CN118138525B - Low-power consumption router integrating energy management - Google Patents
Low-power consumption router integrating energy management Download PDFInfo
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
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Abstract
The invention discloses a low-power consumption router integrating energy management, which comprises: the system comprises a network communication module, a power monitoring sensor, an adaptive energy management system, a mode switching execution module and a battery management system; the network communication module is used for processing all the in-out network data and acquiring real-time network flow data; the power monitoring sensor is used for acquiring real-time power grid condition data; the self-adaptive energy management system is used for determining an energy consumption mode according to the real-time network flow data and the real-time power grid condition data; the mode switching execution module is used for switching to the determined energy consumption mode; the battery management system is used for controlling the built-in battery to discharge to supply power for the router when the energy consumption mode is a battery power supply mode. The router can realize efficient energy utilization and optimize power supply through an integrated energy management system.
Description
Technical Field
The invention relates to computer network hardware and an energy management system thereof, in particular to a low-power consumption router integrating energy management.
Background
With the rapid development of information technology and the internet, the energy consumption of network devices, especially routers, becomes a non-negligible problem. Conventional routers suffer from inefficient and power-intensive energy management, especially in standby mode, while still consuming significant energy. Furthermore, conventional routers typically rely on continuous grid power, lacking the ability to effectively cope with power instability or interruption.
Disclosure of Invention
In view of the above, an embodiment of the present invention provides a low power router integrated with energy management to solve the above technical problems.
To achieve the above object, there is provided a low power router integrating energy management, comprising: the system comprises a network communication module, a power monitoring sensor, an adaptive energy management system, a mode switching execution module and a battery management system;
the network communication module is used for processing all the in-out network data and acquiring real-time network flow data;
the power monitoring sensor is used for acquiring real-time power grid condition data;
The self-adaptive energy management system is used for determining an energy consumption mode according to the real-time network flow data and the real-time power grid condition data;
The mode switching execution module is used for switching to the determined energy consumption mode;
The battery management system is used for controlling the built-in battery to discharge to supply power for the router when the energy consumption mode is a battery power supply mode.
In some possible embodiments, the adaptive energy management system is specifically configured to use a deep learning technology, train a deep learning model by analyzing historical data, predict a router energy demand change trend and a power grid condition change trend in a future period based on the deep learning model, and determine an energy consumption mode according to a prediction result of the deep learning model; wherein the historical data includes historical network flow data and historical grid condition data.
In some possible embodiments, the adaptive energy management system comprises:
The data collection and preprocessing module is used for collecting historical data and preprocessing the historical data including cleaning and standardization to obtain preprocessed historical data;
The deep learning model training module is used for training a deep learning model by using the preprocessed historical data;
the real-time prediction and decision making module is used for predicting real-time data including real-time network flow data and real-time power grid condition data by using the trained deep learning model to obtain a prediction result, and determining an energy consumption mode based on the prediction result;
the mode switching execution module is specifically configured to adjust a working state of the router to execute the energy consumption mode.
In some possible embodiments, the energy consumption mode includes a plurality of full power mode, normal mode, energy saving mode, ultra low power consumption mode, and battery powered mode; the real-time prediction and decision making module is specifically configured to:
if the prediction result indicates that the network flow rate is higher than a preset high flow rate threshold value in a future period of time and the power grid parameters corresponding to the predicted power grid condition change trend are all in a normal range, determining that the energy consumption mode is a full power mode;
If the prediction result indicates that the network flow in a period of time in the future is lower than a preset low flow threshold, determining that the energy consumption mode is an energy saving mode;
If the prediction result indicates that the network flow is between a preset high flow threshold and a preset low flow threshold in a future period of time and the power grid parameter corresponding to the predicted power grid condition change trend is stable in a normal range, determining that the energy consumption mode is a normal mode;
if the prediction result indicates that the network flow is close to zero in a future period of time, determining that the energy consumption mode is an ultra-low power consumption mode;
If the prediction result indicates that abnormal fluctuation or interruption of the power grid parameters occurs in a period of time in the future, determining that the energy consumption mode is a battery power supply mode;
if the prediction indicates that the grid flow and grid conditions will remain at the current level for a period of time in the future, the current energy consumption pattern is maintained.
In some possible embodiments, the battery management system includes:
the battery state monitoring module is used for continuously collecting battery state data including the voltage, the current, the temperature and the state of charge of the built-in battery;
The charging control module is used for adjusting the charging rate or the charging period through an intelligent charging algorithm according to the battery state data;
The discharging control module is used for controlling the built-in battery to discharge to supply power for the router when the energy consumption mode is a battery power supply mode, and determining discharging power or discharging rate according to the load requirement of the router and the battery state data;
And the battery health and performance monitoring module is used for periodically evaluating the health condition of the built-in battery according to the battery state data and/or detecting whether the performance of the built-in battery is abnormal or not according to a preset battery parameter safety threshold.
In some possible embodiments, the mode switching execution module is specifically configured to switch to the determined energy consumption mode by controlling hardware settings; wherein,
In the full power mode, the mode switching execution module sets the clock frequency of the central processing unit as the highest main frequency, opens all wireless radio frequency transmitting channels and works at the maximum power, enables all wired network ports to work at the highest speed and enables other peripheral devices to keep working states;
In the normal mode, the mode switching execution module dynamically adjusts the frequency of the central processing unit according to the network flow, selectively closes part of wireless radio frequency emission channels according to the connection condition of wireless equipment, adjusts the port work rate according to the use condition of a wired network and closes an idle peripheral equipment power supply;
in the energy-saving mode, the mode switching execution module reduces the clock frequency of the central processing unit to the lowest working frequency, closes part of wireless radio frequency emission channels, reserves a 2.4G channel and a 5G channel, limits the highest speed of a wired network port to be below 100Mbps, and closes all unnecessary peripheral equipment power supplies;
Under the ultra-low power consumption mode, the mode switching execution module switches the central processing unit to the lowest idle mode, closes all wireless radio frequency emission channels, closes all wired network ports and closes all peripheral equipment power supplies;
In the battery-powered mode, the mode switching execution module performs the following operations: cutting off a power supply circuit of an external power line; enabling a battery power management circuit; adjusting a hardware working mode to prolong the battery endurance time; enabling battery power monitoring; and dynamically adjusting the hardware working mode according to the real-time electric quantity condition of the battery.
In some possible embodiments, the adjusting the hardware operation mode to extend the battery life specifically includes:
reducing the CPU clock frequency to a moderate level;
Closing part of the wireless radio frequency channels and reserving 2.4G channels or 5G channels;
limiting the operation rate of the wired network port to be below 100 Mbps; and
The optional peripheral power is turned off.
In some possible embodiments, the dynamically adjusting the hardware working mode according to the real-time electric quantity condition of the battery specifically includes:
When the battery power is higher than a preset first power threshold, the battery power is considered to be sufficient, and the frequency of the central processing unit and the wireless radio frequency power are improved;
when the battery power is lower than a preset second power threshold, the battery is regarded as power shortage, and the hardware power consumption is reduced.
In some possible embodiments, the integrated energy management low-power consumption router further comprises a renewable energy module, configured to generate electricity by using renewable energy, where the generated electricity can power the router or be stored in a built-in battery of the router; the renewable energy source module comprises a solar panel and/or a wind energy generator, wherein the solar panel is arranged on the top or the side surface of the router, and the wind energy generator is arranged on the outside or the top of the router; the battery management system is positioned at the bottom of the router and is connected with the energy management system and the renewable energy module through cables.
In some possible embodiments, the network communication module employs a network interface controller, the network interface controller comprising: the network processor is used for receiving and transmitting the data packet; the hardware counter is used for monitoring the incoming and outgoing data packets in real time, and carrying out statistics and analysis on the data packets to obtain real-time network flow data; the energy consumption modes comprise a full power mode, a normal mode, an energy saving mode, an ultra-low power consumption mode and a battery power supply mode;
The power monitoring sensor adopts a Hall effect sensor, a voltage transformer or a power quality analyzer; the real-time grid condition data includes: grid voltage, grid current, grid frequency, and grid power factor.
The technical scheme has the following beneficial technical effects:
Through the self-adaptive energy management system, the router can intelligently adjust the energy consumption mode according to the real-time network flow and the power grid state. Such adaptive adjustment helps to reduce unnecessary energy consumption, particularly when network traffic is low, thereby reducing overall power costs.
The design of the battery management system allows for automatic switching to battery-powered mode in case of grid-powered instability or outage. This ensures that the router can continue to operate when the power supply is interrupted, enhancing the reliability and stability of the system.
The power monitoring sensors provide real-time grid condition data so that the energy management system can more accurately evaluate and adjust energy consumption. The method is not only beneficial to optimizing energy use, but also can respond in time when a power grid problem occurs, and improves the response speed and the safety of the system.
By optimizing energy consumption, the router can reduce overall power requirements and reduce carbon emissions, with a positive impact on the environment. Particularly when renewable energy sources and batteries are used for supplying power, the environment-friendly characteristic of the energy-saving type solar energy storage device can be further improved.
The introduction of the mode switching execution module allows the system to automatically switch after determining the energy consumption mode most suitable for the current situation without manual intervention. Such intelligent operation reduces management complexity and improves operation efficiency.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a functional block diagram of a low power router integrated with energy management in accordance with an embodiment of the present invention;
FIG. 2 is a specific functional block diagram of an adaptive energy management system according to an embodiment of the present invention;
FIG. 3 is a specific functional block diagram of a battery management system of an embodiment of the present invention;
FIG. 4 is a flow chart of a method of operation of a low power router integrating energy management in accordance with an embodiment of the present invention;
FIG. 5 is a specific flowchart of step S3 of an embodiment of the present invention;
FIG. 6 is another specific flowchart of step S3 of an embodiment of the present invention;
FIG. 7 is a specific flowchart of step S5 of an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a computer system of an electronic device according to an embodiment of the present invention.
Reference numerals illustrate:
110. A network communication module; 120. a power monitoring sensor; 130. an adaptive energy management system; 140. a mode switching execution module; 150. a battery management system;
131. a data collection and preprocessing module; 132. a deep learning model training module; 133. a real-time prediction and decision-making module;
151. a battery state monitoring module; 152. a charge control module; 153. a discharge control module; 154. and a battery health and performance monitoring module.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The embodiment of the invention aims to provide a low-power consumption router integrating energy management, which can realize efficient energy utilization and optimized power supply through an integrated energy management system and can keep stable operation even if the power supply is unstable or interrupted.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a low power router integrated with energy management, which is powered by an external grid power source and is equipped with a built-in backup battery; the low power router of integrated energy management includes: a network communication module 110, a power monitoring sensor 120, an adaptive energy management system 130, a mode switch execution module 140, and a battery management system 150.
The network communication module 110 is configured to process all incoming and outgoing network data and obtain real-time network traffic data. The functions of this module include data reception, transmission, routing, packet analysis, etc. The network communication module 110 supports a variety of communication protocols (e.g., wi-Fi, ethernet, etc.), and processing refers to the operation of receiving, sending, forwarding, routing, etc., network packets. The network communication module 110 may employ a communication processor supported by high-speed multiprotocol, and has the capability of processing protocols such as IPv4 (Internet Protocol version, internet Protocol version 4), IPv6 (Internet Protocol version, internet Protocol version 6), PPP (Point-to-Point Protocol), PPPoE (Point-to-Point Protocol over Ethernet ), and the like.
Specifically, the network communication module 110 may employ a network interface controller (Network Interface Controller, NIC) that is responsible for receiving and transmitting all data packets, counting and analyzing the data packets flowing through, and providing information such as total traffic, packet type, source IP address, and destination IP address. The network interface controller has a dedicated network processor for high-speed data processing and forwarding. They are capable of processing data flows over multiple network interfaces simultaneously and providing detailed traffic statistics. The network interface controller is internally provided with a hardware counter for monitoring statistics data such as the number of data packets, the number of error packets, the total flow and the like in real time.
Network traffic data refers to the amount of data transmitted through a network device (e.g., router) including information such as sending and receiving rates, time stamps, packet size, number of packets, data type, source and destination addresses, session information, etc. By monitoring the network traffic data, the adaptive energy management system 130 can obtain the intensity of the current network activity, thereby determining how much energy the router needs to devote to maintaining efficient operation. The sending rate and the receiving rate are used to describe the speed of data transmission, i.e. the amount of data transmitted per second. A time stamp for each exact point in time of the data record, for tracking the time of transmission and reception of the data packets. The packet size indicates the specific number of bytes per packet transmitted. The data type indicates the type of data transmission, such as HTTP (HyperText Transfer Protocol ) request, FTP (FILE TRANSFER Protocol, file transfer Protocol) transmission, and the like. The source address and the destination address are network addresses indicating the transmission start point and the reception end point of the data packet. The number of packets refers to the total number of packets that have passed through the network at a particular time. Session information refers to network connection details, such as duration, etc., within a specific time period.
The power monitoring sensor 120 is configured to acquire real-time grid condition data. The power monitoring sensor 120 can monitor parameters such as grid voltage, grid current, grid frequency, grid power factor, etc. The monitoring method adopts a real-time sampling technology, and ensures the accuracy and the real-time performance of the data. Specifically, the power monitoring sensor 120 may employ a hall effect sensor, a voltage transformer, or a power quality analyzer. The Hall effect sensor adopts non-contact measurement, is suitable for high-voltage or high-current occasions, and has high response speed. The voltage transformer can be used for high-precision voltage measurement and is suitable for power grid voltage monitoring. The power quality analyzer is an integrated device that can measure voltage, current, frequency and power factor simultaneously, providing an exhaustive power quality analysis. The multifunctional electric energy meter provides an integrated solution, and is suitable for continuously monitoring and recording various power grid parameters.
The adaptive energy management system 130 is configured to determine an energy consumption pattern according to the real-time network flow data and the real-time grid condition data, so as to optimize the power energy use efficiency. Specifically, the adaptive energy management system 130 analyzes the current network usage intensity and the stability of power supply, and accordingly determines whether to take a different energy consumption mode such as a power saving mode or a full power operation mode to maximize energy efficiency while ensuring performance.
The mode switching execution module 140 is configured to switch to the determined energy consumption mode.
The battery management system 150 is configured to control the built-in battery to discharge to supply power to the router when the energy consumption mode is a battery power mode. Specifically, the battery management system 150 is configured to manage the charging and discharging processes of the built-in battery, so as to ensure that the router can continue to operate when the power supply of the power grid is abnormal. The power supply of the power grid refers to external power supply of the router, namely alternating current power supplied by the power grid; the built-in battery refers to a standby power supply built in the router, and can maintain the operation of the router when the power supply of the power grid is abnormal. The built-in battery is charged by a charging circuit inside the router, and is monitored and maintained by a battery management system.
In some embodiments, the adaptive energy management system 130 is specifically configured to use a deep learning technique, train a deep learning model by analyzing historical data, predict a router energy demand change trend and a power grid abnormal condition or power grid condition change trend (such as voltage instability and frequency fluctuation) in a future period based on the deep learning model trained in advance, and determine an energy consumption mode according to a prediction result of the deep learning model; wherein the historical data includes historical network flow data and historical grid condition data.
As shown in fig. 2, in some embodiments, the adaptive energy management system 130 includes: a data collection and preprocessing module 131, a deep learning model training module 132, and a real-time prediction and decision making module 133. The data collection and preprocessing module 131 is configured to collect the history data and perform preprocessing including cleaning and normalization on the history data to obtain preprocessed history data. The deep learning model training module 132 is configured to train a deep learning model using the preprocessed history data. The real-time prediction and decision-making module 133 is configured to predict real-time data including real-time network traffic data and real-time power grid condition data by using the trained deep learning model, obtain a prediction result, and determine an energy consumption mode based on the prediction result. The energy consumption mode refers to an operation state of the router, such as a full power mode, a normal mode, a power saving mode, an ultra low power consumption mode, a battery power mode, and the like.
Specifically, determining different energy consumption modes based on the prediction results of the deep learning model specifically includes:
When the deep learning model predicts that the grid flow will remain at a very high level (above a preset high flow threshold) for a period of time in the future and the predicted grid conditions are good (the parameters such as the grid voltage, the frequency, etc. are all within the normal range), the adaptive energy management system 130 will determine that the energy consumption mode is the full power mode. Specifically, if the prediction result shows that: within the next few hours, the network flow is higher than a preset high flow threshold; in the next several hours, parameters such as power grid voltage, frequency and the like are all in a normal stable range; then a switch will be made to full power mode for optimum network performance and user experience.
When the deep learning model predicts that the grid flow fluctuates at a medium level (between the preset high and low flow thresholds) for a period of time in the future, and the grid conditions are normal, the adaptive energy management system 130 will determine that the energy consumption mode is the normal mode. Specifically, if the prediction result shows that: within a few hours in the future, network traffic fluctuates between medium low and medium high levels; in the next few hours, the power grid parameters are stable in a normal range; then the normal mode will be switched to, dynamically adjusting the hardware resources, and balancing the performance and power consumption.
When the deep learning model predicts that network traffic will remain at a low level for a period of time in the future, the adaptive energy management system 130 will determine that the energy consumption mode is an energy saving mode. Specifically, if the prediction result shows that: in the next few hours, the network flow is continuously lower than a preset low flow threshold; then the switch will be made to the energy saving mode reducing the energy consumption but still maintaining basic network services.
When the deep learning model predicts that the router will be in an idle state for a long period of time, the adaptive energy management system 130 will determine that the energy consumption mode is an ultra-low power consumption mode. Specifically, if the prediction result shows that: for a few hours or more in the future, network traffic is near zero; then the system will switch to the ultra-low power mode and only the minimum system resources will be reserved for operation.
When the deep learning model predicts that a grid anomaly will occur within a future period of time, the adaptive energy management system 130 will determine that the energy consumption mode is a battery-powered mode. Specifically, if the prediction result shows that: in the next few hours, abnormal fluctuation or interruption of parameters such as power grid voltage, frequency and the like occurs; the system will switch to battery powered mode from mains power to built-in battery power ensuring continued operation of the router.
If the deep learning model predicts that network traffic and grid conditions will remain at the current level for a period of time in the future, the adaptive energy management system 130 will maintain the current energy consumption pattern unchanged.
Through the analysis of the prediction results, the adaptive energy management system 130 can switch to the optimal energy consumption mode in advance according to the change trend of the network flow and the power grid condition in real time, so that the energy is saved to the maximum extent, and the network performance and the reliability are ensured. The decision of the system is no longer passively responsive to the current state, but acts ahead of time based on predictions of future states.
In some embodiments, the mode switching execution module 140 is specifically configured to adjust an operating state of the router to execute the energy consumption mode, and feed back an execution effect of the energy consumption mode to the deep learning model.
Specifically, the mode switching execution module 140 is configured to switch to the determined energy consumption mode by controlling the hardware setting.
In full power mode, all hardware components of the router operate in the highest performance state to meet the maximum network throughput and best user experience. The mode switch execution module 140 is realized by the following hardware control: the CPU clock frequency is set to the highest main frequency; all wireless radio frequency emission channels are opened and work at the maximum power; all wired network ports operate at the highest rate; other peripheral devices (such as USB interfaces) remain operational.
In the normal mode, part of the hardware resources are dynamically adjusted according to actual requirements to balance between performance and power consumption. The mode switch execution module 140 will: dynamically adjusting the CPU frequency according to the network flow; selectively closing part of wireless radio frequency emission channels according to the connection condition of the wireless equipment; according to the use condition of the wired network, the port work rate is regulated; and closing the idle peripheral power supply.
In the energy saving mode, the energy consumption is further reduced, and the method is started when the network traffic is low. The mode switch execution module 140 will: reducing the CPU clock frequency to the lowest working frequency; closing part of the wireless radio frequency emission channels, and reserving only one 2.4G channel and one 5G channel; limiting the highest rate of the wired network port to 100Mbps or less; all optional peripheral power is turned off.
In the ultra low power mode, this is the lowest power mode, used when the router is idle for a long time. The mode switch execution module 140 will: switching the CPU to the lowest idle mode; closing all wireless radio frequency emission channels; closing all wired network ports; all peripheral power is turned off.
In the battery-powered mode, the router relies on the built-in battery to operate, and the mode switch execution module 140 is implemented by the following hardware control:
(1) Power supply circuit for closing power line
First, the mode switch execution module 140 will cut off the power supply circuit of the external power line, ensuring that the router is fully dependent on the built-in battery for operation.
(2) Battery-powered enabled management circuit
The mode switch execution module 140 will then enable the battery power management circuitry, including the DC-DC converter, etc., to convert and stabilize the direct current voltage of the battery to the operating voltage required by the various hardware components.
(3) Adjusting hardware modes of operation
To extend battery life, the mode switch execution module 140 will exercise fine power consumption control over the hardware:
The CPU clock frequency is reduced to a moderate level, so that only basic network service is ensured; closing part of the wireless radio frequency channels, and only reserving one of 2.4G or 5G; the wired network port operating rate is limited to 100Mbps or less; power to optional peripheral devices, such as USB interfaces, etc., is turned off.
(4) Enabling battery power monitoring
The mode switch execution module 140 will enable the battery power monitoring circuit to collect the voltage, current, temperature, etc. data of the battery in real time and feed back these data to the battery management system 150.
(5) Dynamic adjustment according to electric quantity
Depending on the real-time battery charge status, the mode switch execution module 140 may further adjust the hardware operation mode, with trade-off between endurance and performance: when the electric quantity is sufficient, the CPU frequency and the wireless radio frequency power are properly improved; when the electric quantity is insufficient, the hardware power consumption is further reduced, and the key network service is ensured. Wherein the first charge threshold may be set to a higher charge value, for example 80% of the charge; the second charge threshold may be set to a lower charge value, for example 20% of the charge. When the battery power is higher than 80%, the battery power is considered to be sufficient, and the CPU frequency and the wireless power can be properly improved so as to obtain better performance; when the battery power is lower than 20%, the battery is regarded as insufficient power, and the hardware power consumption needs to be reduced to ensure the operation of key network services.
Through the series of fine hardware control, the mode switching execution module 140 can seamlessly switch the router to the battery power supply mode, dynamically adjust the hardware working mode according to the battery condition, furthest prolong the battery endurance time and ensure that the router can stably operate under the battery power supply condition.
In a further embodiment, further comprising: and in the automatic adjustment mode, the router automatically switches between a full-power mode and an energy-saving mode according to the network service condition and the power grid state monitored in real time, and manual intervention is not needed. Standby mode during periods of non-use, such as during night or off-peak hours, the router may automatically switch to standby mode to further reduce power consumption. And in the peak value reduction mode, the energy consumption is actively reduced in the peak period of the power grid, the load of the power grid is reduced, and the energy cost is reduced.
In the automatic adjustment mode, the mode switching execution module 140 automatically switches between the full power mode and the energy saving mode according to the network use condition and the power grid state monitored in real time, without manual intervention. The specific implementation method comprises the following steps: the module monitors the network traffic, the number of wireless equipment connections, the use condition of the wired network port and the like, and judges the use intensity of the network. And meanwhile, the power supply state of the power grid provided by the power supply state monitoring sub-module is also monitored. When the network use intensity is high and the power supply of the power grid is stable, the system is automatically switched to a full-power mode, and the hardware performance is maximized in the mode. When the network use intensity is low or the power supply of the power grid is abnormal, the energy-saving mode is automatically switched to, and the hardware power consumption is reduced in the mode.
With respect to standby mode, it is meant that during periods of non-use, such as during night or off-peak hours, the mode switch execution module 140 may automatically switch the router to standby mode to further reduce power consumption. The method is realized by the following hardware control: network usage is continuously monitored and when no network activity is detected for a number of consecutive hours, a period of non-use is determined. And switching the CPU to the lowest idle mode, so that the clock frequency is greatly reduced. Closing all the wireless radio frequency transmitting channels. All wired network ports are closed. All optional peripheral power is turned off.
Regarding the peak clipping mode, during peak grid periods, the mode switch execution module 140 actively switches the router to a low power mode to clip energy consumption peaks, reduce grid burden and reduce energy costs. The implementation method comprises the following steps: and obtaining a peak time schedule from the power grid company or autonomously analyzing a peak time mode according to the historical electricity consumption data. Before the peak time comes, the router is automatically switched to an energy-saving mode or an ultra-low power consumption mode, and the hardware power is limited. After the peak period is over, the previous energy consumption mode is automatically restored.
Through the hardware control strategy, the mode switching execution module 140 can switch between different energy consumption modes automatically or intelligently according to the power grid state, realize energy supply according to requirements, avoid resource waste, and simultaneously actively reduce peak energy consumption, load shedding for the power grid and save energy cost.
As shown in fig. 3, in some embodiments, the battery management system 150 includes: a battery state monitoring module 151 for continuously collecting battery state data including voltage, current, temperature, and state of charge of the built-in battery; the charging control module 152 is configured to adjust a charging rate or a charging period according to the battery status data through an intelligent charging algorithm; a discharging control module 153 for controlling the built-in battery to discharge to supply power to the router when the energy consumption mode is the battery power supply mode, and determining a discharging power or a discharging rate according to a load demand of the router and the battery state data; the battery health and performance monitoring module 154 is configured to periodically evaluate the health status of the internal battery according to the battery status data and/or detect whether the performance of the internal battery is abnormal according to a preset battery parameter safety threshold.
Specifically, the charge control module 152 specifically includes:
And the data analysis sub-module is used for analyzing the battery state data by utilizing a built-in intelligent charging algorithm after receiving the battery state data to obtain an analysis result, wherein the analysis result indicates the charging requirement and the capacity limitation of the battery. The intelligent charging algorithm evaluates the charging requirement and capacity limitation of the battery according to the current state of charge, temperature and other factors of the battery.
And the charging strategy adjustment sub-module is used for adjusting the charging rate or the charging period according to the analysis result of the intelligent charging algorithm. For example, if the state of charge of the battery is low and the battery temperature is normal, the charge rate is increased to quickly replenish the charge; if the battery temperature is too high, the charge rate is reduced or charging is suspended to prevent overheating and protect the battery from health.
And the execution and feedback sub-module is used for continuously monitoring the charging process and adjusting the charging strategy according to the response of the battery in the execution process of the adjusted charging strategy. The execution and feedback sub-module also continuously feeds back the battery status to the battery status monitoring module 151, ensuring that other parts can also be adjusted accordingly according to the latest battery status.
Specifically, the discharging control module 153 is specifically configured to obtain a power supply state (normal, interrupted, unstable, etc.), a battery state parameter (voltage, current, charge state, etc.), and a router load requirement; when the power supply of the power grid is abnormal (interrupted or unstable), the built-in battery is controlled to discharge to supply power for the router. According to the load requirement of the router and the battery state data, the optimal discharge current or discharge power is calculated intelligently, so that the power supply requirement of the router is met, and the damage of the built-in battery caused by excessive discharge is avoided.
In particular, load demand refers to the actual amount of demand of power by a router at run-time. The router can be obtained from the working state (full power, energy saving and the like), the network traffic size, the number of external equipment connections and the like of the router. The method can be obtained by monitoring real-time operation data such as power consumption, access connection number, data throughput and the like of the router.
Specifically, the discharge control module 153 may include the following sub-modules:
And the power supply state monitoring sub-module is used for monitoring the power supply state of the power grid in real time and judging whether power supply abnormality (interruption, instability and the like) occurs. Meanwhile, the operation state of the router is also monitored, and the real-time power consumption and load demand information of the router are obtained.
The battery state monitoring sub-module is configured to obtain real-time state data of the built-in battery, including voltage, current, temperature, state of charge, etc., from the battery state monitoring module 151, and provide a decision basis for discharging control.
And the discharging strategy calculation sub-module is used for calculating the optimal discharging current or discharging power which meets the router requirement and does not damage the battery through the intelligent discharging algorithm by combining the battery data provided by the battery state monitoring sub-module according to the router load requirement acquired by the power state monitoring sub-module.
And the discharging execution sub-module is used for calculating a discharging current or a discharging power set value given by the sub-module according to a discharging strategy and controlling the built-in battery to be actually discharged to supply power for the router.
The discharge monitoring feedback sub-module is used for monitoring the actual power consumption of the router and the discharge condition of the built-in battery in the discharge process in real time, and feeding back monitoring data to the discharge strategy calculation sub-module for dynamically adjusting the discharge strategy.
Specifically, the battery health and performance monitoring module 154 specifically includes: the battery health evaluation sub-module is used for regularly reading the state data of the built-in battery, tracking the change trend of the battery performance index along with time, such as capacity reduction, internal resistance increase and the like, and accordingly evaluating the battery health condition; the battery remaining life prediction sub-module is used for estimating the remaining service life of the built-in battery by using a battery aging model and combining historical battery use data and the current state of the built-in battery; the current state of the built-in battery includes the voltage, current, temperature, state of charge of the battery, the number of possible cycles, etc. These parameters reflect the instantaneous health and performance conditions of the battery, facilitating the assessment of the overall health of the battery and the prediction of its remaining life; and the battery performance detection sub-module is used for setting a safety threshold value of key parameters (such as voltage and temperature) of the built-in battery, monitoring battery data in real time, judging that the performance is abnormal if the battery data exceeds the threshold value, and sending out a warning.
In some embodiments, the battery aging model is a function of battery capacity fade as a function of cycle number, temperature, state of charge, etc. through fitting to a plurality of battery aging experimental data. First, battery aging test data are collected. And under the working conditions of different temperatures, charge states and the like, carrying out long-term cyclic aging experiments on batteries of the same model, and periodically testing and recording the key parameters such as the capacity, the internal resistance and the like of the batteries. Then, a battery aging model is established. And processing the collected experimental data, fitting out the functional relation between the capacity attenuation rate and the cycle times, the temperature and the state of charge, and obtaining the coefficient of the battery aging model. Finally, a battery aging model is applied to estimate the remaining life. The battery remaining life prediction sub-module combines the obtained battery aging model with the historical use data (such as cycle times, temperature, state of charge and the like) and the current state data (such as voltage, current, temperature, state of charge and the like) of the specific battery, substitutes the data into the battery aging model, calculates the current aging degree of the battery, and further calculates the remaining usable cycle times and the remaining service life of the battery.
In some embodiments, the battery performance detection sub-module is specifically configured to: a set of preset battery parameter safety thresholds are preset, including the highest voltage, lowest voltage, maximum allowed temperature, current range, etc. of the battery. Real-time data of the battery is collected from the battery status monitoring module 151 and compared with a preset safety threshold. By analyzing whether the parameters of the voltage, the current, the temperature and the like of the battery exceed the normal range, whether the battery has abnormal performance can be evaluated.
In some embodiments, the integrated energy management low power router further comprises a renewable energy module for generating electricity from renewable energy, the generated electricity being either powering the router or stored in a built-in battery of the router.
In some embodiments, the renewable energy module includes a solar panel mounted on top of or to the side of the router and/or a wind generator mounted on the outside of or on top of the router.
In some embodiments, the battery management system is located at the bottom of the router and is connected to the energy management system and the renewable energy module by a cable. By arranging the high-efficiency battery pack in the power grid, the power grid can be automatically switched when the power supply of the power grid is interrupted, and uninterrupted network service is ensured.
In some embodiments, the energy consumption modes include a full power mode, a normal mode, a power saving mode, an ultra low power consumption mode, and a battery powered mode.
In full power mode, the router operates at a maximum performance level, with all processor cores and network interfaces in a highest performance state to handle large amounts of data or high demand tasks. Application scenarios are suitable for high load situations such as mass media transfer, file download or high speed data processing.
In the normal mode, the router provides sufficient processing power to cope with everyday use. In this normal mode, the power consumption and performance of the router are in equilibrium.
In the power saving mode, the router reduces its performance to reduce power consumption, which will turn off some uncore functions or reduce processor frequency. The application scenario includes a low or off-peak network usage period, such as a night or a user-less period, where the energy-saving mode may reduce power consumption while meeting basic network demands.
In the ultra-low power mode, the router further reduces power consumption, possibly maintaining only the most basic network connection, such as Wi-Fi signal maintenance, closing most wired ports and unnecessary radio bands.
When the power supply of the power grid is interrupted or unavailable, the router is automatically switched to a battery power supply mode, and the power consumption and the functions are adjusted according to the residual capacity of the battery, so that the router is ensured to continue providing services.
In some embodiments, the router further comprises a housing supporting the installation of the solar panel and the wind power generator, and having ventilation and heat dissipation structures ensuring that all critical components can operate at optimal temperatures. The solar panel is used for generating electricity to power the router or to charge the built-in battery when the lighting conditions allow. Wind energy generators are used to generate electricity in environments where there is sufficient wind power, as well as for powering or charging. The solar panel is arranged on the top or the side surface of the router, so that the maximum light receiving is ensured. The wind energy generator may be mounted outside or on top of the router, the location depending on the wind conditions of the installation environment.
In a further embodiment, the router may further include: the user interface and remote management tool are used for allowing a user or administrator to remotely set energy use preferences, monitor energy use states and adjust network configuration. The user interface and remote management tool provide services through the network communication module 110, which can be accessed by a user through any network-connected device (e.g., smart phone, tablet, etc.). The user interface is simple and visual in design, and ensures that a user can easily access all necessary functions including energy efficiency control, power management, network flow monitoring and system maintenance updating. By the mode, the performance and the energy consumption of the router can be effectively optimized by the user and the administrator, and the economical efficiency and the environmental protection of the router are further improved.
In a further embodiment, the adaptive energy management system 130 incorporates edge computing capabilities so that the deep learning model described above can be deployed directly on a router chip, enabling localized real-time decisions. Compared with the method that the decision model is deployed at the cloud, the localization decision can greatly reduce decision delay and improve response speed; the network transmission energy consumption is avoided, and the overall power consumption is further reduced; the privacy protection capability is improved, and real-time data does not need to be uploaded to the cloud.
Example two
As shown in fig. 4, the embodiment of the invention further provides a working method of the low-power consumption router integrated with energy management, which comprises the following steps:
Step S1: processing the incoming and outgoing network data through the network communication module 110 and obtaining real-time network traffic data;
step S2: acquiring real-time grid condition data by the power monitoring sensor 120;
Step S3: determining, by the adaptive energy management system 130, an energy consumption pattern from the real-time network flow data and the real-time grid condition data;
Step S4: switching to the determined energy consumption mode by the mode switching performing module 140;
step S5: when the energy consumption mode is the battery power mode, the built-in battery is controlled to discharge by the battery management system 150 to power the router.
As shown in fig. 5, in some embodiments, step S3 specifically includes:
Step S31: training a deep learning model by analyzing historical data comprising historical network flow data and historical power grid condition data by adopting a deep learning technology;
step S32: based on a trained deep learning model, predicting a router energy demand change trend and a power grid condition change trend in a period of time in the future;
step S33: and determining an energy consumption mode according to the prediction result of the deep learning model.
As shown in fig. 6, in some embodiments, steps S31 to S33 specifically include:
Step S311: the data collection and preprocessing module 131 collects the history data and performs preprocessing such as cleaning and standardization to obtain preprocessed history data.
Specifically, historical data is first collected from the network communication module 110 and the power monitoring sensor 120. The history data may include: historical network flow data, historical grid condition data, power consumption history data of routers, and historical time-consuming power demand information.
Where historical network traffic data refers to the amount of data transmitted through a network device (e.g., router) including sending and receiving rates, time stamps, packet size, number of packets, data type, source and destination addresses, session information, etc. By monitoring the network traffic data, the adaptive energy management system 130 can obtain the intensity of the current network activity, thereby determining how much energy the router needs to devote to maintaining efficient operation. The sending rate and the receiving rate are used to describe the speed of data transmission, i.e. the amount of data transmitted per second. A time stamp for each exact point in time of the data record, for tracking the time of transmission and reception of the data packets. The packet size indicates the specific number of bytes per packet transmitted. The data type indicates the type of data transfer, such as HTTP request, FTP transfer, etc. The source address and the destination address are network addresses indicating the transmission start point and the reception end point of the data packet. The number of packets refers to the total number of packets that have passed through the network at a particular time. Session information refers to network connection details, such as duration, etc., within a specific time period.
The power consumption history data of the router refers to a record of the power consumption of the router in a past period of time, and the power consumption is recorded in time sequence. The power consumption history data of the router helps the system learn the energy usage patterns of the router under different network loads and grid conditions, thereby predicting future energy demands and optimizing energy consumption patterns.
Wherein the grid condition data includes grid voltage and ac frequency. Grid voltage refers to the voltage value provided by the grid, and frequency is the ac frequency of the power supply system, for example, 50 Hz or 60 Hz. Monitoring the grid voltage and frequency helps the adaptive energy management system 130 evaluate the stability and quality of the grid. In case of voltage or frequency anomalies, it is necessary to adjust the energy use or to switch to a backup power supply, for example battery powered.
In a further embodiment, the grid condition data may further include:
The harmonic content of the power grid, the harmonic refers to the non-fundamental component existing in the power grid and is usually generated by nonlinear loads. Excessive harmonics can cause problems such as equipment heating, interference, etc. The quality of the power grid can be judged by monitoring the harmonic content.
Grid flicker refers to short-time anomalies in voltage, such as transient over-voltages or under-voltages. Frequent flicker can affect the proper operation of the device. Monitoring flicker may evaluate the transient stability of the grid.
The power supply interruption record of the power grid, statistics of the times, duration and the like of the power supply interruption can evaluate the reliability of the power grid and provide for switching the standby power supply.
The electric energy quality event records electric energy quality events such as voltage sag, voltage interruption and the like, analyzes the occurrence frequency and reasons of the electric energy quality events, and provides basis for improving the quality of a power grid.
The load level of the power grid is monitored, the load level and the change trend of the whole power grid are monitored, the potential overload risk of the power grid can be predicted, and load shedding measures can be adopted in advance.
The grid fault information may obtain information such as the location, type, expected recovery time, etc. of the grid fault from the grid company, and provide a reference for the adaptive energy management system 130.
Weather and environmental data, weather and environmental conditions such as lightning, low temperature, etc. can affect the operating conditions of the grid.
Wherein the historical time-period power demand information refers to data collected from the history regarding power demands of routers of different time periods. This includes daily, weekend, holiday or seasonal power usage, such information reflecting the pattern of power usage over a particular period of time.
The collected data is then subjected to preprocessing such as washing and normalization to facilitate subsequent analysis. For example, outliers are filtered out, normalized to match the input requirements of the deep learning model.
Step S312: the pre-processed historical data is used to train the deep learning model by the deep learning model training module 132.
In particular, a suitable deep learning model is designed, such as a convolutional neural network or a recurrent neural network, depending on the timing characteristics and complexity of the data. Training a deep learning model by using a historical data set, so that the deep learning model learns how to predict future energy demands and potential problems according to past network behaviors and power conditions.
Historical data sets refer to data accumulated over a period of time. The specific contents of the historical dataset include: network traffic data including the sending and receiving rates, time stamps, packet sizes, data types, source and destination addresses for each point in time in the history. The router power consumption history data refers to router power consumption amounts recorded at different time points. Historical grid condition data, including grid voltage and ac frequency. Historical time-lapse power demand information including the power demand amounts of the historic syncs, including power usage peaks and valleys divided by time periods.
The training process of the deep learning model specifically comprises the following steps:
step A: and (5) preprocessing data.
Prior to starting training the model, a pre-processing of the historical dataset is first required, which includes: removing or correcting errors and missing values in the data; selecting features from the data that facilitate prediction, such as a sending rate, a receiving rate, a data packet size, a data type, etc.; normalizing or normalizing the data (e.g., normalizing grid voltage and power demand to a range) to avoid affecting model performance due to excessive variation in variable scale; if a time series model such as a recurrent neural network is used, it is necessary to ensure that the data format is suitable for time series analysis, for example by constructing hysteresis features (e.g. power consumption of the previous hour).
And (B) step (B): model selection and model architecture design.
An appropriate model is selected based on the characteristics of the data. The time series data (e.g., grid data and network traffic) is suitably used with a recurrent neural network or variant thereof. The model architecture design is performed, which includes determining the number of layers of the model, the number of nodes per layer, activation functions, and so forth. For example, long Short-Term Memory networks (LSTM) may be selected to address time-series dependency issues. Super parameters such as learning rate, batch size, training cycle number, etc. are set.
Step C: and (5) model training.
Once the model design is completed and the super parameters are set, the actual training of the model is performed next. The data set is divided into a training set for learning the model, a validation set for adjusting the model parameters, and a test set for final performance evaluation. The training set is used to train the model by back propagation, gradient descent, and the like. During the training process, the generalization capability of the model is checked by using the verification set regularly, so that overfitting is avoided. The hyper-parameters are adjusted based on the performance of the validation set, such as adjusting network architecture or optimizer settings, to optimize model performance.
Step D: model evaluation and tuning.
After the model passes through a sufficient training period, the performance of the model is evaluated using a separate test set. It comprises the following steps: and evaluating indexes such as accuracy, recall rate, F1 score and the like of the model on the test set. Depending on the test results, it may be necessary to return to the model design stage to make adjustments to improve the model's performance on unseen data.
Step E: and (5) model deployment.
Once the model is sufficiently trained and validated, the final step is to deploy the model into the actual environment for real-time or periodic predictive tasks.
Step S321: the real-time prediction and decision making module 133 predicts real-time data including real-time network flow data and real-time power grid condition data by using the trained deep learning model to obtain a prediction result.
Specifically, the current collected real-time data is predicted by using a trained deep learning model, and the upcoming network load and the state of the power grid are estimated.
Specifically, the output of the deep learning model includes network load predictions and/or grid state predictions. Network load prediction is used to predict network usage, such as transmission and reception rates, over a future time period. Grid state prediction is used to predict the stability and frequency of the grid voltage and the total power demand.
Network load prediction, comprising: and a transmission rate prediction that predicts the rate of data transmission in the network over a future period of time, expressed in megabits per second. This may help to understand the expected upstream network usage. And a received rate prediction that predicts a rate of data reception in the network over a period of time in the future. This reflects the expected downstream network usage. Packet density predictions, which predict packet density in network traffic over a period of time in the future, are measured in packets per second.
Grid state prediction, comprising: grid voltage predictions that predict grid voltage levels in volts over a period of time in the future. The prediction of the voltage level helps to determine the power supply stability and possible voltage fluctuations of the power grid. And predicting the power grid frequency in hertz in the future. The stability of frequency is an important indicator of the health of the power system, and predicting its variation is critical to ensure stable operation of the power system. Total power demand predictions, which predict total power demand over a period of time in the future, are measured in kilowatt-hours or megawatts.
Step S331: an energy consumption pattern is determined based on the prediction result.
Specifically, based on the prediction result, the adaptive energy management system 130 determines the most appropriate energy consumption pattern. For example, if a high network load is predicted and the grid is stable, a decision is made to operate in full power mode; if a low network load is predicted and the grid is unstable, switching to a power saving mode or using built-in batteries for power. For example, if the prediction shows that the network load will be high for a future period of time while the grid conditions are stable (voltage and frequency are in the normal range, power supply is sufficient), then the full power mode is selected. The router will operate at maximum capacity to handle high data traffic, ensuring that network performance is not impacted. If the predicted network load is low and the grid voltage is unstable or frequency is abnormal, a power saving mode is selected or built-in battery power supply is started. Thus, the dependence on an unstable power grid can be reduced, and the energy consumption is reduced when the network usage is low. Assuming that in the morning of a workday, the deep learning model predicts that there will be a peak of network usage in the afternoon based on past data, while the grid will remain stable. The adaptive energy management system 130 thus pre-adjusts the router to full power mode, ensuring that large amounts of data can be processed seamlessly during peak periods. In the opposite case, if the grid pressure is predicted to be greater on weekends while the network load is predicted to be lower, the adaptive energy management system 130 decides to enable the energy saving mode, reduce power consumption, or switch to battery powered during periods of least stable grid to avoid the effects of power fluctuations on the device.
The step S4 may specifically include: the router operating state is adjusted to execute the energy consumption mode by the mode switching execution module 140, and the execution effect is fed back to the deep learning model.
Specifically, according to the energy consumption mode, the system adjusts the working state of the router through its hardware interface. This may include adjusting the frequency of the central processor, managing the strength of the wireless signal, activating or deactivating certain unnecessary hardware modules, etc.
After the execution is finished, the system monitors the execution effect of the energy consumption mode and feeds the execution effect back to the deep learning model. This facilitates self-tuning and optimization of the deep learning model in future predictions.
Example 1: energy management during high network loads is handled.
Assuming that the system predicts that there will be a significant amount of videoconferencing activity during the afternoon, this will result in a significant increase in network load. To address this challenge, the adaptive energy management system 130 takes the following actions: to ensure fast processing and efficient routing of data packets, the system increases the operating frequency of the central processing unit, thereby increasing processing power to cope with high loads. In environments with more users, the system increases the transmit power of the wireless signal in order to improve wireless network coverage and connection quality. Activating all network interfaces, turning on all ethernet and wireless interfaces, ensuring that all network channels are available for transmitting data.
Example 2: low network loads and grid instability should be addressed.
On a weekend, the forecast data shows that network usage will be low while grid voltage fluctuates frequently. In this case, the adaptive energy management system 130 takes the following measures: because the network load is lower, the system reduces the operating frequency of the central processing unit to reduce energy consumption. The transmitting power of the wireless signals is reduced, the method is suitable for lower network use requirements, and meanwhile, the energy consumption is reduced. Turning off unnecessary hardware modules, such as multiple radio bands (e.g., 2.4 GHz and 5 GHz) are running, may temporarily turn off the 5 GHz band because it generally consumes more power and does not have to run at full power when network loads are low.
Example 3: the night mode of operation is optimized.
At night, network activity is expected to decrease and power costs may be lower. The adaptive energy management system 130 may be adapted such that: the night energy saving mode is enabled, and the operation strategy is adjusted according to low-cost power provided by the power grid, such as reducing the operation frequency of a processor, closing unnecessary interfaces and the like, and simultaneously ensuring that critical tasks (such as safety monitoring and the like) are continuously operated. The advantages of low use of the night network and low power cost are utilized to carry out data backup and system maintenance work, and the operations are avoided during daytime peak hours.
Real-time feedback is provided to a user or administrator via a user interface regarding energy management decisions and current system status. The user may view the energy consumption report or adjust preset energy management preference settings.
The self-adaptive energy management algorithm realizes accurate energy consumption adjustment by using a deep learning technology, maximizes the energy use efficiency of the router through continuous data analysis and real-time system reaction, and ensures the optimal performance and the lowest energy consumption under various power grids and network conditions.
As shown in fig. 7, in some embodiments, step S5 specifically includes:
step S51: continuously collecting battery state data including voltage, current, temperature and state of charge of the built-in battery through the battery state monitoring module 151; these monitoring data are continuously collected and sent to the energy management system for real-time analysis.
Step S52: the charge rate or charge cycle is adjusted by the intelligent charging algorithm based on the battery state data by the charge control module 152. Specifically, the step is based on the collected battery state data, and the battery management system adjusts the charging rate or the charging period through an intelligent charging algorithm to optimize the charging process of the battery so as to prolong the service life of the battery and prevent overcharge or overdischarge. If the router integrates solar or wind energy modules, the battery management system would prefer to use these renewable energy sources for charging, reducing reliance on the grid.
Step S53: when the energy consumption mode is a battery power supply mode, the built-in battery is controlled to discharge by the discharging control module 153 to supply power to the router, and the discharging power or the discharging rate is determined according to the router load demand and the battery state data; specifically, through a discharge control module, when the power supply of an external power grid is interrupted or unstable, a built-in battery is controlled to discharge to supply power for a router, and the discharge power or the discharge rate is determined according to the load demand of the router and the battery state data; in the event of a grid power outage or instability, the battery management system controls the battery discharge to supply the necessary power to maintain router operation. The intelligent algorithm adjusts the discharge rate to ensure that the power output meets the device requirements without unduly draining the battery.
Step S54: the battery health and performance monitoring module 154 periodically evaluates the health of the built-in battery based on the battery status data and/or detects whether the performance of the built-in battery is abnormal based on a preset battery parameter safety threshold. The battery management system periodically evaluates the health of the battery and predicts its remaining life and performance degradation. Upon detection of any performance anomalies, maintenance personnel are notified to check or replace the battery. All monitoring data and system alarms can be accessed through the user interface enabling users and administrators to view battery status and historical performance.
In some embodiments, the method of operation further comprises:
the renewable energy source module is used for generating electricity by using renewable energy sources, and the electric energy is used for supplying power to the router or is stored in the built-in battery. Renewable energy sources include solar and/or wind energy. The energy consumption modes include a full power mode, a normal mode, a power saving mode, an ultra low power consumption mode, and a battery powered mode.
In a further embodiment, the method of operation further comprises the steps of:
The user can configure specific energy consumption modes and parameters through interface customization, for example, the user can automatically switch to the energy saving mode in a specific time period. The energy consumption mode switching system provides feedback of the current energy consumption mode and equipment state to the user in real time, wherein the feedback comprises energy consumption data and performance indexes.
The beneficial technical effects of the technical scheme include:
The self-adaptive energy management system adopts a deep learning technology to analyze historical data and forecast future energy demands and power grid anomalies, so that the router can automatically adjust an energy consumption mode according to a forecast result. Such prediction-based adaptive tuning can significantly improve energy efficiency, particularly in terms of energy costs and environmental impact.
The design of the battery management system enables the power supply of the power grid to be unstable or the power to be cut off, the power supply mode of the power grid can be automatically switched to, the discharging power is intelligently controlled, and the router can still work stably under different load demands. This enhances the continuous operation capability of the system, reducing the risk of service interruption due to power problems.
The battery state monitoring module and the battery health and performance monitoring module can continuously monitor key parameters of the battery and evaluate the health condition of the battery. The charging strategy is intelligently adjusted through the charging control module, so that the charging process can be optimized, and the aging rate of the battery is reduced, thereby prolonging the service life of the battery and improving the economic benefit of the whole system.
The real-time prediction and decision making module allows the system to quickly respond to real-time data changes by using a trained deep learning model, and automatically adjusts the energy consumption mode. The automated decision making reduces the need for human intervention and improves the efficiency and response speed of the operation.
By optimizing energy use and reducing reliance on conventional grids, the router is capable of reducing energy consumption and operating costs, while also reducing environmental pollution. Such routers can provide a greener, more sustainable network communication solution, especially with renewable energy and battery powered.
Example III
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the working method of any one of the above-mentioned low-power routers integrated with energy management.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. Of course, there are other ways of readable storage medium, such as quantum memory, graphene memory, etc. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
Example IV
The invention also provides electronic equipment. The electronic equipment of the embodiment of the invention comprises: one or more processors; a storage device for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement a method of:
Acquiring real-time network flow data and real-time power grid condition data;
Determining an energy consumption mode according to the real-time network flow data and the real-time power grid condition data;
control switches the router to the determined energy consumption mode.
In some embodiments, in the processing of the processor, determining the energy consumption mode according to the real-time network traffic data and the real-time grid condition data specifically includes:
Training a deep learning model by analyzing historical data including historical network flow data and historical power grid condition data by adopting a deep learning technology;
based on a trained deep learning model, predicting a router energy demand change trend and a power grid condition change trend in a period of time in the future;
And determining an energy consumption mode according to the prediction result of the deep learning model.
In some embodiments, in the processing of the processor, determining the energy consumption mode according to the real-time network traffic data and the real-time grid condition data specifically includes:
Acquiring historical data, and performing preprocessing such as cleaning, standardization and the like to obtain preprocessed historical data;
training a deep learning model by using the preprocessed historical data;
Predicting real-time data including real-time network flow data and real-time power grid condition data by using the trained deep learning model to obtain a prediction result;
An energy consumption pattern is determined based on the prediction result.
Referring now to FIG. 8, there is illustrated a schematic diagram of a computer system 800 suitable for use in implementing an electronic device of an embodiment of the present invention. The electronic device shown in fig. 8 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the invention.
As shown in fig. 8, the computer system 800 includes a Central Processing Unit (CPU) 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data required for the operation of the computer system 800 are also stored. The CPU801, ROM 802, and RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, mouse, etc.; an output portion 807 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 808 including a hard disk or the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. The drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 810 as needed, so that a computer program read out therefrom is installed into the storage section 808 as needed.
In particular, the processes described in the main step diagrams above may be implemented as computer software programs according to the disclosed embodiments of the invention. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the main step diagrams. In the above-described embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The above-described functions defined in the system of the present invention are performed when the computer program is executed by the central processing unit 801.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, a computer readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with computer readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present invention may be implemented in software or in hardware. The units described may also be provided in a processor, the names of these units in some cases not constituting a limitation of the unit itself.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (6)
1. A low power router for integrated energy management, comprising: the system comprises a network communication module, a power monitoring sensor, an adaptive energy management system, a mode switching execution module and a battery management system;
The network communication module is used for processing all the in-out network data and acquiring real-time network flow data; the network flow data refers to the data quantity transmitted by a router, and comprises a sending rate, a receiving rate, a time stamp, a data packet size, the number of data packets, a data type, a source address, a destination address and session information;
the power monitoring sensor is used for acquiring real-time power grid condition data; the real-time grid condition data includes: grid voltage, grid current, grid frequency and grid power factor;
The self-adaptive energy management system is used for determining an energy consumption mode according to the real-time network flow data and the real-time power grid condition data;
the mode switching execution module is used for switching to the determined energy consumption mode;
The battery management system is used for controlling the discharge of the built-in battery to supply power for the router when the energy consumption mode is a battery power supply mode;
The self-adaptive energy management system is specifically used for training a deep learning model by analyzing historical data by adopting a deep learning technology, predicting the router energy demand change trend and the power grid condition change trend in a period of time in the future based on the deep learning model, and determining an energy consumption mode according to the prediction result of the deep learning model; wherein the historical data comprises historical network flow data and historical grid condition data;
The adaptive energy management system includes:
The data collection and preprocessing module is used for collecting historical data and preprocessing the historical data including cleaning and standardization to obtain preprocessed historical data;
The deep learning model training module is used for training a deep learning model by using the preprocessed historical data;
the real-time prediction and decision making module is used for predicting real-time data including real-time network flow data and real-time power grid condition data by using the trained deep learning model to obtain a prediction result, and determining an energy consumption mode based on the prediction result;
the mode switching execution module is specifically configured to adjust a working state of the router to execute the energy consumption mode;
The energy consumption mode comprises a plurality of modes of a full power mode, a normal mode, an energy saving mode, an ultra-low power consumption mode and a battery power supply mode; the real-time prediction and decision making module is specifically configured to:
if the prediction result indicates that the network flow rate is higher than a preset high flow rate threshold value in a future period of time and the power grid parameters corresponding to the predicted power grid condition change trend are all in a normal range, determining that the energy consumption mode is a full power mode;
If the prediction result indicates that the network flow in a period of time in the future is lower than a preset low flow threshold, determining that the energy consumption mode is an energy saving mode;
If the prediction result indicates that the network flow is between a preset high flow threshold and a preset low flow threshold in a future period of time and the power grid parameter corresponding to the predicted power grid condition change trend is stable in a normal range, determining that the energy consumption mode is a normal mode;
if the prediction result indicates that the network flow is close to zero in a future period of time, determining that the energy consumption mode is an ultra-low power consumption mode;
If the prediction result indicates that abnormal fluctuation or interruption of the power grid parameters occurs in a period of time in the future, determining that the energy consumption mode is a battery power supply mode;
If the prediction result indicates that the network flow and the network condition are to be kept at the current level for a period of time in the future, the current energy consumption mode is kept unchanged;
the mode switching execution module is specifically used for switching to a determined energy consumption mode through controlling hardware setting;
in the full power mode, the mode switching execution module sets the clock frequency of the central processing unit as the highest main frequency, opens all wireless radio frequency transmitting channels and works at the maximum power, enables all wired network ports to work at the highest speed and enables other peripheral devices to keep working states;
In the normal mode, the mode switching execution module dynamically adjusts the frequency of the central processing unit according to the network flow, selectively closes part of wireless radio frequency emission channels according to the connection condition of wireless equipment, adjusts the port work rate according to the use condition of a wired network and closes an idle peripheral equipment power supply;
in the energy-saving mode, the mode switching execution module reduces the clock frequency of the central processing unit to the lowest working frequency, closes part of wireless radio frequency emission channels, reserves a 2.4G channel and a 5G channel, limits the highest speed of a wired network port to be below 100Mbps, and closes all unnecessary peripheral equipment power supplies;
Under the ultra-low power consumption mode, the mode switching execution module switches the central processing unit to the lowest idle mode, closes all wireless radio frequency emission channels, closes all wired network ports and closes all peripheral equipment power supplies;
In the battery-powered mode, the mode switching execution module performs the following operations: cutting off a power supply circuit of an external power line; enabling a battery power management circuit; adjusting a hardware working mode to prolong the battery endurance time; enabling battery power monitoring; and dynamically adjusting the hardware working mode according to the real-time electric quantity condition of the battery.
2. The integrated energy management low power router of claim 1, wherein the battery management system comprises:
the battery state monitoring module is used for continuously collecting battery state data including the voltage, the current, the temperature and the state of charge of the built-in battery;
The charging control module is used for adjusting the charging rate or the charging period through an intelligent charging algorithm according to the battery state data;
The discharging control module is used for controlling the built-in battery to discharge to supply power for the router when the energy consumption mode is a battery power supply mode, and determining discharging power or discharging rate according to the load requirement of the router and the battery state data;
And the battery health and performance monitoring module is used for periodically evaluating the health condition of the built-in battery according to the battery state data and/or detecting whether the performance of the built-in battery is abnormal or not according to a preset battery parameter safety threshold.
3. The integrated energy managed low power router of claim 1, wherein the adjusting the hardware operating mode to extend battery life comprises:
reducing the CPU clock frequency to a moderate level;
Closing part of the wireless radio frequency channels and reserving 2.4G channels or 5G channels;
limiting the operation rate of the wired network port to be below 100 Mbps; and powering down the optional peripheral device.
4. The integrated energy management low power router of claim 1, wherein the dynamically adjusting the hardware operating mode according to the real-time power condition of the battery comprises:
When the battery power is higher than a preset first power threshold, the battery power is considered to be sufficient, and the frequency of the central processing unit and the wireless radio frequency power are improved;
when the battery power is lower than a preset second power threshold, the battery is regarded as power shortage, and the hardware power consumption is reduced.
5. The integrated energy managed low power router of claim 1, further comprising a renewable energy module for generating electricity from renewable energy, the generated electricity being either powering the router or stored in a built-in battery of the router; the renewable energy source module comprises a solar panel and/or a wind energy generator, wherein the solar panel is arranged on the top or the side surface of the router, and the wind energy generator is arranged on the outside or the top of the router;
The battery management system is positioned at the bottom of the router and is connected with the energy management system and the renewable energy module through cables.
6. The integrated energy management low power consumption router of claim 1, wherein the network communication module employs a network interface controller comprising: the network processor is used for receiving and transmitting the data packet; the hardware counter is used for monitoring the incoming and outgoing data packets in real time, and carrying out statistics and analysis on the data packets to obtain real-time network flow data;
the power monitoring sensor adopts a Hall effect sensor, a voltage transformer or a power quality analyzer.
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