Data aggregation gateway energy optimization method based on battery power supply

Abstract

The invention relates to a battery power supply-based data aggregation gateway energy optimization method, which comprises the following steps of: 1, after a power consumption management application is started, periodically sending a task state query request to each business application, and inquiring the current state of each business application; 2, the power consumption management application receives feedback of all the service applications, and controls the system to enter the dormant state and the dormant time after calculating by integrating the current state and the dormant time of each service application; 3, even if the system is in a dormant state, the system still monitors wake-up events such as RTC clock wake-up events and serial port wake-up events; the method has the advantages of being more intelligent and based on a self-adaptive dormancy mechanism and a dynamic energy management strategy.

Description

Technical Field

[0001] The invention belongs to the technical field of data convergence gateways, and in particular relates to an energy optimization method for a data convergence gateway based on battery power supply. Background Art

[0002] With the vigorous development of social economy, the coverage of power network is constantly expanding, which puts forward more stringent requirements on the monitoring and management of transmission lines. The online monitoring device of transmission line plays a vital role in ensuring the safety of power grid, improving operation and maintenance efficiency and reducing operation and maintenance costs. However, there are multiple online monitoring devices deployed on the transmission towers. They upload data to the cloud platform through wireless communication modules. This practice not only greatly increases the construction cost, but also leads to inefficient use of resources. In order to solve the above problems, the data aggregation gateway came into being. It is used to integrate and manage various devices and data. The data aggregation gateway is deployed on the transmission tower and adopts a power supply method combining solar energy and batteries. The data aggregation gateway is a kind of data aggregation gateway that can collect data actively or passively to ensure the real-time and reliability of the data. However, due to the large number of connected devices and frequent tasks, the data aggregation gateway faces the problem of high energy consumption, which directly affects the operating time of single-battery powered devices. In order to extend the operating time of the equipment, it is usually necessary to increase the battery capacity. Larger capacity batteries often need to be equipped with larger photovoltaic panels, which will not only increase the weight and volume of the equipment, but also bring additional difficulties to high-altitude installation and maintenance work, and increase the equipment cost, which is not conducive to the widespread promotion of the equipment. Therefore, it is very necessary to provide a more intelligent energy optimization method for battery-powered data aggregation gateway based on adaptive sleep mechanism and dynamic energy management strategy. Summary of the invention

[0003] 1. Technical issues

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

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

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

[0007] 3. Increasing the battery capacity to extend the equipment's operating time often requires the use of larger photovoltaic panels, which increases the weight and volume of the equipment, brings additional difficulties to high-altitude installation and maintenance work, and increases equipment costs.

[0008] (II) Technical solution

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

[0010] The object of the present invention is achieved by: a battery-powered data aggregation gateway energy optimization method, the method comprising the following steps:

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

[0012] Step 2: The power management application receives feedback from all business applications, and controls the system to enter the sleep state and sleep time after calculating the current state and sleep time of each business application;

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

[0014] Furthermore, the step 1 is specifically as follows: the power consumption management cycle sends a task status query request to each business application, inquiring about the current status of each business application: if the business application is in a working state, the working status is fed back and the task continues to be executed; if the business application is in an idle state, the idle state and the expected sleep time are fed back.

[0015] Furthermore, the estimated sleep time in step 1 is specifically as follows: the estimated sleep time T of the business application is defined as the following function: T=T interval -T elapsed (1), where T interval The interval time for business execution; T elapsed The time since the last business execution.

[0016] Furthermore, the sleep time of the system in step 2 should be set to the shortest time period between the time when all service programs enter the idle state and the time when the first service program is expected to wake up.

[0017] Furthermore, the step 2 is specifically as follows: defining the sleep time T of the system sleep is the following function: T sleep =max(0,min(wake_times)-T current )(2), where T current is the current time; wake_times = {T1, T2, ..., T n} is the estimated sleep time set of all business programs; min(wake_times) is the minimum value in the set wake_times; max(0,x) means if x≤0, it returns 0; otherwise it returns x, that is, if T sleep = 0, then do not enter sleep mode; if T sleep >0, the system goes into sleep mode T sleep Unit time.

[0018] Furthermore, the 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: After 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 estimated sleep time to the power management application;

[0022] Step 3.4: The power management application controls the system to enter the next round of sleep and sleep time according to the feedback results of the business application.

[0023] Furthermore, the judgment rule for the business application in step 3.2 to determine whether it can execute the task is as follows: the system sets the execution interval of the business, and if the detection time interval has not arrived, the execution will be postponed; when the serial port is woken 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 execute the task based on the sleep time.

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

[0026] Furthermore, the power consumption management application will periodically inquire about the task status of the business application, and the business application will feedback its current status when being inquired;

[0027] The power consumption management application performs a comprehensive analysis based on the state information fed back by the service application, thereby reasonably controlling the sleep time of the system;

[0028] The power consumption management application readjusts the system state according to the feedback state information of all the business applications and enters the next sleep or task state.

[0029] (III) Beneficial effects

[0030] 1. The present invention proposes an energy optimization method for a battery-powered data aggregation gateway based on an adaptive sleep mechanism, which optimizes the energy consumption in battery-powered devices, reduces overall power consumption by dynamically controlling the operating state of the system, and improves the real-time performance and reliability of data;

[0031] 2. The method of the present invention solves the problem of high power consumption and short operation time of the data aggregation gateway when multiple types of online monitoring devices are connected. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0033] Figure 2 It is the work flow chart of the present invention. DETAILED DESCRIPTION

[0034] The present invention is further described below in conjunction with embodiments and / or drawings.

[0035] Example 1

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

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

[0038] In the present invention, step 1 is specifically as follows: the power consumption management cycle sends a task status query request to each business application, inquiring about the current status of each business application: if the business application is in a working state, the working status is fed back and the task continues to be executed; if the business application is in an idle state, the idle state and the estimated sleep time are fed back (for example: each temperature sensor collects data once every five minutes, and the interval for executing the temperature collection business is 300s).

[0039] The estimated sleep time is as follows: The estimated sleep time T of the business application is defined as the following function: T = T interval -T elapsed (1), where T interval The interval time for business execution; T elapsed The time since the last business execution.

[0040] Step 2: The power management application receives feedback from all business applications, and controls the system to enter the sleep state and sleep time after calculating the current state and sleep time of each business application;

[0041] In the present invention, the sleep time of the system should be set to the shortest time period between when all business programs enter the idle state and when the first business program is expected to wake up, specifically: define the sleep time of the system T sleep is the following function: T sleep =max(0,min(wake_times)-T current )(2), where T current is the current time; wake_times = {T1, T2, ..., T n} is the estimated sleep time set of all business programs, including the estimated sleep time of n business programs; min(wake_times) is the minimum value in the set wake_times, that is, the earliest sleep end time of all business programs in the system; max(0,x) means if x≤0, it returns 0; otherwise it returns x, that is, if T sleep = 0, then do not enter sleep mode; if T sleep >0, the system goes into sleep mode T sleep Unit time.

[0042] Step 3: Even if the system is in sleep mode, the system still monitors wake-up events, such as RTC clock, serial port, etc.

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

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

[0045] 3.2: After receiving the event, the business application determines whether it can execute the task: the system sets the execution interval of the business. If the detection interval has not arrived, the execution will be postponed; when the serial port is woken up, the business application will determine whether the event is related to its own task; for example, if the system is woken up by the temperature sensor, the business of the temperature sensor 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 according to the sleep time;

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

[0047] 3.4: The power consumption management application controls the system to enter the next round of sleep and sleep time according to the feedback results of the business application.

[0048] The present invention is an energy optimization method for a battery-powered data aggregation gateway. During use, the present invention proposes an energy optimization method for a battery-powered data aggregation gateway based on an adaptive sleep mechanism, optimizes the energy consumption in battery-powered equipment, reduces overall power consumption by dynamically controlling the operating state of the system, and improves the real-time and reliability of data. The method of the present invention solves the problems of high power consumption and short operating time of the data aggregation gateway when multiple types of online monitoring devices are connected. Through the implementation of the energy management strategy, while ensuring the real-time and reliability of the access data, the average power consumption of the whole machine is reduced, the operating time of the equipment under single battery power supply is extended, the equipment cost is reduced, and the product reliability and competitiveness are improved. The present invention has the advantages of being more intelligent, based on an adaptive sleep mechanism, and a dynamic energy management strategy.

[0049] Example 2

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

[0051] In the present invention, the gateway system includes a power management application and several business applications, and the power management application and the business applications communicate with each other by means of shared memory; the power management application will periodically inquire about the task status of the business application, and the business application will feedback its current status when being inquired: working status or idle status, if it is in the working status, the business application continues its task; if it is in the idle status, the idle status and sleep time are fed back; the power management application performs a comprehensive analysis based on the status information fed back by the business application, so as to reasonably control the sleep time of the system; when the system is in the sleep state, it continuously monitors the wake-up event, and if a wake-up event is triggered (RTC clock or serial port), the system immediately exits the sleep state; the power management application readjusts the system state according to the status information fed back by all business applications, and enters the next sleep or task state.

[0052] The present invention is an energy optimization method for a battery-powered data aggregation gateway. During use, the present invention proposes an energy optimization method for a battery-powered data aggregation gateway based on an adaptive sleep mechanism, optimizes the energy consumption in battery-powered equipment, reduces overall power consumption by dynamically controlling the operating state of the system, and improves the real-time and reliability of data. The method of the present invention solves the problems of high power consumption and short operating time of the data aggregation gateway when multiple types of online monitoring devices are connected. Through the implementation of the energy management strategy, while ensuring the real-time and reliability of the access data, the average power consumption of the whole machine is reduced, the operating time of the equipment under single battery power supply is extended, the equipment cost is reduced, and the product reliability and competitiveness are improved. The present invention has the advantages of being more intelligent, based on an adaptive sleep mechanism, and a dynamic energy management strategy.

Claims

1. A battery-powered data aggregation gateway energy optimization method, characterized in that: The method comprises the following steps: Step 1: After the power management application is started, it periodically sends a task status query request to each business application to inquire about the current status of each business application; Step 2: The power management application receives feedback from all business applications, and controls the system to enter the sleep state and sleep time after calculating the current state and sleep time of each business application; Step 3: Even if the system is in sleep state, the system still listens for wake-up events.

2. The battery-powered data aggregation gateway energy optimization method according to claim 1, characterized in that: The step 1 is specifically as follows: the power consumption management cycle sends a task status query request to each business application, inquiring about the current status of each business application: if the business application is in a working state, the working status is fed back and the task is continued; if the business application is in an idle state, the idle state and the estimated sleep time are fed back.

3. The battery-powered data aggregation gateway energy optimization method according to claim 2, characterized in that: The estimated sleep time in step 1 is specifically as follows: define the estimated sleep time T of the business application as the following function: T = T interval -T elapsed (1), where T interval The interval time for business execution; T elapsed The time since the last business execution.

4. The battery-powered data aggregation gateway energy optimization method according to claim 1, characterized in that: The sleep time of the system in step 2 should be set to the shortest time period between the time when all business programs enter the idle state and the time when the first business program is expected to wake up.

5. The battery-powered data aggregation gateway energy optimization method according to claim 4, characterized in that: The step 2 is specifically as follows: defining the system sleep time T sleep is the following function: T sleep =max(0,min(wake_times)-T current )(2), where T current is the current time; wake_times = {T1, T2, ..., T n } is the estimated sleep time set of all business programs; min(wake_times) is the minimum value in the set wake_times; max(0,x) means if x≤0, it returns 0; otherwise it returns x, that is, if T sleep = 0, then do not enter sleep mode; if T sleep >0, the system goes into sleep mode T sleep Unit time.

6. The battery-powered data aggregation gateway energy optimization method according to claim 1, characterized in that: The step 3 specifically comprises the following steps: Step 3.1: When a wake-up event occurs, the system enters the working state; Step 3.2: After 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 estimated sleep time to the power management application; Step 3.4: The power management application controls the system to enter the next round of sleep and sleep time according to the feedback results of the business application.

7. The battery-powered data aggregation gateway energy optimization method according to claim 6, characterized in that: The judgment rule of the business application itself in step 3.2 to determine whether the task can be executed is: the system sets the execution interval of the business, and if the detection time interval has not arrived, the execution will be postponed; When the serial port is woken up, the business application will determine whether the event is related to its own task.

8. The battery-powered data aggregation gateway energy optimization method according to claim 7, 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 execute the task based on the sleep time.

9. A battery-powered data aggregation gateway energy optimization system, which uses a battery-powered data aggregation gateway energy optimization method as described in any one of claims 1 to 8 to dynamically control the operating state of the system to reduce overall power consumption, characterized in that: The system comprises a gateway system, wherein the gateway system comprises a power consumption management application and a plurality of business applications, and the power consumption management application and the business applications communicate with each other in a shared memory manner.

10. The battery-powered data aggregation gateway energy optimization system according to claim 9, characterized in that: The power consumption management application will periodically inquire the task status of the business application, and the business application will feedback its current status when being inquired; The power consumption management application performs a comprehensive analysis based on the state information fed back by the service application, thereby reasonably controlling the sleep time of the system; The power consumption management application readjusts the system state according to the feedback state information of all the business applications and enters the next sleep or task state.

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