Carbon flux real-time dynamic interaction and regulation platform based on sky tower ground well observation

Abstract

The invention discloses a carbon flux real-time dynamic interaction and regulation and control platform based on sky tower ground well observation. The platform comprises a sky tower ground well comprehensive observation and data acquisition module, a regulation and control module and an interactive visualization and decision support module. The sky tower ground well comprehensive observation and data acquisition module comprises a satellite remote sensing module, an unmanned aerial vehicle remote sensing module, a vortex covariance flux tower, a ground observation module and an underground observation module; the regulation and control module gives an instruction for regulating and controlling carbon emission and / or carbon sink and a dynamic regulation and control mechanism according to the data acquired by the data acquisition module; the interactive visualization and decision support module provides support for intelligent decision in the carbon emission and carbon sink management process through a visual and real-time graphical interface. According to the invention, carbon emission and carbon sink conditions of a coal mining area can be observed in real time, management measures are automatically adjusted through a dynamic regulation and control mechanism, and the accuracy, efficiency and flexibility of carbon flux observation and regulation and control are significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of data interactive control and visualization technology, and in particular to a real-time dynamic interaction and control platform for carbon flux based on "sky tower ground well" observation. Background Art

[0002] In the development and management of coal mining areas, the dynamic changes in carbon emissions and carbon sinks are crucial to maintaining a balance between environmental protection and resource utilization. In recent years, with growing environmental awareness, despite the development of some integrated observation technologies, such as sky-to-ground, sky-to-ground well, and sky-tower-to-ground, long-term, real-time, and high-precision carbon emissions and sinks observations remain infeasible. As a result, existing carbon emissions and sinks observation platforms mostly remain at the data collection and analysis level, lacking real-time, dynamic control capabilities. This makes it impossible to make immediate adjustments to carbon emissions and sinks based on actual data, and even more unable to meet the dynamic optimization needs of carbon emissions and sinks under varying environmental conditions.

[0003] Specifically, the existing carbon emission and carbon sink observation platforms have the following defects:

[0004] Insufficient decision-making support: The existing carbon emission and carbon sink observation platform mainly relies on a single data source, which makes it difficult to provide a comprehensive analysis of carbon emissions and carbon sinks. It lacks intelligent decision-making support and cannot effectively respond to sudden changes in carbon emissions and carbon sinks.

[0005] Imperfect visualization and interaction functions: Although the existing carbon emission and carbon sink observation platform provides a visual display of carbon emission and carbon sink observation results, it is limited to static data charts and trend analysis, lacks interactivity and dynamism, and cannot achieve real-time interaction with users.

[0006] Lack of dynamic response mechanism: The existing carbon emission and carbon sink observation platform lacks an intelligent dynamic regulation mechanism for carbon emissions and carbon sinks.

[0007] Therefore, there is an urgent need for a comprehensive platform that can realize multi-source data fusion, real-time dynamic regulation, intelligent decision support and interactive visualization display to adapt to the complexity and dynamics of carbon emissions and carbon sink observations in coal mining areas. Summary of the Invention

[0008] To overcome the above-mentioned shortcomings, the present invention provides a real-time dynamic interaction and control platform for carbon flux based on "sky-tower-ground-well" observations, which utilizes the integration and dynamic control algorithms of multi-source heterogeneous data to provide real-time, intelligent, and interactive monitoring and decision-making support. The present invention aims to solve the problems of data silos, slow response, lack of dynamic control, and insufficient decision-making support in the existing technology for carbon emission and carbon sink management. Among them, "sky-tower-ground-well" refers to the general term for multi-source data collected at the sky level, tower level, and ground-well level.

[0009] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0010] A real-time dynamic interaction and control platform for carbon flux based on "Sky Tower Ground Well" observations, including a "Sky Tower Ground Well" integrated observation and data acquisition module, a control module, and an interactive visualization and decision support module;

[0011] The data acquisition module includes a satellite remote sensing module, an unmanned aerial vehicle remote sensing module, an eddy covariance flux tower, a ground observation module and an underground observation module;

[0012] The control module controls carbon emissions and / or carbon sinks in real time based on the data acquired by the data acquisition module, thereby reducing carbon emissions and improving carbon sink capacity;

[0013] The interactive visualization and decision support module provides support for intelligent decision-making in the process of carbon emission and carbon sink management through an intuitive and real-time graphical interface.

[0014] Preferably, the carbon emission control strategy of the control module is as follows:

[0015]

[0016] in, Indicates the intensity of carbon emission regulation; α is the carbon emission regulation coefficient, and ΔE(t) is the carbon emission deviation value;

[0017] The carbon sequestration control strategy of the control module is as follows:

[0018]

[0019] in, represents the intensity of carbon sink regulation; β is the carbon sink regulation coefficient, and ΔC(t) is the carbon sink deviation value;

[0020] Based on equations (3) and (4), the control module adjusts the carbon emission and carbon sink targets according to real-time observation data and generates corresponding control plans.

[0021] Furthermore, the control module includes intelligent optimization of the control scheme, and the intelligent optimization goal of the control scheme is:

[0022]

[0023] The above formula represents minimizing the sum of squares of carbon emissions and carbon sink deviations.

[0024] Furthermore, the intelligent optimization of the control scheme adopts an adaptive optimization mechanism based on deep learning according to the feedback results, and optimizes the control decision by training through reinforcement learning history and real-time data.

[0025] Preferably, the feedback result is as follows:

[0026] F adjust (t)=|ΔE(t)|+|ΔC(t)| (6)

[0027] When F adjust (t) When the preset threshold is exceeded, the control module triggers intelligent optimization;

[0028] The intelligent optimization is based on the deep Q reinforcement learning model. The optimization formula of the deep Q reinforcement learning model is as follows:

[0029] Q(s,a)=R(s,a)+γ·max a Q(s′,a′) (7)

[0030] Where Q(s,a) represents the expected reward for taking action a in state s; R(s,a) is the immediate reward; γ is the discount factor that determines the impact of future rewards; s′ is the next state, and a′ is the optimal next action.

[0031] Preferably, the carbon emission deviation is calculated as follows:

[0032] ΔE(t)=E pollution (t)-E target (t) (1)

[0033] Where ΔE(t) is the carbon emission deviation at time t; E pollution (t) is the actual carbon emission concentration; E target (t) is the set target carbon emission concentration;

[0034] The carbon sink deviation is calculated as follows:

[0035] ΔC(t)=C sink (t)-C target (t) (2)

[0036] Where ΔC(t) is the carbon sink deviation at time t; C sink (t) is the actual carbon sink capacity; C target (t) is the target carbon sink capacity.

[0037] Preferably, the interactive visualization and decision support module includes:

[0038] Data input and real-time display: The front-end interface presents the received multi-source data in real time, and based on the fused data, displays the following graphical information: carbon emission heat map, carbon sink distribution map, and carbon emission and carbon sink dynamic change trend map;

[0039] Interactive adjustment and real-time feedback: providing an interactive interface for adjusting control parameters and selecting different management objectives, and displaying the adjusted responses. The control parameters include: carbon emission targets and carbon sequestration compensation plans;

[0040] Decision support and optimization suggestions. Based on real-time data and optimization algorithms, the platform not only provides a visual display of monitoring results, but also generates optimization suggestions through intelligent algorithms. The suggestions include: automatically giving adjustment suggestions based on the current carbon emissions and carbon sink status of the mining area, and displaying the expected effects of these adjustments in real time.

[0041] Preferably, the decision support and optimization suggestions include model prediction and decision support, which are based on historical data and real-time data, and predict future carbon emissions and carbon sink changes through machine learning models or time series analysis models. According to the prediction results, the carbon emission and carbon sink targets are automatically adjusted to avoid exceeding carbon emissions standards or insufficient carbon sink capacity.

[0042] Preferably, the model prediction uses an ARIMA or LSTM model for time series prediction, and the ARIMA or LSTM model outputs the following predicted values:

[0043]

[0044] Among them, f pollution and f sink It is a prediction model, t represents the current moment, and n is the length of the time window of historical data.

[0045] Furthermore, the decision support and optimization suggestions generate an optimal strategy based on model optimization, and the optimization objectives of the optimal strategy are as follows:

[0046]

[0047] The above formula aims to minimize the difference between carbon emission and carbon sink targets simultaneously, and finds the optimal solution through an optimization algorithm, which is a gradient descent method or linear programming.

[0048] The beneficial effects of the present invention are as follows:

[0049] 1. This invention can acquire multi-source data in real time and automatically adjust carbon emission and carbon sink management measures according to real-time changes, significantly improving the accuracy, efficiency and flexibility of carbon flux observation and regulation.

[0050] 2. Through the adaptive control mechanism and optimization algorithm, the platform of the present invention can intelligently generate control plans, reduce manual intervention and improve decision-making efficiency.

[0051] 3. Through multi-source data fusion and spatiotemporal synchronization, the platform of this invention can provide more accurate carbon emission and carbon sink observation results, helping managers to carry out efficient regulation.

[0052] 4. The interactive interface of the present invention enhances the user experience, and managers can intuitively view data changes and quickly adjust control strategies.

[0053] 5. The present invention combines feedback mechanisms with optimization algorithms to ensure continuous optimization of carbon emission and carbon sink management measures. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is described in further detail below.

[0055] The embodiment discloses a real-time dynamic interaction and control platform for carbon flux based on "sky tower ground well" observation (hereinafter referred to as "platform"), which is as follows:

[0056] 1. Platform architecture design:

[0057] This platform is based on the integrated carbon emission and carbon sink observation technology of "Sky Tower Ground Well", and uses the following data sources:

[0058] The "sky" level includes two major modules. Satellite remote sensing module: using multi-spectral, high-resolution remote sensing satellites to periodically scan the mining area and its surroundings to obtain environmental parameters such as vegetation index, soil moisture, and temperature, and then predict changes in carbon flux in the mining area. Especially when conducting large-scale, long-term dynamic observations, satellite remote sensing can provide carbon flux data for the mining area and surrounding areas, filling the gap in large-scale observation technology in the mining area, and is suitable for continuously obtaining macro data on carbon flux changes. UAV remote sensing module: in areas where satellite data updates are insufficient or the local environment is complex, drones equipped with high-precision sensors are used to supplement the collection of local environmental images and radiation data. It is particularly suitable for short-term, dynamic observations in local areas of the mining area, and has unique advantages in the detailed observation of local hot spots.

[0059] "Tower" level: Eddy covariance flux towers are deployed in key areas of the mining area. With the help of gas flow sensors, temperature and humidity sensors, weather stations and other equipment, wind speed, temperature, carbon dioxide and other greenhouse gas concentration data are collected in real time, and the carbon gas exchange flux in the atmosphere is calculated. The eddy covariance flux tower can observe the carbon emissions and carbon sink changes in the mining area in real time. Its observation range is usually 1.5-2 times the height of the tower, which depends on the surface roughness and turbulence characteristics. By measuring the wind speed and gas concentration time series data at high frequency and calculating the covariance between the two, the vertical exchange rate of the gas in the turbulence can be obtained, and then the gas flux in the vertical direction can be estimated.

[0060] The "ground and well" level includes two major modules. Ground observation module: Deployment of fixed or mobile sensor networks, combined with regular manual sampling, can accurately capture the release and absorption of carbon in the mining area, including the impact of mining activities on the carbon sequestration capacity of soil and vegetation, and real-time observation of key indicators of carbon emissions and carbon absorption in the surface environment. Downhole observation module: The underground observation module is similar to the ground. High-temperature, waterproof, moisture-proof, and dust-resistant explosion-proof carbon flux observation sensors are deployed in key areas underground. Combined with manual sampling, underground carbon flux data and other related environmental parameters are obtained.

[0061] The above multi-source data are integrated and fused through the platform to achieve comprehensive monitoring of carbon emissions and carbon sinks in coal mining areas.

[0062] 2. Real-time dynamic interactive control mechanism

[0063] One of the platform's core innovations is its real-time dynamic control mechanism, designed to automatically generate and adjust carbon emission and sink management measures based on real-time, multi-source carbon emission and sink data. Using intelligent algorithms, the platform automatically generates and optimizes management plans based on real-time changes in carbon emissions and sinks within coal mining areas, ensuring real-time control and optimization of carbon emissions and sinks, thereby improving the efficiency and responsiveness of environmental management.

[0064] The core goal of the real-time dynamic control mechanism is to dynamically adjust carbon emissions and carbon sink management measures within coal mining areas. Specific objectives include: carbon emission control. When the platform observes that carbon emissions in a certain area exceed a preset threshold, it automatically initiates control measures, such as increasing the absorption of fugitive gas, to reduce carbon emissions; and carbon sink adjustment. When carbon sink capacity is insufficient, the platform automatically adjusts carbon sink compensation areas or introduces new carbon sink measures to enhance carbon sink capacity, such as carbon storage in goaf areas.

[0065] However, practical applications face the following technical challenges: First, data timeliness and real-time performance. Carbon emissions and carbon sinks change rapidly, requiring real-time data streams to support rapid decision-making. Second, regulatory response accuracy requires precise adjustments to carbon emission and sink management measures based on real-time data to maximize carbon emission reductions and enhance carbon sink capacity.

[0066] The working principle of the real-time dynamic control mechanism is based on the following main steps:

[0067] Data input and real-time observation: the platform collects carbon emission, carbon sink and meteorological data from multiple sources (including satellite remote sensing, drone images, ground and underground sensors, etc.). pollution (t), represents the carbon emission concentration at time t; carbon sink capacity C sink (t), represents the carbon sink capacity at time t.

[0068] Target setting and deviation calculation. The platform sets the target value of carbon emissions and carbon sinks E target (t) and C target (t), and calculate the deviation between actual carbon emissions and carbon sink capacity and target. Carbon emission deviation is expressed as:

[0069] ΔE(t)=E pollution (t)-E target (t) (1)

[0070] Where ΔE(t) represents the carbon emission deviation at time t; E pollution (t) is the actual carbon emission concentration; E target (t) is the set target carbon emission concentration.

[0071] The carbon sink deviation is expressed as:

[0072] ΔC(t)=C sink (t)-C target (t) (2)

[0073] Where ΔC(t) represents the carbon sink deviation at time t; C sink (t) is the actual carbon sink capacity; C target (t) is the target carbon sink capacity.

[0074] The platform will automatically generate a control strategy based on the deviation between carbon emissions and carbon sinks. Specifically, the platform will generate an adjustment strategy based on the size of the deviation value to quickly respond to excessive carbon emissions or insufficient carbon sinks. The control strategy calculation formula is as follows:

[0075]

[0076] in, It represents the intensity of carbon emission regulation; α is the regulation coefficient, which indicates the adjustment range, and its value is based on historical data and optimization model; ΔE(t) is the deviation value of carbon emissions.

[0077] The carbon sequestration adjustment strategy is as follows:

[0078]

[0079] in, represents the intensity of carbon sink regulation; β is the carbon sink regulation coefficient, which represents the intensity of improving carbon sink capacity; ΔC(t) is the deviation value of carbon sink.

[0080] Through the above formula, the platform intelligently adjusts carbon emission and carbon sink targets based on real-time observation data and generates corresponding control plans.

[0081] Intelligent optimization and updating of control schemes. This embodiment also introduces an intelligent optimization algorithm to optimize the control strategy in real time. After the control strategy is executed, the platform will further optimize the control measures based on the feedback data to ensure the accuracy of the control results. The optimization goals are as follows:

[0082]

[0083] The optimization goal is to minimize the sum of the squares of carbon emissions and carbon sink deviations. In this way, the platform can dynamically adjust the management plan to achieve the optimization of carbon emissions and carbon sink management.

[0084] Intelligent feedback and adjustment mechanism. The purpose of the feedback mechanism is to continuously evaluate the control effect and adjust the control strategy based on this. The feedback result is calculated by the following formula:

[0085] F adjust (t)=|ΔE(t)|+|ΔC(t)| (6)

[0086] If F adjust (t) If the preset threshold is exceeded, it means that the current carbon emission and carbon sink adjustment effects are not ideal. The platform will trigger the re-optimization process and readjust the strategy.

[0087] After receiving real-time feedback, the platform uses a deep learning-based adaptive optimization mechanism to train historical and real-time data through reinforcement learning and other technologies to optimize control decisions. The algorithm is based on the Deep Q Reinforcement Learning model, which continuously improves decision accuracy based on environmental changes and data feedback. The optimization formula of the Deep Q Learning model (DQN) is as follows:

[0088] Q(s,a)=R(s,a)+γ·max a Q(s′,a′) (7)

[0089] Where Q(s,a) represents the expected reward for taking action a in state s; R(s,a) is the immediate reward; γ is a discount factor that determines the impact of future rewards; s′ is the next state, and a′ is the optimal next action. This model dynamically adjusts carbon emission and carbon sink management measures using real-time data and feedback.

[0090] The platform utilizes a dynamic control mechanism. Using artificial intelligence and optimization algorithms, it intelligently predicts future trends in carbon emissions and carbon sinks based on real-time observation data. When the platform detects excessive carbon emissions or insufficient carbon sinks, it automatically generates control measures and pushes them to mine managers. These control measures include adjusting mining intensity to reduce carbon emissions; increasing carbon sink compensation areas to enhance carbon sequestration capacity; and modifying production processes or introducing green technologies to reduce carbon emissions. Through this real-time dynamic control mechanism, the platform can effectively respond to sudden carbon emission issues and provide managers with optimal control solutions.

[0091] Automatic feedback and optimization: After adjustments are made, the platform tracks the effects of the adjustments in real time and adjusts management strategies based on new data feedback. Through this feedback mechanism, the platform can continuously optimize carbon emission and carbon sink management measures to ensure optimal environmental benefits.

[0092] 3. Interactive visualization and decision support

[0093] The interactive visualization and decision support system uses an intuitive, real-time graphical interface to help coal mine managers make intelligent decisions in carbon emissions and carbon sink management. Through the interactive interface, users can view real-time trends in carbon emissions and carbon sinks, analyze data, adjust management strategies, and visualize the effects of these adjustments. The platform supports real-time feedback and historical data backtracking, allowing managers to adjust plans based on different carbon emission targets, carbon sink targets, and specific mine site conditions, optimizing the decision-making process. The working principle is as follows:

[0094] The first is data input and real-time display. The platform receives multi-source data (such as carbon emissions, carbon sinks, and meteorological data) and presents it in real time through the front-end interface. Based on the integrated data, the platform displays the following graphical information:

[0095] Carbon emission heat map: displays the intensity of carbon emissions in different areas within the mining area, helping to identify carbon emission hotspots;

[0096] Carbon sink distribution map: shows the spatial distribution of carbon sinks within the mining area, helping managers understand the overall carbon sink capacity and which areas have insufficient carbon sinks;

[0097] Carbon emissions and carbon sinks dynamic change trend chart: Displays the changing trends of carbon emissions and carbon sinks based on real-time observation data, helping decision makers understand the dynamic balance of carbon emissions and carbon sinks.

[0098] Interactive adjustment and real-time feedback: users can adjust control parameters, select different management goals, and observe the system response after adjustment through the platform's interactive interface. Specific interactive functions include:

[0099] Carbon emission target adjustment: Users can adjust the carbon emission target of the mining area based on real-time carbon emission data (E target (t));

[0100] Adjustment of carbon sink compensation plan: Users can choose to adjust the carbon sink compensation plan and dynamically change the compensation area according to the actual situation of carbon sinks.

[0101] Decision support and optimization recommendations. Based on real-time data and optimization algorithms, the platform not only provides a visual display of monitoring results but also generates optimization recommendations through intelligent algorithms. These recommendations are based on the current carbon emissions and carbon sink status of the mining area, automatically providing adjustment suggestions and displaying the expected effects of these adjustments in real time. For example, the system can generate control plans for different scenarios based on historical and current data, calculate the adjusted carbon emissions and carbon sink trends, and provide decision support to managers.

[0102] Model prediction and decision support, in addition to real-time decision support, this embodiment also includes model-based prediction functions. The platform can predict future carbon emissions and carbon sink changes based on historical data and real-time data through machine learning models or time series analysis models. According to the prediction results, the system automatically adjusts the carbon emission and carbon sink targets to avoid excessive carbon emissions or insufficient carbon sink capacity. Use ARIMA (Auto-Regressive Integrated Moving Average) or LSTM (Long Short-Term Memory) models for time series prediction. The model output prediction value is and

[0103]

[0104] Among them, f pollution and f sink It is a prediction model, t represents the current moment, and n is the length of the time window of historical data.

[0105] In the platform's intelligent forecasting and optimization module, the system combines historical and real-time data to predict carbon emissions and carbon sinks over the next period of time. This forecast provides data support for managers, enabling decision-makers to make adjustments in advance.

[0106] Forecasting and Adjusting Strategies. Forecasting based on historical data: By analyzing historical data (such as carbon emissions and meteorological conditions), the system uses machine learning models to predict future carbon emissions and carbon sinks. Strategy Generation and Optimization: The platform generates the optimal control plan based on the forecast results and optimizes the plan to ensure that the set carbon emission and carbon sink targets are achieved within the future time period.

[0107] The platform generates the best strategy based on the mathematical model of the optimization suggestion to minimize the deviation of carbon emissions and carbon sinks. The optimization objectives are:

[0108]

[0109] The objective function aims to simultaneously minimize the difference between carbon emission and carbon sink targets, and find the optimal solution through optimization algorithms (such as gradient descent, linear programming, etc.).

[0110] The platform monitors carbon emissions and carbon sinks in coal mining areas in real time and automatically adjusts management measures through a dynamic control mechanism, ensuring rapid response to changing environmental conditions. Furthermore, the platform provides an interactive visual interface, enabling managers to intuitively adjust management plans in real time and generate optimization recommendations. The platform combines integrated observation technology from sky towers, underground wells, and advanced data fusion algorithms to ensure high accuracy and efficiency in carbon emissions and sink management.

[0111] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A real-time dynamic interaction and control platform for carbon flux based on "Sky Tower and Ground Well" observation, characterized by: It includes the "Sky Tower and Ground Well" comprehensive observation and data acquisition module, control module, and interactive visualization and decision support module; The "Sky Tower and Ground Well" comprehensive observation and data acquisition module includes a satellite remote sensing module, a UAV remote sensing module, an eddy covariance flux tower, a ground observation module, and an underground observation module; The control module controls carbon emissions and / or carbon sinks in real time based on the data acquired by the data acquisition module, thereby reducing carbon emissions and improving carbon sink capacity; The interactive visualization and decision support module provides support for intelligent decision-making in the process of carbon emission and carbon sink management through an intuitive and real-time graphical interface.

2. The carbon flux real-time dynamic interaction and control platform based on "sky tower ground well" observation according to claim 1 is characterized in that, The carbon emission control strategy of the control module is as follows: in, Indicates the intensity of carbon emission regulation; α is the carbon emission regulation coefficient, and ΔE(t) is the carbon emission deviation value; The carbon sequestration control strategy of the control module is as follows: in, represents the intensity of carbon sink regulation; β is the carbon sink regulation coefficient, and ΔC(t) is the carbon sink deviation value; Based on equations (3) and (4), the control module adjusts the carbon emission and carbon sink targets according to real-time observation data and generates corresponding control plans.

3. The carbon flux real-time dynamic interaction and control platform based on "sky tower ground well" observation according to claim 2 is characterized in that, The control module includes intelligent optimization of the control scheme, and the intelligent optimization goal of the control scheme is: The above formula represents minimizing the sum of squares of carbon emissions and carbon sink deviations.

4. The carbon flux real-time dynamic interaction and control platform based on "sky tower ground well" observation according to claim 3 is characterized in that: The intelligent optimization of the control scheme adopts an adaptive optimization mechanism based on deep learning according to the feedback results, and optimizes the control decision by training through reinforcement learning history and real-time data.

5. The carbon flux real-time dynamic interaction and control platform based on "sky tower ground well" observation according to claim 4 is characterized in that, The feedback results are as follows: F adjust (t)=|ΔE(t)|+|ΔC(t)| (6) When F adjust (t) When the preset threshold is exceeded, the control module triggers intelligent optimization; The intelligent optimization is based on the deep Q reinforcement learning model. The optimization formula of the deep Q reinforcement learning model is as follows: Q(s,a)=R(s,a)+γ·max a Q(s′,a′) (7) Where Q(s,a) represents the expected reward for taking action a in state s; R(s,a) is the immediate reward; γ is the discount factor that determines the impact of future rewards; s′ is the next state, and a′ is the optimal next action.

6. The carbon flux real-time dynamic interaction and control platform based on "sky tower ground well" observation according to any one of claims 2 to 5 is characterized in that: The carbon emission deviation is calculated as follows: ΔE(t)=E pollution (t)-E target (t) (1) Where ΔE(t) is the carbon emission deviation at time t; E pollution (t) is the actual carbon emission concentration; E target (t) is the set target carbon emission concentration; The carbon sink deviation is calculated as follows: ΔC(t)=C sink (t)-C target (t) (2) Where ΔC(t) is the carbon sink deviation at time t; C sink (t) is the actual carbon sink capacity; C target (t) is the target carbon sink capacity.

7. The carbon flux real-time dynamic interaction and control platform based on "sky tower ground well" observation according to claim 1 is characterized in that: The interactive visualization and decision support module includes: Data input and real-time display: The front-end interface presents the received multi-source data in real time, and based on the fused data, displays the following graphical information: carbon emission heat map, carbon sink distribution map, and carbon emission and carbon sink dynamic change trend map; Interactive adjustment and real-time feedback: providing an interactive interface for adjusting control parameters and selecting different management objectives, and displaying the adjusted responses. The control parameters include: carbon emission targets and carbon sequestration compensation plans; Decision support and optimization suggestions. Based on real-time data and optimization algorithms, the platform not only provides a visual display of monitoring results, but also generates optimization suggestions through intelligent algorithms. The suggestions include: automatically giving adjustment suggestions based on the current carbon emissions and carbon sink status of the mining area, and displaying the expected effects of these adjustments in real time.

8. The carbon flux real-time dynamic interaction and control platform based on "sky tower ground well" observation according to claim 7 is characterized in that: The decision support and optimization suggestions include model prediction and decision support. The model prediction and decision support are based on historical data and real-time data, and predict future carbon emissions and carbon sink changes through machine learning models or time series analysis models. According to the prediction results, the carbon emission and carbon sink targets are automatically adjusted to avoid exceeding carbon emissions standards or insufficient carbon sink capacity.

9. The carbon flux real-time dynamic interaction and control platform based on "sky tower ground well" observation according to claim 8 is characterized in that: Model prediction uses ARIMA or LSTM models for time series prediction. The ARIMA or LSTM model outputs the following predicted values: Among them, f pollution and f sink It is a prediction model, t represents the current moment, and n is the length of the time window of historical data.

10. The carbon flux real-time dynamic interaction and control platform based on "sky tower ground well" observation according to claim 9 is characterized in that: The decision support and optimization suggestions generate the best strategy based on model optimization. The optimization objectives of the best strategy are as follows: The above formula aims to minimize the difference between carbon emission and carbon sink targets simultaneously, and finds the optimal solution through an optimization algorithm, which is a gradient descent method or linear programming.

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