, this invention proposes an energy optimization method for battery-powered data aggregation gateways based on an adaptive sleep mechanism. It optimizes energy consumption in battery-powered devices by dynamically controlling the operating status of the system to reduce overall power consumption and improve data real-time performance and reliability. This method solves the problem of high power consumption and short operating time of data aggregation gateways when multiple types of online monitoring devices are connected. Through the implementation of energy management strategies, while ensuring the real-time performance and reliability of connected data, it reduces the average power consumption of the entire device, extends the operating time of the device under single-battery power, reduces equipment costs, and improves product reliability and competitiveness. This invention has the advantages of being more intelligent, based on an adaptive sleep mechanism, and employing a dynamic energy management strategy.

Claims

1. An energy optimization method for a battery-powered data aggregation gateway, characterized in that: The method includes the following steps: Step 1: After the power management application starts, it periodically sends task status query requests to each business application to inquire about the current status of each business application; Step 2: The power management application will receive feedback from all business applications, and after comprehensively calculating the current status and sleep time of each business application, control the system to enter the sleep state and the sleep time. Step 3: Even when the system is in hibernation, it still listens for wake-up events; Step 1 specifically involves: During the power management cycle, a task status query request is sent to each business application to inquire about the current status of each business application. If the business application is in a working state, the working status is reported and the task continues to be executed. If the business application is in an idle state, the idle status and the expected sleep time are reported. The expected sleep time in step 1 is specifically defined as follows: the expected sleep time T of the business application is defined as the following function: ,in, The interval between business executions; This refers to the time since the last business transaction was executed. Step 2 specifically involves defining the system's sleep time. For the following functions: ,in, The current time; This is the set of expected wake-up times, which are obtained by adding the current time to the expected sleep time of the business program. For set The minimum value in; Indicates if If the condition is met, return 0; otherwise, return 0. That is, if Then it will not enter hibernation; if The system will then go into hibernation. Unit of time.

2. The energy optimization method for a battery-powered data aggregation gateway as described in claim 1, characterized in that: Step 3 specifically includes the following steps: Step 3.1: When a wake-up event occurs, the system enters the working state; Step 3.2: Upon receiving the event, the business application determines whether it can execute the task. Step 3.3: After the task is completed, the business application feeds back its current status and expected sleep time to the power management application; Step 3.4: Based on the feedback from the business applications, the power management application controls the system to enter the next round of sleep mode and the sleep time.

3. The energy optimization method for a battery-powered data aggregation gateway as described in claim 2, characterized in that: The rule for the business application to determine whether it can execute the task in step 3.2 is as follows: the system sets the execution interval of the business. If the time interval has not been reached, the execution will be postponed. When a serial port is woken up, the business application will determine whether the event is related to its own task.

4. The energy optimization method for a battery-powered data aggregation gateway as described in claim 3, characterized in that: If there are multiple business applications that meet the conditions in step 3.2, the business application with the shortest sleep time will be selected to perform the task based on the sleep time.

5. A battery-powered data aggregation gateway energy optimization system, employing the battery-powered data aggregation gateway energy optimization method as described in any one of claims 1-4, to dynamically control the operating state of the control system to reduce overall power consumption, characterized in that: The system includes a gateway system, which includes a power management application and several service applications. The power management application and the service applications communicate with each other through shared memory.

6. The energy optimization system for a battery-powered data aggregation gateway as described in claim 5, characterized in that: The power management application periodically queries the task status of the business application, and the business application responds to its current status when queried. The power management application performs comprehensive analysis based on the status information received from the business application, thereby reasonably controlling the system's sleep time; The power management application readjusts the system state based on the status information fed back by all the service applications, and enters the next sleep or task state.

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