Self-powered and energy management system for atmospheric component hyper-spectral remote sensing equipment

Abstract

The invention discloses a self-powered and energy management system for atmospheric composition hyper-spectral remote sensing equipment. The self-powered and energy management system comprises a clean low-carbon energy acquisition module used for realizing acquisition of solar energy and wind energy; the system energy intelligent regulation and control module is used for controlling the clean low-carbon energy acquisition module to acquire and store solar energy and wind energy and controlling to supply power to each part in the system; the environmental parameter acquisition and perception module is used for acquiring sky cloud layer images and meteorological element data; the instrument state monitoring module is used for monitoring the energy flow state and operation state of each part of the system; the industrial personal computer is integrated with a system intelligent management software subsystem, comprises intelligent power management sub-software and parameter acquisition and data analysis sub-software, and is used for controlling the operation of each hardware part in the self-powered and energy management system, so that the overall self-powered, pollution-free and carbon-emission-free hyper-spectral environment telemetering system can stably monitor atmospheric pollutants for a long time.

Description

Technical Field

[0001] The present invention belongs to the technical field of combining power supply technology design with energy management systems, and particularly relates to a self-power supply and energy management system for hyperspectral remote sensing equipment for atmospheric components. Background Art

[0002] Currently, for environmental monitoring instruments such as particulate matter, gaseous pollutants (NO 2 , SO 2 ), greenhouse gases (CO 2 , CH 4 ), etc., especially hyperspectral instruments, civil alternating current power supply is widely used. The current thermal power generation will produce pollutants such as wastewater, dust, and sulfur dioxide, and at the same time will emit a large amount of CO 2 , causing the greenhouse effect and exacerbating global warming, thus leading to climate change. Environmental protection monitoring instruments indirectly emit greenhouse gases while using commercial power, affecting the atmospheric environment. Therefore, comprehensively utilizing new low-carbon energy sources such as solar energy and wind energy and upgrading existing equipment to be low-carbon will help reduce carbon emissions and pollution.

[0003] Hyperspectral environmental telemetry equipment is generally installed in relatively high positions such as rooftops. The main hardware components include a telescope module, an ultra-high-resolution spectrometer, and an optical fiber unit, etc. It has strict requirements for power supply conditions, and installation and observation are highly dependent on commercial power supply. Currently, all fixed-point hyperspectral remote sensing equipment uses 220V alternating current to provide electrical energy. The power consumption of the instrument is relatively low, with an average power of about 100W and a peak power that can reach 300W, and the daily power consumption is about 2.4 kwh.

[0004] The self-power supply and energy management system for hyperspectral environmental telemetry equipment, on the one hand, not only gets rid of the serious dependence of the equipment on commercial power, realizes independent power consumption in principle, can operate independently without relying on any external electrical energy input, and the applicable range is no longer limited to broad sites such as urban rooftops where commercial power is easily accessible, but can be effectively applied to special environments such as farmlands, mines, gobi deserts, etc. where commercial power cannot be accessed, greatly expanding the application range of the equipment. At the same time, it does not consume electrical energy, realizes net-zero carbon dioxide emissions, does not emit any polluting gases, has no impact on the environment, avoids the embarrassing situation of environmental protection monitoring instruments indirectly emitting pollutants, and ensures stable and uninterrupted observation of the equipment while making full use of low-carbon clean energy.

[0005] Based on the above advantages of the self-power supply and energy management system, there is an urgent need for a self-power supply and energy management system for hyperspectral remote sensing equipment for atmospheric components. Summary of the Invention

[0006] In view of the above, in the current situation in China where air quality monitoring instruments have high power consumption and indirectly contribute to a certain amount of carbon emissions, the present invention provides a self-powered and energy management system for an atmospheric composition hyperspectral remote sensing device, realizing the overall self-power supply, pollution-free, carbon emission-free, and long-term stable monitoring of atmospheric pollutants for a hyperspectral environmental telemetry system.

[0007] To achieve the above-mentioned invention object, an embodiment provides a self-powered and energy management system for an atmospheric composition hyperspectral remote sensing device, including: a hardware subsystem and an intelligent management software subsystem, wherein the hardware subsystem includes an industrial control computer as the core, a clean and low-carbon energy collection module, a system energy intelligent regulation module, an environmental parameter collection and perception module, and an instrument status monitoring module;

[0008] The clean and low-carbon energy collection module is used to realize the collection of solar energy and wind energy;

[0009] The system energy intelligent regulation module is used to control the collection and storage of solar energy and wind energy by the clean and low-carbon energy collection module, and control the power supply for each part in the system;

[0010] The environmental parameter collection and perception module is used to collect sky cloud images and meteorological element data;

[0011] The instrument status monitoring module is used to monitor the energy flow status and operation status of each part of the system;

[0012] The industrial control computer is integrated with a system intelligent management software subsystem, including an intelligent power management sub-software and a parameter collection and data analysis sub-software, for controlling the operation of each hardware part in the self-powered and energy management system to realize various functions.

[0013] Preferably, the clean and low-carbon energy collection module includes a solar panel, a three-dimensional motion platform for real-time tracking of the sun's azimuth, and a wind turbine;

[0014] The solar panel is fixed on the three-dimensional motion platform for real-time tracking of the sun's azimuth through a planar clamping device, and under the adjustment of the three-dimensional motion platform for real-time tracking of the sun's azimuth, it keeps itself perpendicular to the direction of the direct sunlight in real time to obtain the maximum photovoltaic power generation in real time, receive solar energy and convert it into electric energy;

[0015] The three-dimensional motion platform for real-time tracking of the sun's azimuth is controlled by the industrial control computer to adjust the optimal pitch and azimuth postures in real time, so that the plane of the solar panel is perpendicular to the direction of the direct sunlight in real time, and when the sun drops below the horizon, the three-dimensional motion platform for real-time tracking of the sun's azimuth returns to the zero position to standby and resumes work after the sun rises the next day;

[0016] The wind turbine rotates the rotor blades by wind force and finally converts wind energy into electric energy.

[0017] Preferably, the system energy intelligent regulation module includes a solar charge and discharge controller, an AC / DC conversion sub-module, a DC integrated output sub-module, and an electric energy storage unit;

[0018] The solar charge and discharge controller serves as an intermediate bridge between the solar panels and the electric energy storage unit, stipulating and controlling the charging and discharging conditions of the large-capacity electric energy storage unit, and controlling the electric energy output of the solar panels and the electric energy storage unit according to part of the power demand;

[0019] The AC / DC conversion sub-module serves as an intermediate bridge between the wind turbine and the electric energy storage unit, and is used to convert the alternating current collected by the wind turbine into direct current as supplementary energy, store it in the electric energy storage unit and supply energy to the system;

[0020] The DC integrated output sub-module serves as an intermediate bridge between the electric energy storage unit and each part, and is used to provide corresponding adapted voltages for each part according to part of the power demand, control the electric energy transmission of each part, and switch the electric energy source according to the internal and external conditions of the system;

[0021] The electric energy storage unit is used to store the electric energy collected by the solar panels and the wind turbine. When the real-time power generation power is greater than the system power consumption power, the excess power generation is stored; when the real-time power generation power is less than the system power consumption power, the solar panels, the wind turbine and the electric energy storage unit supply power to the system at the same time; when the real-time power generation power is zero, the electric energy storage unit supplies power to the system alone.

[0022] Preferably, the environmental parameter acquisition and perception module includes an image acquisition sub-module and an integrated meteorological element collector,

[0023] The image acquisition sub-module is used to take pictures of the sky clouds under the control of the industrial control computer to obtain sky cloud images;

[0024] The integrated meteorological element collector includes a solar radiation sensor, a rain sensor, a temperature sensor, a humidity sensor, a wind speed measurement sensor, and an atmospheric pressure detection sensor, which are respectively used to collect the direct solar radiation intensity and the diffuse solar radiation intensity, monitor the real-time precipitation, collect the environmental temperature and the temperature of the solar panel surface, collect the environmental humidity, measure the wind speed, and measure the atmospheric pressure in the use scenario.

[0025] Preferably, the instrument status monitoring module includes an electric energy information processing sub-module and a system operation status detection sub-module;

[0026] The electric energy information processing sub-module is used to monitor the energy flow status of each part of the system, including the photovoltaic input voltage, photovoltaic input current, and the power consumption of each part of the system; it is also used to control the overall energy flow direction, cooperate with the software subsystem to control the charging and discharging of the electric energy storage unit and the operating power consumption of each part of the system;

[0027] The system operating status detection sub-module is used to detect the operating status and fault diagnosis of the solar panels and the electric energy storage unit, and take timely protection measures against the hot spots, hidden cracks, power attenuation of the solar panels, and abnormal voltage output, abnormal temperature, overcharging and over-discharging problems of the electric energy storage unit, and give early warnings through the industrial control computer.

[0028] Preferably, the industrial control computer analyzes the cloud thickness and area based on the sky layer image and evaluates the occlusion of the sun, predicts the power generation power of the solar panels based on the occlusion situation, calculates the current solar azimuth angle and altitude angle based on the current time and combined with the longitude and latitude of the system installation location, and calculates the best pitch and azimuth postures of the three-dimensional motion platform for real-time tracking of the solar azimuth based on the previous solar azimuth angle and altitude angle, so as to control and adjust the angle of collecting solar energy.

[0029] Preferably, the parameter acquisition and data analysis sub-software can display and record in real time the solar power generation power, wind power generation power, the instant status of the electric energy storage unit, and the power consumption of each module collected by the system. Among them, the instant status of the electric energy storage unit includes the output voltage of each battery cell, the internal temperature of the battery cell, the state of the switch MOS, the total output voltage, the total output current, and the output voltage fluctuation range;

[0030] The intelligent power management sub-software can automatically adjust the system working mode, intelligently monitor the system energy flow, allocate and manage the overall power consumption of the system in combination with future condition prediction and system status, control the overall power consumption level of the system, and ensure the long-term stable observation of the system.

[0031] Preferably, the intelligent power management sub-software is implemented based on the intelligent power management algorithm, and the intelligent power management algorithm includes the system electric energy monitoring algorithm, the future power generation prediction algorithm, and the system power consumption and electric energy management algorithm;

[0032] The system electric energy monitoring algorithm is used to obtain the system electric energy flow status. Specifically, it calculates the average power generation power and the daily total power generation based on the system instantaneous input voltage and input current, and calculates the average power consumption and the daily total power consumption based on the system instantaneous output voltage and output current; detects the voltage and current fluctuation ranges of each part of the system, and issues a warning signal when a circuit problem is found; it also compares the difference between the discharge characteristic curve of the electric energy storage unit in the working state and the original discharge characteristic curve, and gives a warning when the performance of the electric energy storage unit drops to a certain extent.

[0033] Preferably, the future power generation prediction algorithm is used to predict future power generation, including a solar power generation prediction algorithm and a wind power generation prediction algorithm;

[0034] The solar power generation prediction algorithm uses the solar power generation prediction model constructed by LSTM to predict the solar power generation at future moments. The input data of the solar power generation prediction model includes cloud data, outdoor temperature, humidity, near-surface solar radiation intensity, real-time wind speed and wind direction, solar panel surface temperature, and atmospheric pressure;

[0035] The wind power generation prediction algorithm uses a wind power generation prediction model constructed by LSTM to predict wind power generation at future times. The input data of the wind power generation prediction model includes wind speed, wind direction, and temperature.

[0036] Preferably, the system power consumption and power management algorithm is used to monitor the input and output energy of the management system in real time, and comprehensively control the power distribution and utilization of the system based on the predicted solar power generation power in the next few hours, the power of the power storage unit, the change of meteorological elements in the next few hours, and the current operating status of the system, including:

[0037] Taking the current time, the latitude and longitude of the instrument installation site, and weather conditions as input, the azimuth and pitch angles of the solar panels are changed by controlling the sun's position in real time to track the three-dimensional motion platform, thereby increasing the real-time solar power generation power.

[0038] Taking weather conditions as input, the direction of power flow in the system is changed by controlling the solar charge and discharge controller, wind turbine generator, and power storage unit to maximize the energy utilization of the system;

[0039] Taking partial power demand and the state of the power storage unit as input, the charging and discharging state of the power storage unit is changed by controlling partial energy consumption and the switch of the power storage unit, thereby reducing the problems of overcharging, over-discharging and performance degradation of the power storage unit;

[0040] Taking weather conditions and cloud data as input, the system controls the sun's position to track the three-dimensional motion platform, atmospheric composition hyperspectral remote sensing equipment, and industrial computer operating status in real time, adjusts the system's operating mode according to different weather conditions, and reduces unnecessary system energy loss.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] Based on the exclusive use of new clean and low-carbon energy, the present invention ensures long-term uninterrupted observation of the system. By integrating a meteorological element collector and using a power generation prediction model built with a long short-term memory recurrent neural network (LSTM), it realizes the prediction of the total power generation of solar energy and wind energy in the next few days, solving the problem that the power generation power prediction algorithm in the past few hours is difficult to be stably applied to observation instruments. Through the energy management algorithm, high-time-resolution monitoring and scheduling of the instrument's power generation power, power consumption power, and instrument power consumption are realized, greatly improving the utilization rate of clean energy; comprehensively using new low-carbon energy such as solar energy and wind energy to assist in the low-carbon upgrade of existing equipment, reducing carbon emissions from the source, and will contribute to the goal of zero carbon emissions for domestic environmental monitoring instrument equipment. The system of the present invention solves the problem of indirect carbon emissions brought by traditional atmospheric environment monitoring instruments and at the same time has the ability to stably observe for a long time under unattended conditions.

[0043] The present invention utilizes intelligent new clean energy management technology to achieve a new solution for intelligent energy conservation, carbon reduction, pollution reduction, safety, and environmental protection, with broad application prospects. Through future power generation prediction, the system can intelligently adjust the distribution of electric energy and achieve intelligent adjustment of electric energy in the system distribution. In this way, the system can automatically perform hyperspectral environmental telemetry tasks and can be intelligently adjusted and optimized according to real-time situations.

[0044] The present invention can provide overall power supply and intelligent allocation for the system, providing a stable and reliable setup solution for hyperspectral remote sensing equipment for atmospheric components. At the same time, based on pollution reduction, carbon reduction, self-power supply, and energy management, the system realizes self-power supply and pollution-free emissions, having a positive impact on the environment.

[0045] In summary, the system of the present invention has the advantages of intelligence, high efficiency, and low carbon emissions, making important contributions to realizing intelligent energy conservation, carbon reduction, pollution reduction, safety, and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a schematic diagram of the hardware part structure of the self-power supply and energy management system for hyperspectral remote sensing equipment for atmospheric components provided by the embodiment;

[0048] Figure 2 It is a schematic diagram of the power supply of the energy management system for hyperspectral remote sensing equipment for atmospheric components provided by the embodiment;

[0049] Figure 3 It is a functional schematic diagram of the intelligent power management algorithm provided by the embodiment;

[0050] Figure 4 It is a workflow diagram of the self-power supply and energy management system for the atmospheric composition hyperspectral remote sensing equipment provided by the embodiment;

[0051] Figure 5 It is a time series diagram of the real-time power generation power provided by the embodiment;

[0052] Figure 6 It is a time series diagram of the remaining capacity of the electric energy storage unit provided by the embodiment;

[0053] Figure 7 It is a time series diagram of the meteorological parameters provided by the embodiment;

[0054] Figure 8 It is a prediction diagram of the short-term future power generation effect provided by the embodiment;

[0055] Figure 9 It is a statistical chart of the daily power generation during the observation period provided by the embodiment;

[0056] Figure 10 It is a statistical chart of the predicted daily power generation during the observation period provided by the embodiment. Specific Embodiments

[0057] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the protection scope of the present invention.

[0058] The embodiment provides a self-power supply and energy management system for an atmospheric composition hyperspectral remote sensing equipment, which can use solar energy and wind energy to provide electric energy for the equipment, and conduct integrated and all-round management of the overall energy use, power management, status monitoring, and fault diagnosis of the system, and regulate the self-power supply according to the current status and future power generation prediction to ensure the long-term stable operation of the atmospheric composition hyperspectral remote sensing equipment. As Figure 1 shown, it specifically includes a hardware subsystem and a software subsystem. The hardware subsystem includes an industrial control computer as the core, a clean and low-carbon energy acquisition module, a system energy intelligent regulation module, an environmental parameter acquisition and perception module, and an instrument status monitoring module. These modules are responsible for collecting solar energy and wind energy, collecting and storing parameters such as the current input status of new clean energy, the input and output power of the system, and the fluctuation range, inputting the clean and low-carbon electric energy collected by the system into the electric energy storage device for storage, and scheduling and distributing the overall energy of the system according to the current working status of the system to maximize energy utilization.

[0059] In the embodiment, the clean and low-carbon energy collection module is used to collect solar energy and wind energy. For example, Figure 1 As shown, it specifically includes a solar panel, a three-dimensional motion platform for real-time tracking of the sun's azimuth, and a wind turbine. Among them, the solar panel is fixed on the three-dimensional motion platform for real-time tracking of the sun's azimuth through a planar clamping device, and under the adjustment of two motors of the three-dimensional motion platform for real-time tracking of the sun's azimuth, it keeps itself perpendicular to the direction of direct sunlight in real time to obtain the maximum photovoltaic power generation in real time, receive solar energy and convert it into electrical energy.

[0060] The three-dimensional motion platform for real-time tracking of the sun's azimuth is controlled by an industrial computer to adjust the optimal pitch and azimuth attitudes in real time, so that the plane of the solar panel is perpendicular to the direction of direct sunlight in real time. And when the sun drops below the horizon, the three-dimensional motion platform for real-time tracking of the sun's azimuth returns to the zero position to standby and resumes work after the sun rises the next day. In order to cooperate with the work of the three-dimensional motion platform for real-time tracking of the sun's azimuth, the industrial computer analyzes the cloud thickness and area based on the sky layer image and evaluates the occlusion of the sun, predicts the power generation power of the solar panel based on the occlusion situation, and at the same time calculates the current solar azimuth angle and altitude angle based on the current time and combined with the longitude and latitude of the system installation location, and calculates the optimal pitch and azimuth attitudes of the three-dimensional motion platform for real-time tracking of the sun's azimuth based on the current solar azimuth angle and altitude angle to control and adjust the angle of collecting solar energy.

[0061] The wind turbine rotates the rotor blades by wind force, finally converts wind energy into electrical energy, which is stored by the electrical energy buffer unit for system use.

[0062] In the embodiment, the system energy intelligent regulation module is used to control the collection and storage of solar energy and wind energy by the clean and low-carbon energy collection module, and control the power supply for each part in the system. For example, Figure 1 As shown, it specifically includes a solar charge and discharge controller, an AC-DC conversion sub-module, a DC integrated output sub-module, and an electrical energy storage unit; among them, the solar charge and discharge controller serves as an intermediate bridge between the solar panel and the electrical energy storage unit, stipulates and controls the charging and discharging conditions of the large-capacity electrical energy storage unit, and controls the electrical energy output of the solar panel and the electrical energy storage unit according to the power demand of the part.

[0063] The AC-DC conversion sub-module serves as an intermediate bridge between the wind turbine and the electrical energy storage unit, and is used to convert the alternating current collected by the wind turbine into direct current (such as 48V direct current) as supplementary energy, store it in the electrical energy storage unit and supply energy to the system.

[0064] The DC integrated output sub-module serves as an intermediate bridge between the power storage unit and each part, and is used to provide corresponding adapted voltages for each part (including the internal components of the energy management system and the internal components of the hyperspectral remote sensing device) according to the power requirements of each part. At the same time, it controls the power transmission of each part and switches the power source according to the internal and external conditions of the system, such as Figure 2 shown.

[0065] The power storage unit is used to store the electric energy collected by the solar panels and wind turbines. When the real-time power generation is greater than the power consumption of the system, the excess power generation is stored; when the real-time power generation is less than the power consumption of the system, the solar panels, wind turbines and power storage unit supply power to the system at the same time; when the real-time power generation is zero, the power storage unit supplies power to the system alone.

[0066] In the embodiment, the environmental parameter acquisition and perception module is used to acquire sky layer images and meteorological element data, such as Figure 1 shown, and specifically includes an image acquisition sub-module and an integrated meteorological element collector. Among them, the image acquisition sub-module is used to take pictures of the sky clouds under the control of the industrial control computer to obtain sky layer images, and the sky layer images are used to evaluate the occlusion of the sun to predict the sun's azimuth and track the best pitch and azimuth postures of the three-dimensional motion platform in real time.

[0067] The integrated meteorological element collector includes a solar radiation sensor, a rain sensor, a temperature sensor, a humidity sensor, a wind speed measurement sensor, and an atmospheric pressure detection sensor. Among them, the solar radiation sensor is used to collect the direct solar radiation intensity and the diffuse solar radiation intensity; the rain sensor is used to monitor the real-time precipitation; the temperature sensor collects the ambient temperature and the temperature of the solar panel surface; the humidity sensor collects the ambient humidity; the wind speed measurement module is used to measure the wind speed; the atmospheric pressure detection module is used to measure the atmospheric pressure in the usage scenario.

[0068] In the embodiment, the instrument status monitoring module is used to monitor the energy flow status and operation status of each part of the system, such as Figure 1 shown, and specifically includes an electric energy information processing sub-module and a system operation status detection sub-module; among them, the electric energy information processing sub-module is used to monitor the energy flow status of each part of the system, including the photovoltaic input voltage, the photovoltaic input current, and the power consumption of each part of the system; it is also used to control the overall energy flow direction, and cooperate with the software sub-system to control the charging and discharging of the power storage unit and the operation power consumption of each part of the system.

[0069] The system operation status detection sub-module is used to detect the operation status and fault diagnosis of the solar panels and the power storage unit, and take timely protection measures against problems such as hot spots, hidden cracks and power attenuation of the solar panels and abnormal voltage output, temperature abnormality, overcharge and over-discharge of the power storage unit, and give an early warning through the industrial control computer.

[0070] In the embodiment, the industrial personal computer is integrated with a system intelligent management software subsystem, including an intelligent power management sub-software and a parameter acquisition and data analysis sub-software, which are used to control the operation of each hardware part in the self-powered and energy management system to realize various functions.

[0071] Among them, the parameter acquisition and data analysis sub-software can display and record in real time the solar power generation power, wind power generation power, the instant state of the electric energy storage unit, and the power consumption of each module collected by the system. Among them, the instant state of the electric energy storage unit includes the output voltage of each cell monomer, the internal temperature of the cell, the state of the switch MOS, the total output voltage, the total output current, and the output voltage fluctuation range.

[0072] The intelligent power management sub-software can automatically adjust the system working mode, intelligently monitor the system energy flow, allocate and manage the overall power consumption of the system by combining future condition prediction and system status, control the overall power consumption level of the system, and ensure the long-term stable observation of the system; specifically, the intelligent power management sub-software is based on, for example, Figure 3 the intelligent power management algorithm shown. Among them, the intelligent power management algorithm includes a system electric energy monitoring algorithm, a future power generation prediction algorithm, and a system power consumption and electric energy management algorithm. These three algorithms run normally after the system is powered on. The system electric energy monitoring algorithm is used to obtain the system electric energy flow state, the future power generation prediction algorithm is used to predict the future power generation, and the system power consumption and electric energy management algorithm is used to schedule and manage the system energy flow.

[0073] For the system electric energy monitoring algorithm, read the system instantaneous input voltage U in , the system instantaneous input current I in , the system instantaneous output voltage U out , the system instantaneous output current I out , and the reading time interval is set to T 1 . When T 1 is relatively small, the instantaneous voltage and instantaneous current can be used to replace the average voltage and average current within the T 1 time period. Then, within this T 1 time period, there are:

[0074] The average power generation power P in =U in ·I in , the average power consumption power P out =U out ·I out , and P in , P out are the average power generation powers within this T 1 time period.

[0075] Statistically calculate all Ts during the period from 0:00 to 24:00 on the same day 1The power generation within a time period can be used to calculate the total daily power generation \(W\). in and the total power consumption \(W\). out Then we have:

[0076] Total power generation

[0077] Total power consumption

[0078] Where \(P\) in，i and \(P\) out，i are respectively the average power generation power and the average power consumption power within the \(i\)-th \(T\) time period of the day, and \(T\) 1 is the time interval read by the power energy information processing module. 1 Furthermore, the system power energy monitoring algorithm also monitors the voltage and current fluctuation ranges of various parts of the system. When circuit problems such as excessive voltage fluctuation range, current mutation, overload, and undercurrent occur within a short period of time, the system power energy monitoring algorithm sends a warning signal to the industrial control computer.

[0079] The industrial control computer records the self-discharge characteristic curve of the power energy storage unit when it leaves the factory. This discharge characteristic curve is the functional relationship between the output voltage and the remaining power of the power energy storage unit. The output voltage of the power energy storage unit and the percentage of the remaining power of the power energy storage unit can be read through the power energy information processing module to obtain the discharge characteristic curve under the working state. Then, by comparing and detecting the difference between the discharge characteristic curve under the working state and the original discharge characteristic curve, the industrial control computer gives an early warning when the performance of the power energy storage unit drops to a certain extent. Since the characteristic curve of the power energy storage unit changes when affected by temperature changes and its own performance loss, the remaining power is corrected through the statistics of the initial power, solar power generation, and the overall power consumption of the system to eliminate the influence of temperature changes and the power energy storage unit itself.

[0080] For the future power generation prediction algorithm, it includes a solar power generation prediction algorithm and a wind power generation prediction algorithm, which are used to predict future power generation.

[0081]

[0082] ​The solar power generation prediction algorithm uses a solar power generation prediction model constructed by a long short-term memory recurrent neural network (LSTM) to predict the solar power generation at future times (i.e., within the next few hours). First, set the latitude and longitude information of the current instrument installation location, historical weather data, solar panel parameters, and real-time power generation. Among them, the historical weather data is provided by the integrated meteorological element collector, the solar panel parameters are provided by the manufacturer, and the real-time power generation is provided by the electric energy information processing module. The input data of the solar power generation prediction model includes cloud data, outdoor temperature, humidity, near-surface solar radiation intensity, real-time wind speed and direction, solar panel surface temperature, and atmospheric pressure. Through the training and verification process of the relevant variables and real-time power generation at the same time, find the periodic changes of the real-time power generation depending on the weather conditions, the non-linear relationships existing in different relevant variables and the real-time power generation, and the coupling relationships between the relevant variables, establish a short-term future solar power generation prediction model, and then train the model by real-time data collection. By comparing and calculating the predicted value and the actual value, analyze the error source, and then add the error term to obtain the basic expression of the real-time power generation under the influence of internal and external factors.

[0083] The cloud data is converted from the images collected by the image acquisition sub-module into data such as cloud thickness, cloud area, cloud movement direction, cloud movement speed, and solar position information in the sky. According to the cloud cover obtained by the image acquisition sub-module, then measure the influence of cloud information on solar power generation, and re-analyze and correct the error of the historical predicted power generation and the actual power generation to continuously improve the model. Using the cloud data collected by the image acquisition sub-module, take the cloud information as an input parameter, and evaluate the influence of the cloud on the solar radiation degree according to the light brightness and area occlusion in the grid. Then the cloud data can be input into the model.

[0084] The outdoor temperature, humidity, near-surface solar radiation intensity, real-time wind speed and direction, solar panel surface temperature, and atmospheric pressure data are all provided by the integrated meteorological element collector. After collection, preprocessing is carried out according to an appropriate time interval, removing outliers and interpolating the missing data, and then it can be input into the model.

[0085] The short-term future solar power generation prediction model quantifies the solar radiation intensity and effective light time within the next few hours based on the future weather conditions at the instrument installation location, according to relevant factors such as the latitude and longitude of the instrument installation location, the diffusion ratio and transmittance of direct and indirect sunlight in similar past weather, and at the same time corrects the prediction result according to other meteorological parameters collected, to obtain the real-time power generation within the next few hours under the influence of internal and external factors.

[0086] Based on this, the basic prediction expression of the solar power generation prediction model is as follows:

[0087]

[0088] Where P(t) is the real-time solar power generation at time t, X i (t) is the value of the factors affecting solar power generation at time t, the input data of the model, a i (x) is the correction coefficient of the influencing factor X when the value is x, and b is the mutual coupling term between the influencing factors.

[0089] The wind power generation prediction algorithm also uses the wind power generation prediction model built by LSTM to predict the wind power generation in the future. The input data of the wind power generation prediction model includes variables such as wind speed, wind direction, and temperature, and there is no need to consider the influence of clouds. The model establishment, training and verification process is the same as the solar power generation prediction algorithm.

[0090] The system power consumption and power management algorithm monitors and manages the input and output energy of the system, and uses a closed-loop feedback control algorithm to control the amount of power stored in the power storage unit, the system power consumption, power generation, and the working mode under special conditions, and adjusts the direction of system energy flow in real time to maximize energy utilization efficiency. The system power consumption and power management algorithm uses the device status and monitoring data of each unit of the system as input, aiming to improve energy utilization without reducing working performance and maintain observation for as long as possible. The system power consumption and power management algorithm comprehensively controls the power distribution and utilization of the system based on the predicted solar power generation power in the next few hours, the power of the power storage unit, the changes in meteorological elements in the next few hours, and the current operating status of the system. The specific control process is as follows:

[0091] (1) Taking the current time, the latitude and longitude of the instrument installation site, and weather conditions as input, the azimuth and pitch angles of the solar panels are changed by controlling the real-time tracking three-dimensional motion platform of the sun's position to achieve the purpose of increasing the real-time solar power generation power. The specific steps are as follows:

[0092] System power consumption and power management algorithm control the sun's position in real time to track the three-dimensional motion platform for automatic positioning and tracking the sun. 2 , that is, every T 2 The three-dimensional motion platform performs automatic positioning operations. The specific steps are that the industrial computer reads the time and calculates the current solar azimuth and altitude angle according to the longitude and latitude of the system installation site. The industrial computer tracks the three-dimensional motion platform in real time by controlling the solar azimuth to make the plane of the solar panel perpendicular to the incident light of the sun. Keeping the plane of the solar panel perpendicular to the incident light of the sun can maximize the use of solar energy resources and improve the power generation efficiency of solar panels. By continuously monitoring the position of the sun and adjusting the orientation of the solar panel, the system achieves more efficient light energy conversion, improves the overall energy scheduling performance of the system, and provides stable power output.

[0093] (2) With weather conditions as the input, by controlling the solar charge-discharge controller, wind turbine, and energy storage unit, the direction of the system's electrical energy flow is changed to maximize the system's energy utilization rate. The specific steps are as follows:

[0094] Based on solar energy prediction, wind energy prediction, and real-time electricity consumption, the system can perform dynamic control according to current demands. For example, when the weather is good, solar radiation is sufficient, and wind power generation is relatively weak, the output of the solar power generation can be increased to the maximum to improve the self-sufficiency of the system; while when the weather deteriorates, solar radiation weakens, and wind power generation is strong, the output of solar power generation can be reduced, and the output of wind power generation can be increased to avoid power shortages.

[0095] (3) With the electrical energy demand of the load and the state of the energy storage unit as the input, by controlling partial energy consumption and the switch of the energy storage unit, the charge-discharge state of the energy storage unit is changed to reduce problems such as overcharging, over-discharging, and performance degradation of the energy storage unit. The specific steps are as follows:

[0096] The system determines the charge-discharge strategy of the energy storage unit based on the power situation of the energy storage unit, including information such as the current charge-discharge state, remaining stored power, charge-discharge efficiency, etc., and the current load demand. If the load demand is large, more electrical energy is released from the energy storage unit; while when the load demand is small, the charging amount of the energy storage unit is increased; according to the power situation and load demand, the system needs to formulate a reasonable charge-discharge control strategy. For example, when the load demand is large, the charging efficiency of the energy storage unit is improved to cope with high-load periods. At the same time, when the load demand is small, an appropriate discharge strategy is adopted to control the depth and rate of charge and discharge, ensuring that the energy storage unit is in an appropriate charge-discharge state and avoiding overly frequent deep discharges or charges.

[0097] The system load and operating state are important factors affecting the electrical energy use efficiency. The system needs to monitor information such as the power consumption of the equipment and the system load in real time and make adjustments as needed.

[0098] (4) With weather conditions and cloud data as the input, by controlling the real-time tracking three-dimensional motion platform of the solar azimuth, the hyperspectral remote sensing equipment for atmospheric components, and the operating state of the industrial control computer, the system working mode is adjusted according to different weather conditions to reduce unnecessary energy losses of the system. The specific steps are as follows:

[0099] The system further optimizes the system energy scheduling according to the changes in meteorological elements. Adjustments are made to the system operation according to different weather conditions, including:

[0100] (a) In cloudy weather, focus on analyzing cloud data, including cloud thickness, cloud area, cloud movement direction, cloud movement speed, etc., and predict cloud data within the next few hours. If the cloud cover blocks the sun in the next few hours, causing the output power of solar power generation to drop to a certain level, at this time, extend the tracking time interval of the three-dimensional motion platform for real-time tracking of the sun's azimuth, and find the maximum net output power between the increased power consumption of the three-dimensional motion platform for real-time tracking of the sun's azimuth and the increased power generation of solar power.

[0101] (b) Under sunny conditions with high power generation, the system power consumption and power management algorithm schedule the system's power consumption according to the prediction of the solar power generation power within the next few hours by the future power generation prediction algorithm. If a high solar power generation power can be maintained within the next few hours, and it can meet the system operation consumption and reach the maximum stored power of the energy storage unit, shorten the tracking time interval of the three-dimensional motion platform for real-time tracking of the sun's azimuth, and at the same time, the industrial control computer analyzes and processes the spectral data collected on the same day.

[0102] (c) Since the observation effect is poor in rainy weather, when the rainfall reaches a certain level, the industrial control computer shuts off the power supply of the energy storage unit to the three-dimensional motion platform for real-time tracking of the sun's azimuth, the telescope module, and the ultra-high-resolution spectrometer, and at the same time adjusts the screen brightness of the industrial control computer to the lowest level, reducing the system power consumption on the premise of ensuring the necessary data collection and operation of the system. The system power consumption and power management algorithm continuously monitor the rainfall data to judge whether the current weather condition is still rainy. If it is judged to be sunny, the industrial control computer turns on the power supply of the energy storage unit to the three-dimensional motion platform for real-time tracking of the sun's azimuth, the telescope module, and the ultra-high-resolution spectrometer, and the system operates normally.

[0103] (d) When entering the season of continuous rainy and cloudy days, and the remaining power of the energy storage unit drops to a certain level, the system enters the low-power mode.

[0104] The low-power mode means that the system will reduce power consumption in the low-power mode. The specific implementation operations include reducing the screen brightness of the industrial control computer, slightly relaxing the temperature limit of the ultra-high-resolution spectrometer, and reducing the rotation speed of the system cooling fan. If the system is in the low-power mode after the spectral data collection on the same day, the spectral data of the same day will not be processed, and the industrial control computer records the date of the same day.

[0105] Set the low-power mode entry threshold A in the industrial control computer 1 and the low-power mode exit threshold A 2 . When the system is in the normal observation mode, when the power of the energy storage unit drops to the threshold A of the total power 1 , the system enters the low-power mode; when the system is in the low-power mode, when the power of the energy storage unit rises to the threshold A of the total power 2 , the system automatically exits the low-power mode.

[0106] If the system is in the normal working mode at this time, the industrial control computer performs inversion processing on the spectra collected on the same day. The inversion processing is completed by the operation of the industrial control computer. The software inputs the spectra collected on the same day, and the absorption cross-sections of different polluting gases at specific wavelength bands are imported in advance. The least squares method is used to fit the differences between the collected spectra and the reference spectra to obtain the slant column density (SCD) of the gas. After the inversion is completed, the industrial control computer transmits the inversion results and the original spectral files back to the server. At the same time, the power supply of the ultra-high resolution spectrometer by the electric energy storage unit is turned off.

[0107] If the system is in the low-power mode at this time, no spectral inversion is performed, and the industrial control computer automatically reads the date of the same day and records it.

[0108] It should also be noted that the system of the present invention is generally placed on the top floor of a high-rise building with good lighting conditions, and the observation records are the sky scattered light of a set of elevation angle sequences corresponding to different azimuth angles.

[0109] The embodiment also provides the working process of the self-powered and energy management system for the above-mentioned hyperspectral remote sensing equipment for atmospheric components, as Figure 4 shown, including the following steps:

[0110] Step 1, the system starts, and the software part of the industrial control computer system runs, and the instrument performs self-check. The industrial control computer built in the system automatically runs the parameter acquisition and data analysis sub-software and the intelligent power management sub-software; the electric energy storage unit supplies power to the ultra-high resolution spectrometer and the three-dimensional motion platform for real-time tracking of the sun's azimuth; the industrial control computer performs self-check and serial port signal connection with the control motors in the ultra-high resolution spectrometer, the three-dimensional motion platform for real-time tracking of the sun's azimuth, the image acquisition module, the integrated meteorological element collector, and the telescope module.

[0111] Step 2, the industrial control computer controls the relevant hardware, and the hyperspectral remote sensing equipment for atmospheric components works according to the set program. After the light intensity received by the hyperspectral telescope module reaches the predetermined value, observation is carried out; the spectral acquisition software controls the rotation of the azimuth angle motor in the telescope module to turn the observation direction of the telescope to the preset observation direction; the industrial control computer controls the rotation of the elevation angle motor in the telescope module to control the prism part in the telescope module to turn to the preset elevation angle, and the observation elevation angle is generally a set of observation sequences including an elevation angle of 90 degrees; the industrial control computer controls the high-resolution spectrometer to collect spectral light intensity information, and its shape is displayed in real time through the spectral acquisition software and saved locally.

[0112] Step 3, the industrial control computer runs the relevant algorithms, and the self-powered and energy management system works. The industrial control computer reads the system input voltage, system input current, system output voltage, and system output current through the electric energy information processing module for monitoring, and the measurement interval is T 1 , and the photovoltaic power generation P can be obtained inand the system loss power P out ; The industrial control computer controls the three-dimensional motion platform for real-time tracking of the sun's azimuth to perform sun-tracking operations, with an operation interval of T 2 ; The industrial control computer predicts the power generation within the next several hours based on the data collected in real time by the integrated meteorological element collector, historical weather data, and historical power generation data; the industrial control computer controls the stored electricity in the energy storage unit, the power generation and consumption of the system, and the working mode under special conditions through the system power consumption and power management algorithm; the industrial control computer saves all the collected data of the intelligent power management software and detects the operating status of each part of the system.

[0113] Step 4, after the daytime system observation task ends, the hyperspectral remote sensing equipment for atmospheric components stops working. When the solar radiation intensity is 0 W / m 2 ², the spectral acquisition software stops spectral acquisition, the industrial control computer returns the three-dimensional motion platform for real-time tracking of the sun's azimuth to the zero position, that is, due east, stops the energy storage unit from supplying power to the three-dimensional motion platform for real-time tracking of the sun's azimuth, and the hyperspectral remote sensing equipment for atmospheric components stops working.

[0114] Step 5, the system processes the data of the current day. The intelligent power management sub-software calculates the solar power generation and system power consumption of the current day according to the set algorithm. The industrial control computer reads the remaining power percentage of the energy storage unit and generates the system work log of the current day. The industrial control computer determines whether to perform inversion processing on the spectra collected on the current day according to whether the system enters the low-power mode. If the system is in the normal working mode at this time, after the inversion is completed, the industrial control computer uploads the inversion results and the original spectral files to the server, and at the same time shuts down the power supply of the energy storage unit to the ultra-high-resolution spectrometer. When the light intensity reaches the spectral acquisition requirement the next day, the industrial control computer turns on the power supply of the energy storage unit to the three-dimensional motion platform for real-time tracking of the sun's azimuth and the ultra-high-resolution spectrometer, and the system runs. If the system is in the low-power mode at this time, no spectral inversion is performed. The industrial control computer automatically reads the current date and records the date, shuts down the power supply of the energy storage unit to the ultra-high-resolution spectrometer, and shuts down the industrial control computer. After the program-set time the next day, the industrial control computer starts relying on the timed startup program and turns on the power supply of the energy storage unit to the three-dimensional motion platform for real-time tracking of the sun's azimuth and the ultra-high-resolution spectrometer, and the system runs.

[0115] The embodiment also provides a specific experimental example of the above system. The specific hyperspectral remote sensing equipment for atmospheric components is set up on the Science Island, Luyang District, Hefei City, Anhui Province, China, 31.91°N, 117.18°E, which belongs to a typical urban center observation site. The observation time selected for this embodiment is from August 9th to August 21st, 2024, and the observation results are presented.

[0116] The preset parameter settings and operation of the self-power supply and energy management system are as follows: the relevant parameters of the solar panel and the large-capacity energy storage unit are determined according to the overall power consumption of the system. The real-time average power of the system is 100W. The solar panel selection is 1000W for peak power generation, 400W for peak power generation of the wind turbine, and 5.76kwh for the energy storage unit. The DC integrated output module converts DC power of any voltage into 24V DC power. In actual operation, the new low-carbon energy generation can ensure the long-term stable operation of the instrument, and the AC-DC conversion module converts the AC power output by the wind turbine. Low power mode entry threshold A 1 Set to 20%, low power mode exit threshold A 2 Set to 40%.

[0117] When the system starts, the industrial computer runs the initialization script, and each part of the system begins self-checking. The atmospheric composition hyperspectral remote sensing equipment and the self-power supply and energy management system start working, including: the atmospheric composition hyperspectral remote sensing equipment collects solar scattering spectra according to the established observation elevation sequence (1°, 2°, 3°, 4°, 5°, 6°, 8°, 10°, 15°, 30° and 90°), and the collection time for each angle is about 1 minute. After an observation elevation sequence is completed, the system stores the collected solar scattering spectrum data in the industrial computer for storage. After the data is saved, the elevation sequence observation is repeated until the light conditions on that day do not meet the observation requirements. The industrial computer performs inversion processing on the spectrum data collected on that day, performs preliminary processing on the acquired spectrum based on the dark background and bias structure of the spectrometer collected by the instrument at night, selects the 90° zenith spectrum in the observation sequence as the reference spectrum, selects the absorption cross section of different gases in a specific band, uses the hyperspectral fitting algorithm to obtain the total concentration of the pollutant optical path, and outputs the pollutant profile results of the observation location on that day.

[0118] The self-powered and energy management system adjusts the sun's position from the beginning of receiving the light intensity, and tracks the azimuth of the three-dimensional motion platform to the east in real time. It calculates the current sun's azimuth and altitude every 5 minutes. The calculation formula is as follows:

[0119] Calculation of solar altitude angle sinH s =sinθ·sinδ+cosθ·cosv·cosω

[0120] Solar azimuth calculation

[0121] Where n is the number of days, January 1 is counted as 1, and December 31 is counted as 365 (common year)

[0122] H 1s Indicates the standard time of the location, here is Beijing time;

[0123] Llso Represents the geographical longitude of the location, and the system installation location is 117.18°;

[0124] L sm Represents the geographical longitude of the area where the standard time is set, which is 120° east longitude here;

[0125]

[0126] Correction value E = 9.87sin2B - 7.53cosB - 1.5sinB;

[0127] Calculation of the solar declination angle

[0128] Calculation of solar time

[0129] Geographical latitude Xi = 31.91°E;

[0130] Calculation of solar hour angle

[0131] After calculating the solar azimuth angle and altitude angle, the three-dimensional motion platform for real-time tracking of the sun azimuth adjusts the azimuth angle of the solar panel to be the same as the solar azimuth angle, and the tilt angle is complementary to the solar altitude angle, so that the solar panel is perpendicular to the direct sunlight of the sun to achieve the maximum power generation. After 5 minutes, recalculate the current solar azimuth angle and altitude angle, and adjust the azimuth angle and tilt angle of the solar panel. When the solar radiation intensity of the day drops to 0 W / m 2 At this time, the power storage unit stops supplying power to the three-dimensional motion platform for real-time tracking of the sun azimuth.

[0132] The system collects real-time solar power generation power and meteorological parameter data (outdoor temperature, humidity, solar radiation), reads the system input voltage, system input current, system output voltage, and system output current through the electric energy information processing module for monitoring, and the measurement interval is T 1 = 10 s, and the generated power P in and the system loss power P out can be obtained. The integrated meteorological element collector collects various meteorological data in real time. The industrial control computer saves all data and filters out abnormal values, and outputs the average value of the data collected within each minute (such as Figure 5 、 Figure 7) The maximum real-time power generation of solar energy can reach about 950W. The daily power generation time mainly concentrates from 8:00 to 18:00 every day. The power generation power reaches the highest value of the day at about 12:00 every day, and the daily effective power generation time is about 8 hours. On the vast majority of observation days, the electric energy storage unit reaches the maximum electric energy storage state at 14:00-16:00 on the same day, and the phenomenon of curtailment of photovoltaic power generation occurs. Among the meteorological parameters, the three variables of solar radiation, temperature, and humidity are all strongly correlated with the solar power generation power. At the same time, the power generation situation in the next few hours is predicted based on historical and current power generation power and meteorological parameter data. In this implementation case, the LSTM method is used to build a basic prediction model, setting the longitude and latitude information (31.91°N, 117.18°E) of the current installation location of the instrument, historical weather data (outdoor temperature, humidity, solar radiation, wind speed, wind direction, etc. in the past three months), solar panel parameters (solar panel length, width, and internal PN junction arrangement), real-time power generation power (obtained by multiplying the power generation voltage and current), and through the training and verification process of relevant variables and real-time power generation power at the same moment, finding the periodic changes of real-time power generation power depending on weather conditions, the non-linear relationships existing between different relevant variables and real-time power generation power, and the coupling relationships between relevant variables, to establish a future ultra-short-term power generation prediction model. The system allocates the overall energy of the system according to the prediction results, respectively for the ultra-short-term real-time power generation power prediction from August 9th to 22nd, 2024 and the short-term daily power generation prediction. The prediction results are as Figure 8 、 Figure 10 。

[0133] The system runs the system power consumption and electric energy management algorithms in real time based on the remaining power of the electric energy storage unit, future power generation prediction, current meteorological conditions, and system power consumption, controls the stored power of the electric energy storage unit, system power generation and power consumption, and the working mode under special conditions, regulates the overall energy of the system, changes the charge and discharge state of the electric energy storage unit, and reduces unnecessary power consumption of some system components. During the entire observation period, the remaining power of the electric energy storage unit always remains above 5AH ( Figure 6 ), and there are no problems such as insufficient power supply, too low and unstable power supply voltage.

[0134] During the period from August 9th to August 21st, 2024, the weather conditions are mainly sunny and cloudy, and August 14th is rainy and cloudy. The daily power generation on sunny and cloudy days in August is about between 3-6kwh, and the daily power generation on rainy and cloudy days is about between 1-2kwh. The highest daily power generation during this period is on August 15th, reaching 5.31kwh, and the lowest is on August 14th, with a daily power generation of 1.66kwh ( Figure 9 )。

[0135] The specific embodiments described above have elaborated in detail the technical solutions and beneficial effects of the present invention. It should be understood that the above is only the most preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, supplements, equivalent substitutions, etc. made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A self-powered and energy management system for hyperspectral remote sensing equipment of atmospheric composition, characterized in that: It includes a hardware subsystem and an intelligent management software subsystem, wherein the hardware subsystem includes an industrial computer as the core, a clean and low-carbon energy collection module, a system energy intelligent control module, an environmental parameter collection and perception module, and an instrument status monitoring module; The clean low-carbon energy collection module is used to collect solar energy and wind energy; The system energy intelligent control module is used to control the clean low-carbon energy collection module to collect and store solar energy and wind energy, and control the power supply to each part of the system; The environmental parameter acquisition and perception module is used to collect sky cloud images and meteorological element data; The instrument status monitoring module is used to monitor the energy flow status and operation status of each part of the system; The industrial computer is integrated with a system intelligent management software subsystem, including intelligent power management subsoftware and parameter acquisition and data analysis subsoftware, which are used to control the operation of various hardware parts in the self-power supply and energy management system to realize various functions.

2. The self-power supply and energy management system for the atmospheric composition hyperspectral remote sensing device according to claim 1, characterized in that: The clean and low-carbon energy collection module includes a solar panel, a three-dimensional motion platform for real-time tracking of the sun's position, and a wind turbine; The solar panel is fixed on the three-dimensional motion platform for real-time tracking of the sun's position through a plane clamping device, and under the adjustment of the three-dimensional motion platform for real-time tracking of the sun's position, it keeps itself vertical to the direction of direct sunlight in real time, so as to obtain the maximum photovoltaic power generation power in real time, receive solar energy and convert it into electrical energy; The real-time tracking three-dimensional motion platform for the sun's position is controlled by the industrial computer to adjust the optimal pitch and azimuth posture in real time, so that the plane of the solar panel and the direction of the direct sunlight remain vertical in real time, and when the sun drops below the horizon, the real-time tracking three-dimensional motion platform for the sun's position returns to zero position and waits for the sun to rise the next day before resuming work; The wind turbine drives the rotor blades to rotate by wind force, and finally converts the wind energy into electrical energy.

3. The self-power supply and energy management system for the atmospheric composition hyperspectral remote sensing device according to claim 2, characterized in that: The system energy intelligent control module includes a solar charge and discharge controller, an AC / DC conversion submodule, a DC integrated output submodule, and an electric energy storage unit; The solar charge and discharge controller acts as an intermediate bridge between the solar panel and the electric energy storage unit, regulates and controls the charging and discharging conditions of the large-capacity electric energy storage unit, and controls the electric energy output of the solar panel and the electric energy storage unit according to the partial power demand; The AC / DC conversion submodule serves as an intermediate bridge between the wind turbine and the electric energy storage unit, and is used to convert the AC power collected by the wind turbine into DC power and store it in the electric energy storage unit as a supplementary energy source to supply energy to the system; The DC integrated output submodule acts as an intermediate bridge between the power storage unit and each part, and is used to provide each part with a corresponding adaptive voltage according to the power supply requirements of the part, while controlling the power transmission of each part, and switching the power source according to the internal and external conditions of the system; The electric energy storage unit is used to store the electric energy collected by the solar panels and the wind generator. When the real-time power generation is greater than the system power consumption, the excess power generation will be stored; when the real-time power generation is less than the system power consumption, the solar panels, wind generator and the electric energy storage unit will supply power to the system at the same time; when the real-time power generation is zero, the electric energy storage unit will supply power to the system alone.

4. The self-power supply and energy management system for the atmospheric composition hyperspectral remote sensing device according to claim 1, characterized in that: The environmental parameter acquisition and perception module includes an image acquisition submodule and an integrated meteorological element collector. The image acquisition submodule is used to capture the sky clouds under the control of the industrial computer to obtain the sky cloud images; The integrated meteorological element collector includes a solar radiation sensor, a rainfall sensor, a temperature sensor, a humidity sensor, a wind speed measurement sensor, and an atmospheric pressure detection sensor, which are respectively used to collect the intensity of direct solar radiation and the intensity of scattered solar radiation, monitor real-time precipitation, collect ambient temperature and solar panel surface temperature, collect ambient humidity, measure wind speed, and measure atmospheric pressure in the usage scenario.

5. The self-power supply and energy management system for the atmospheric composition hyperspectral remote sensing device according to claim 3, characterized in that: The instrument status monitoring module includes an electric energy information processing submodule and a system operation status detection submodule; The electric energy information processing submodule is used to monitor the energy flow status of each part of the system, including photovoltaic input voltage, photovoltaic input current, and power consumption of each part of the system; it is also used to control the overall energy flow direction, and cooperate with the software subsystem to control the charging and discharging of the electric energy storage unit and the operating power consumption of each part of the system; The system operation status detection submodule is used to detect the operation status of solar panels and electric energy storage units and diagnose faults, take timely protection measures for hot spots, hidden cracks and power attenuation of solar panels and abnormal voltage output, abnormal temperature, overcharge and over-discharge of electric energy storage units, and issue early warnings through industrial computers.

6. The self-power supply and energy management system for the atmospheric composition hyperspectral remote sensing device according to claim 3, characterized in that: The industrial computer analyzes the thickness and area of ​​clouds based on the sky cloud image and evaluates the obstruction to the sun, predicts the power generation of the solar panel based on the obstruction situation, and calculates the current solar azimuth and altitude based on the current time and the longitude and latitude of the system installation site. The industrial computer also calculates the solar azimuth based on the current solar azimuth and altitude to track the optimal pitch and azimuth posture of the three-dimensional motion platform in real time to control and adjust the angle of collecting solar energy.

7. The self-power supply and energy management system for the atmospheric composition hyperspectral remote sensing device according to claim 1, characterized in that: The parameter acquisition and data analysis sub-software can display and record in real time the solar power generation power, wind power generation power, instantaneous status of the electric energy storage unit, and power consumption of each module collected by the system, wherein the instantaneous status of the electric energy storage unit includes the output voltage of each battery cell, the internal temperature of the battery cell, the switch MOS state, the total output voltage, the total output current, and the output voltage fluctuation range; The intelligent power management sub-software can automatically adjust the system working mode, intelligently monitor the system energy flow, allocate and manage the overall power consumption of the system in combination with future condition prediction and system status, control the overall power consumption level of the system, and ensure long-term stable observation of the system.

8. The self-power supply and energy management system for the atmospheric composition hyperspectral remote sensing device according to claim 6, characterized in that: The intelligent power management sub-software is implemented based on an intelligent power management algorithm, wherein the intelligent power management algorithm includes a system power monitoring algorithm, a future power generation prediction algorithm, and a system power consumption and power management algorithm; The system power monitoring algorithm is used to obtain the power flow status of the system. Specifically, the average power generation and the total daily power generation are calculated based on the instantaneous input voltage and input current of the system, and the average power consumption and the total daily power consumption are calculated based on the instantaneous output voltage and output current of the system. It detects the voltage and current fluctuation range of each part of the system, and issues a warning signal when a circuit problem is found. It also issues a warning when the performance of the energy storage unit drops to a certain level by comparing the discharge characteristic curve of the energy storage unit under working conditions with the original discharge characteristic curve.

9. The self-power supply and energy management system for the atmospheric composition hyperspectral remote sensing device according to claim 8, characterized in that: The future power generation prediction algorithm is used to predict future power generation, including a solar power generation prediction algorithm and a wind power generation prediction algorithm; The solar power generation prediction algorithm uses the solar power generation prediction model constructed by LSTM to predict the solar power generation at future moments. The input data of the solar power generation prediction model includes cloud data, outdoor temperature, humidity, near-surface solar radiation intensity, real-time wind speed and wind direction, solar panel surface temperature, and atmospheric pressure; The wind power generation prediction algorithm uses a wind power generation prediction model constructed by LSTM to predict wind power generation at future times. The input data of the wind power generation prediction model includes wind speed, wind direction, and temperature.

10. The self-power supply and energy management system for the atmospheric composition hyperspectral remote sensing device according to claim 8, characterized in that: The system power consumption and power management algorithm is used to monitor the input and output energy of the management system in real time, and comprehensively control the power distribution and utilization of the system based on the predicted solar power generation in the next few hours, the power of the power storage unit, the changes in meteorological elements in the next few hours, and the current operating status of the system, including: Taking the current time, the latitude and longitude of the instrument installation site, and weather conditions as input, the azimuth and pitch angles of the solar panels are changed by controlling the sun's position in real time to track the three-dimensional motion platform, thereby increasing the real-time solar power generation power. Taking weather conditions as input, the direction of power flow in the system is changed by controlling the solar charge and discharge controller, wind turbine generator, and power storage unit to maximize the energy utilization of the system; Taking partial power demand and the state of the power storage unit as input, the charging and discharging state of the power storage unit is changed by controlling partial energy consumption and the switch of the power storage unit, thereby reducing the problems of overcharging, over-discharging and performance degradation of the power storage unit; Taking weather conditions and cloud data as input, the system controls the sun's position to track the three-dimensional motion platform, atmospheric composition hyperspectral remote sensing equipment, and industrial computer operating status in real time, adjusts the system's operating mode according to different weather conditions, and reduces unnecessary system energy loss.

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