Meteorological station intelligent power supply remote control system based on internet of things

Abstract

The application discloses a meteorological station intelligent power supply remote control system based on Internet of Things and belongs to the technical field of meteorological station power supply management. The system comprises a cloud platform and a device end. The cloud platform acquires meteorological station information corresponding to each device end, sets a digital twin model based on the meteorological station information, performs weather prediction according to meteorological monitoring data transmitted by the meteorological station, obtains weather prediction data and a confidence level of a corresponding prediction time, determines an effective prediction period according to the confidence level, performs power generation prediction on the meteorological station, obtains power generation prediction data, performs simulation analysis on the digital twin model, obtains a power supply control mode of the meteorological station, generates a corresponding remote power supply control instruction according to the power supply control mode, sends the remote power supply control instruction to the device end of the corresponding meteorological station, performs weather monitoring on the device end, obtains meteorological monitoring data, sends the meteorological monitoring data to the cloud platform, and performs power supply control according to the received remote power supply control instruction.

Description

Technical Field

[0001] This invention belongs to the field of meteorological station power management technology, specifically an intelligent remote control system for meteorological station power based on the Internet of Things. Background Technology

[0002] In the long-term operation of IoT weather stations in the field without access to mains power, the reliability of the power management system directly determines the continuity of meteorological monitoring data and the overall lifespan of the equipment. It is the core foundation for ensuring the stable implementation of various environmental parameter collection, wireless transmission, and remote control functions. Currently, traditional weather stations mostly adopt a basic power supply architecture of solar energy combined with batteries. In practical applications, there is a common problem of crude energy efficiency management. On sunny days with sufficient sunlight, the electricity generated by the solar panels cannot be fully utilized. On cloudy or rainy days or in low-temperature and low-light conditions in autumn and winter, the batteries are prone to depletion, directly leading to the shutdown of monitoring equipment and the loss of key meteorological data. In complex scenarios such as high altitudes and remote mountainous areas, the meteorological data loss rate caused by such power instability has remained high every year, seriously affecting the accuracy of weather forecasts and the timeliness of disaster warning response. Most existing conventional power control solutions only support simple local on / off control and do not have cloud-based remote control capabilities. Maintenance personnel cannot monitor the power operation status of field sites in real time from the background. When encountering problems such as equipment crashes or branch circuit failures, they must drive to the site to manually restart the equipment. This not only significantly increases the maintenance costs in remote areas, but also makes it difficult to quickly restore the operation of monitoring equipment during critical periods such as flood season and extreme weather events.

[0003] In order to solve the above problems, this invention provides a remote control system for intelligent power supply of weather stations based on the Internet of Things. Summary of the Invention

[0004] To address the problems of the above solutions, this invention provides a remote control system for intelligent power supply of weather stations based on the Internet of Things.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A remote control system for intelligent power supply of weather stations based on the Internet of Things, including a cloud platform and device terminals;

[0007] Furthermore, the cloud platform establishes communication connections with each device.

[0008] The cloud platform includes a weather station information module, a weather forecast module, a power consumption analysis module, and a power management module;

[0009] The meteorological station information module is used to obtain meteorological station information corresponding to each device and set up a digital twin model based on the meteorological station information.

[0010] Furthermore, the digital twin model is updated based on meteorological station information.

[0011] The weather forecasting module is used to make weather forecasts based on the weather monitoring data transmitted by the weather station, obtain the weather forecast data and confidence level for the corresponding forecast time, and determine the effective forecast period based on the confidence level.

[0012] Furthermore, the effective prediction period is determined based on the confidence level, including:

[0013] Identify meteorological forecast data with a confidence level greater than a threshold X1, and form an effective forecast period based on the forecast time corresponding to the meteorological forecast data.

[0014] Furthermore, the effective prediction period is determined based on the confidence level, including:

[0015] Identify the various stakeholders who have application needs for meteorological monitoring data, and obtain the application needs of each stakeholder for meteorological monitoring data at the corresponding forecast time in real time.

[0016] Analyze the minimum confidence level required to meet each application requirement, and mark it as the potential confidence level of the requester;

[0017] The highest confidence level among all potential confidence levels is selected as the standard confidence level for the corresponding prediction time, denoted as τ. t t is time;

[0018] The confidence level of meteorological forecast data is denoted as τ´ t ;

[0019] Identify τ´ t ≥τ t The meteorological forecast data is used to form an effective forecast period based on the forecast time corresponding to the meteorological forecast data.

[0020] Furthermore, the potential confidence levels required to meet various application requirements are analyzed, including:

[0021] Based on historical data of application requirements, determine the minimum confidence level to meet the application requirements, and mark the minimum confidence level as the potential confidence level.

[0022] Furthermore, the potential confidence levels required to meet various application requirements are analyzed, including:

[0023] Step SA1: Determine the minimum confidence level to meet the application requirements based on historical data of the application requirements, and mark the minimum confidence level as the candidate confidence level;

[0024] Step SA2: Reduce the candidate confidence level to obtain a new candidate confidence level;

[0025] Step SA3: Identify, based on historical data, whether there are historical records showing that the meteorological forecast data with the candidate confidence level does not meet the application requirements;

[0026] When the meteorological forecast data with the candidate confidence level does not meet the historical records of the application requirements, the candidate confidence level is evaluated as not meeting the application requirements, the reduction of the candidate confidence level is cancelled, and the candidate confidence level after the reduction is cancelled is marked as a potential confidence level.

[0027] When there is no historical record of meteorological forecast data that does not meet the application requirements for the candidate confidence level, the maximum meteorological data deviation of the meteorological forecast data for the candidate confidence level is estimated. Based on the maximum meteorological data deviation and the meteorological forecast data, simulated meteorological monitoring data is estimated. Based on the simulated meteorological monitoring data and the meteorological forecast data, it is determined whether the application requirements are met. If the application requirements are met, return to step SA2. If the application requirements are not met, cancel the reduction of the candidate confidence level and mark the candidate confidence level after the reduction is canceled as a potential confidence level.

[0028] The power consumption analysis module is used to analyze the power consumption of the weather station, predict the power generation of the weather station, and obtain power generation prediction data; the digital twin model performs simulation analysis based on the effective prediction period, power generation prediction data and meteorological prediction data to obtain the power control mode of the weather station.

[0029] Furthermore, the digital twin model performs simulation analysis based on the effective forecast period, power generation forecast data, and meteorological forecast data, including:

[0030] Set simulation constraints, wherein the simulation constraints are that the meteorological monitoring data or meteorological forecast data meet the application requirements;

[0031] Based on the simulation constraints, the digital twin model is used to conduct simulation analysis according to the effective forecast period, power generation forecast data and meteorological forecast data to obtain various potential power control methods.

[0032] Statistically analyze the power generation utilization rate corresponding to potential power control methods, and obtain the confidence level τ´ of potential power management methods at the corresponding time. t and standard confidence level τ t ;

[0033] Calculate the meteorological satisfaction rate of potential power supply control methods;

[0034]

[0035]

[0036] In the formula: WL is the meteorological satisfaction rate, i represents the corresponding demander, i=1, 2, ..., n, and n is the number of demanders; This represents the weighting coefficient of the corresponding demand side; For a single demand function, t1 and t2 are the start and end times of the potential power control mode, respectively; clip[] is a cutoff function, indicating that the calculated value is limited to [0, 1]; This is the confidence limit;

[0037] The priority value of potential power source control methods is calculated based on meteorological satisfaction rate and power generation utilization rate;

[0038] The formula for calculating priority value is:

[0039] Priority value = meteorological weight coefficient × meteorological satisfaction rate + power generation weight coefficient × power generation utilization rate;

[0040] Select the highest priority potential power control method as the power control method for output.

[0041] Furthermore, .

[0042] The power management module is used to remotely manage the power supply of the weather station, obtain the power control mode of the weather station, generate corresponding remote power control commands according to the power control mode, and send the remote power control commands to the equipment terminal of the corresponding weather station.

[0043] Furthermore, the power management module has a manual control unit, which is used by staff to manually control the power supply of the weather station remotely.

[0044] The device includes a meteorological monitoring module and a power control module;

[0045] The meteorological monitoring module is used to perform meteorological monitoring, obtain meteorological monitoring data, and send the meteorological monitoring data to the cloud platform.

[0046] The power control module is used to control the power supply according to the received remote power control command.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] This invention effectively addresses the industry pain points of existing field IoT weather stations, such as extensive power management, poor power supply stability, and low operation and maintenance efficiency. Relying on IoT remote control and intelligent power regulation, it significantly optimizes the operation mode of field power supply for weather stations. This invention abandons the traditional fixed and single local on / off power supply control method, enabling remote monitoring and flexible control of the weather station's power system. Maintenance personnel can grasp the power operation status and intelligent management of the power supply without having to go to the field site, achieving reasonable planning of electrical energy and ensuring the stability of the weather station's function. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0051] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0052] like Figure 1 As shown, the IoT-based intelligent power remote control system for weather stations includes a cloud platform and device terminals.

[0053] The cloud platform communicates with various devices, which are located at various weather stations.

[0054] The cloud platform includes a weather station information module, a weather forecast module, a power consumption analysis module, and a power management module;

[0055] The meteorological station information module is used to obtain meteorological station information corresponding to each device, set up a digital twin model based on the meteorological station information, and update the digital twin model according to the meteorological station information.

[0056] Digital twin models need to be able to simulate the power consumption of a weather station under different operational requirements, such as simulating the power supply of different equipment within the weather station.

[0057] In one embodiment, weather stations can be classified, and corresponding digital twin models can be established based on the classification of weather stations, thereby improving the efficiency of establishing digital twin models.

[0058] In one embodiment, if the simulation accuracy meets the standard, the same digital twin model is used for the same type of weather station. In subsequent simulations, adjustments are made based on the actual weather station data, that is, the equipment remains unchanged, but parameters such as the remaining power are adjusted.

[0059] The weather forecasting module is used to make weather forecasts based on the weather monitoring data transmitted by the weather station, and to obtain the weather forecast data and confidence level for the corresponding forecast time.

[0060] Determining the effective forecast period based on confidence level indicates that meteorological forecast data can be used for application analysis within the effective forecast period. Meteorological stations may choose not to collect meteorological monitoring data within the effective forecast period.

[0061] In one embodiment, weather forecasting based on meteorological monitoring data can be conducted using current, mature weather forecasting technologies to obtain forecast data and confidence levels. For example, weather forecasting models can be built using machine learning and deep learning algorithms for predictive analysis.

[0062] In one embodiment, determining the effective prediction period based on confidence level includes:

[0063] Identify meteorological forecast data with a confidence level greater than a threshold X1, and form an effective forecast period based on the forecast time corresponding to the meteorological forecast data.

[0064] In one embodiment, considering changes in the application needs of meteorological monitoring data, which may lead to changes in the confidence requirements of the relevant demanders for the meteorological monitoring data, the effective forecast period is determined based on the confidence level, including:

[0065] Identify the various stakeholders who have application needs for meteorological monitoring data, and obtain the application needs of each stakeholder for meteorological monitoring data at the corresponding forecast time in real time.

[0066] Analyze the minimum confidence level required to meet each application requirement, and mark it as the potential confidence level of the requester;

[0067] The highest confidence level among all potential confidence levels is selected as the standard confidence level for the corresponding prediction time, denoted as τ. t t is time;

[0068] The confidence level of meteorological forecast data is denoted as τ´ t ;

[0069] Identify τ´ t ≥τ t The meteorological forecast data is used to form an effective forecast period based on the forecast time corresponding to the meteorological forecast data.

[0070] In one embodiment, analyzing the potential confidence levels required to meet various application requirements includes:

[0071] The minimum confidence level required to meet the application requirements is determined based on historical data of the corresponding application requirements and marked as the potential confidence level.

[0072] In one embodiment, analyzing the potential confidence levels required to meet various application requirements includes:

[0073] Step SA1: Determine the minimum confidence level that satisfies the application requirements based on historical data of the corresponding application requirements, and mark it as the candidate confidence level;

[0074] Step SA2: Reduce the candidate confidence level to obtain a new candidate confidence level. The reduction can be made according to a preset minimum reduction value, such as 0.1, 0.2, etc.

[0075] Step SA3: Identify, based on historical data, whether there are historical records showing that the meteorological forecast data with the candidate confidence level does not meet the application requirements;

[0076] When a historical record of meteorological forecast data with the candidate confidence level is identified as not meeting the application requirements, the candidate confidence level is evaluated as not meeting the application requirements, the reduction of the candidate confidence level is cancelled (i.e., this reduction), and the candidate confidence level after the reduction is cancelled is marked as a potential confidence level.

[0077] When a historical record of meteorological forecast data without the candidate confidence level is identified as not meeting the application requirements, the maximum meteorological data deviation of the candidate confidence level meteorological forecast data is estimated, i.e., the maximum possible data deviation between the candidate confidence level and the actual meteorological monitoring data; based on the maximum meteorological data deviation and the meteorological forecast data, simulated meteorological monitoring data (assuming meteorological monitoring data monitored by a meteorological station) is estimated; based on the simulated meteorological monitoring data and the meteorological forecast data, it is determined whether the analytical accuracy requirements of the application requirements are met, and thus whether the application requirements are met; if the application requirements are met, return to step SA2; if the application requirements are not met, the reduction of the candidate confidence level is canceled (i.e., this reduction), and the candidate confidence level after the reduction is canceled is marked as a potential confidence level.

[0078] If 0.8→0.79 meets the application requirements, and 0.79→0.78 does not meet the application requirements, then the decrease from 0.79→0.78 is cancelled, and 0.79 is regarded as the potential confidence level.

[0079] The power consumption analysis module is used to analyze the power consumption of the weather station, predict the power generation of the weather station, and obtain power generation prediction data; the digital twin model performs simulation analysis based on the effective prediction period, power generation prediction data and meteorological prediction data to obtain the power control mode of the weather station.

[0080] In one embodiment, power generation forecasting is performed on a weather station based on historical power generation data and meteorological data. Existing power generation forecasting technologies, such as various solar power generation forecasting models, can be used, or a forecasting system can be built independently based on machine learning, deep learning algorithms, etc.

[0081] In one embodiment, a digital twin model performs simulation analysis based on the effective forecast period, power generation forecast data, and meteorological forecast data, including:

[0082] Meteorological data collection requirements are set to allow meteorological stations to refrain from meteorological monitoring during the effective forecast period, i.e., meteorological forecast data is used for analysis instead. This allows the power supply to meteorological station equipment, such as the corresponding data collection devices, to be stopped during this period, thus optimizing the simulation limits. Under the meteorological data collection requirements, a digital twin model is used to perform simulation analysis based on the effective forecast period, power generation forecast data, and meteorological forecast data to obtain the optimal power control method.

[0083] For example, a digital twin model is used to traverse various potential power control methods in the simulation, and then these methods are prioritized. The highest-priority potential power control method is selected as the applied power control method. Prioritization can be based on factors such as the stability and effectiveness of meteorological monitoring at the weather station.

[0084] In one embodiment, a digital twin model performs simulation analysis based on the effective forecast period, power generation forecast data, and meteorological forecast data, including:

[0085] The simulation constraints are set so that the meteorological monitoring data or meteorological forecast data meets the application requirements. That is, either there is collected meteorological monitoring data, or it is in the effective forecast period. If it is not in the effective forecast period, the collection and transmission of meteorological monitoring data need to be guaranteed. If it is in the effective forecast period, the power supply of the collection equipment can be eliminated so that the meteorological station can play its role.

[0086] Based on the simulation constraints, the digital twin model is used to conduct simulation analysis according to the effective forecast period, power generation forecast data and meteorological forecast data to obtain various potential power control methods.

[0087] Assess the power generation utilization rate corresponding to potential power control methods, calculating the utilization rate based on meteorological station power generation, storage capacity, and wasted electricity; determine the confidence level τ´ of potential power management methods at the corresponding time. t The confidence level for meteorological monitoring data can be set to 1, or it can be determined based on the average actual error. The confidence level for meteorological forecast data is determined according to the confidence level in the above embodiment. The standard confidence level τ for each demander at the corresponding time is obtained. t ;

[0088] Calculate the meteorological satisfaction rate of potential power supply control methods;

[0089]

[0090]

[0091] In the formula: WL is the meteorological satisfaction rate, i represents the corresponding demander, i=1, 2, ..., n, and n is the number of demanders; This represents the weighting coefficient of the corresponding demand side, which is set according to the importance of each demand side. For example, it can be set according to the proportion of the demand side's application of meteorological monitoring data, or the weighting coefficient can be determined from the proportion of economic losses caused by the distortion of meteorological monitoring data. For a single demand function, t1 and t2 are the start and end times of the potential power control mode, respectively; clip[] is a cutoff function, indicating that the calculated value is limited to [0, 1]; This is the confidence limit, which means that the data is considered to have reached its best performance when the confidence limit is reached. The common value is 1, which is the full confidence value. It can also be reduced to values ​​such as 0.95 or 0.93, and is generally greater than 0.9.

[0092] The priority value of potential power source control methods is calculated based on meteorological satisfaction rate and power generation utilization rate;

[0093] The formula for calculating priority value is:

[0094] Priority value = meteorological weight coefficient × meteorological satisfaction rate + power generation weight coefficient × power generation utilization rate;

[0095] The meteorological weight coefficient and power generation weight coefficient can be set according to the user's emphasis on the importance of meteorology and power generation, and different weight coefficients can be set for different scenarios. The meteorological weight coefficient + power generation weight coefficient = 1, and can be set according to user needs, such as 0.6 or 0.4.

[0096] Select the highest priority potential power control method as the power control method for output.

[0097] The power management module is used to remotely manage the power supply of the weather station, obtain the power control mode of the weather station, generate corresponding remote power control commands according to the power control mode, and send the remote power control commands to the equipment terminal of the corresponding weather station.

[0098] In one embodiment, the power management module has a manual control unit, which is used by staff to remotely control the power supply of the weather station, such as starting and stopping it.

[0099] The device includes a meteorological monitoring module and a power control module;

[0100] The meteorological monitoring module is used to perform meteorological monitoring, obtain meteorological monitoring data, and send the meteorological monitoring data to the cloud platform.

[0101] The power control module is used to control the power supply according to the received remote power control command.

[0102] Information such as the remaining power and power generation data of the weather station is collected and transmitted through other means.

[0103] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0104] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A remote control system for intelligent power supply of a weather station based on the Internet of Things, characterized in that, This includes both cloud platforms and device-side components; The cloud platform includes a weather station information module, a weather forecast module, a power consumption analysis module, and a power management module; The weather station information module is used to obtain weather station information corresponding to each device and set up a digital twin model based on the weather station information; The meteorological forecasting module is used to make meteorological forecasts based on meteorological monitoring data transmitted by the meteorological station, obtain meteorological forecast data and confidence levels for the corresponding forecast time, and determine the effective forecast period based on the confidence level. The electricity consumption analysis module is used to analyze the electricity consumption of the meteorological station, predict the power generation of the meteorological station, and obtain power generation prediction data. The power control mode of the weather station is obtained by simulation analysis based on the effective forecast period, power generation forecast data and meteorological forecast data using a digital twin model; The power management module is used to remotely manage the power supply of the weather station, obtain the power control mode of the weather station, generate corresponding remote power control commands according to the power control mode, and send the remote power control commands to the equipment terminal of the corresponding weather station. The device includes a meteorological monitoring module and a power control module; The meteorological monitoring module is used to perform meteorological monitoring, obtain meteorological monitoring data, and send the meteorological monitoring data to the cloud platform. The power control module is used to control the power supply according to the received remote power control command.

2. The IoT-based intelligent power remote control system for weather stations according to claim 1, characterized in that, The digital twin model is updated based on weather station information.

3. The IoT-based intelligent power remote control system for weather stations according to claim 1, characterized in that, The effective prediction period is determined based on the confidence level, including: Identify meteorological forecast data with a confidence level greater than a threshold X1, and form an effective forecast period based on the forecast time corresponding to the meteorological forecast data.

4. The IoT-based intelligent power remote control system for weather stations according to claim 1, characterized in that, The effective prediction period is determined based on the confidence level, including: Identify the various stakeholders who have application needs for meteorological monitoring data, and obtain the application needs of each stakeholder for meteorological monitoring data at the corresponding forecast time in real time. Analyze the minimum confidence level required to meet each application requirement, and mark it as the potential confidence level of the requester; The highest confidence level among all potential confidence levels is selected as the standard confidence level for the corresponding prediction time, denoted as τ. t t is time; The confidence level of meteorological forecast data is denoted as τ´ t ; Identify τ´ t ≥τ t The meteorological forecast data is used to form an effective forecast period based on the forecast time corresponding to the meteorological forecast data.

5. The IoT-based intelligent power remote control system for weather stations according to claim 4, characterized in that, Analyze the potential confidence levels required to meet the needs of various applications, including: Based on historical data of application requirements, determine the minimum confidence level to meet the application requirements, and mark the minimum confidence level as the potential confidence level.

6. The IoT-based intelligent power remote control system for weather stations according to claim 4, characterized in that, Analyze the potential confidence levels required to meet the needs of various applications, including: Step SA1: Determine the minimum confidence level to meet the application requirements based on historical data of the application requirements, and mark the minimum confidence level as the candidate confidence level; Step SA2: Reduce the candidate confidence level to obtain a new candidate confidence level; Step SA3: Identify, based on historical data, whether there are historical records showing that the meteorological forecast data with the candidate confidence level does not meet the application requirements; When the meteorological forecast data with the candidate confidence level does not meet the historical records of the application requirements, the candidate confidence level is evaluated as not meeting the application requirements, the reduction of the candidate confidence level is cancelled, and the candidate confidence level after the reduction is cancelled is marked as a potential confidence level. When there is no historical record of meteorological forecast data that does not meet the application requirements for the candidate confidence level, the maximum meteorological data deviation of the meteorological forecast data for the candidate confidence level is estimated. Based on the maximum meteorological data deviation and the meteorological forecast data, simulated meteorological monitoring data is estimated. Based on the simulated meteorological monitoring data and the meteorological forecast data, it is determined whether the application requirements are met. If the application requirements are met, return to step SA2. If the application requirements are not met, cancel the reduction of the candidate confidence level and mark the candidate confidence level after the reduction is canceled as a potential confidence level.

7. The IoT-based intelligent power remote control system for weather stations according to claim 4, characterized in that, The simulation analysis, conducted by the digital twin model based on the effective forecast period, power generation forecast data, and meteorological forecast data, includes: Set simulation constraints, wherein the simulation constraints are that the meteorological monitoring data or meteorological forecast data meet the application requirements; Based on the simulation constraints, the digital twin model is used to conduct simulation analysis according to the effective forecast period, power generation forecast data and meteorological forecast data to obtain various potential power supply control methods. Statistically analyze the power generation utilization rate corresponding to potential power control methods, and obtain the confidence level τ´ of potential power management methods at the corresponding time. t and standard confidence level τ t ; Calculate the meteorological satisfaction rate of potential power supply control methods; In the formula: WL is the meteorological satisfaction rate, i represents the corresponding demander, i=1, 2, ..., n, and n is the number of demanders; This represents the weighting coefficient of the corresponding demand side; For a single demand function, t1 and t2 are the start and end times of the potential power control mode, respectively; clip[] is a cutoff function, indicating that the calculated value is limited to [0, 1]; This is the confidence limit; The priority value of potential power source control methods is calculated based on meteorological satisfaction rate and power generation utilization rate; The formula for calculating priority value is: Priority value = meteorological weight coefficient × meteorological satisfaction rate + power generation weight coefficient × power generation utilization rate; Select the highest priority potential power control method as the power control method for output.

8. The IoT-based intelligent power remote control system for weather stations according to claim 7, characterized in that, 。 9. The IoT-based intelligent power remote control system for weather stations according to claim 1, characterized in that, The power management module has a manual control unit, which is used by staff to remotely control the power supply of the weather station.

10. The IoT-based intelligent power remote control system for weather stations according to claim 1, characterized in that, The cloud platform communicates with each device.

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