A carbon emission detection method suitable for Gobi desert areas

Abstract

The present invention relates to a carbon emission detection method suitable for Gobi desert regions, and relates to the field of carbon emission monitoring and detection technology. The method comprises: deploying ground sensors and drones in the Gobi desert region, planning the drone's flight trajectory based on data collected by the ground sensors; calculating and dividing the target region into high- and low-emission zones based on a carbon emission matrix; simultaneously adjusting the drone's flight path to acquire multispectral remote sensing image data; generating an aerial carbon concentration grid based on the multispectral remote sensing image data and data collected by the ground sensors; further, predicting carbon concentration using a spatiotemporal modeling algorithm and a meteorological disturbance modulation mechanism, and adjusting the drone monitoring frequency based on the predicted carbon concentration value; and calculating a comprehensive risk index based on the predicted carbon concentration value to classify the Gobi desert region into risk levels. This method addresses the problem that existing carbon emission detection methods are unable to adapt to the specific environment of the Gobi desert, making it difficult to achieve comprehensive and accurate carbon emission analysis.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission monitoring and detection technology, and in particular to a carbon emission detection method suitable for Gobi desert areas. Background Art

[0002] Gobi deserts are widely distributed in Asia, North Africa, and the Americas. They are characterized by drought, little rainfall, sparse vegetation, and dramatic surface temperature differences. As climate change becomes more serious, the issue of carbon emissions in the Gobi desert is gradually gaining attention. Due to its extreme climatic conditions, such as large temperature differences, frequent sandstorms, and sparse vegetation, the monitoring of carbon emissions in the Gobi desert faces many challenges. Carbon emission sources in the Gobi desert are dispersed and the climate is complex. Traditional carbon emission detection methods struggle to provide accurate real-time data in such environments. Furthermore, traditional carbon emission monitoring methods typically rely on ground sensor networks or meteorological data, which often struggle to guarantee stability and high accuracy in special areas like the Gobi desert. Therefore, how to cope with the complex geographical environment and extreme climatic conditions of the Gobi desert and provide an accurate, real-time, and adaptable carbon emission detection technology has become a technical challenge that needs to be addressed urgently. Summary of the Invention

[0003] The purpose of the present invention is to provide a carbon emission detection method suitable for Gobi desert areas, so as to solve the problem that existing carbon emission detection methods cannot adapt to the specific environment of the Gobi desert, such as drastic temperature changes and frequent extreme weather; and the problem that carbon emission monitoring often relies only on a single data collection method, such as single-point sensors or remote sensing data, lacks a multi-level, multi-dimensional comprehensive monitoring system, and is difficult to achieve comprehensive and accurate carbon emission analysis.

[0004] The carbon emission detection method suitable for Gobi desert areas described in the present invention specifically comprises the following steps:

[0005] S1. Deploy ground sensors and drones in the Gobi Desert. Plan the drone's flight trajectory based on the data collected by the ground sensors. Calculate the target area's carbon emission matrix and, based on the values ​​of the elements in the matrix, divide the target area into high- and low-emission zones. Simultaneously, adjust the drone's flight path to acquire multispectral remote sensing image data. Generate an aerial carbon concentration grid based on the multispectral remote sensing image data and data collected by the ground sensors.

[0006] S2. Based on the airborne carbon concentration grid, a spatiotemporal modeling algorithm and a meteorological disturbance modulation mechanism are used to predict carbon concentrations, and the frequency of drone monitoring is adjusted based on the predicted carbon concentration values.

[0007] S3. Based on the predicted carbon concentration value, calculate the comprehensive risk index and classify the Gobi desert area into risk levels.

[0008] Preferably, the S1 specifically includes:

[0009] When the data collected by the ground sensor exceeds the threshold, the current area is marked as the target area, and the flight mission is triggered to send the UAV to the target area for monitoring and obtain multispectral remote sensing image data.

[0010] Preferably, the S1 specifically includes:

[0011] In the process of calculating the carbon emission matrix of the target area, a two-dimensional scoring matrix is ​​constructed based on the historical carbon emission data and the data collected by ground sensors to represent the carbon emission weight of each target area.

[0012] Preferably, the S1 specifically includes:

[0013] Based on the historical frequency of carbon emission violations, carbon concentration fluctuations and distance factors in the target area, the values ​​of the elements in the carbon emission matrix of the target area are calculated.

[0014] Preferably, the S1 specifically includes:

[0015] According to the values ​​of the elements in the carbon emission matrix of the target area, high and low emission areas are divided; the target area where the values ​​of the elements in the carbon emission matrix are higher than the standard threshold is divided into a high emission area, and the target area where the values ​​of the elements in the carbon emission matrix are lower than the standard threshold is divided into a low emission area.

[0016] Preferably, the S2 specifically includes:

[0017] The predicted carbon concentration value is calculated based on the carbon concentration value at the current time and region, spatial gradient, the rate of change of temperature and humidity over time, and the surface disturbance function extracted from the multispectral remote sensing images obtained by drones.

[0018] Preferably, the S3 specifically includes:

[0019] Based on the predicted carbon concentration values ​​and data collected by ground sensors, a composite risk judgment is made by integrating the temporal variation intensity, spatial gradient mutation and error feedback to obtain a comprehensive risk index.

[0020] Preferably, the S3 specifically includes:

[0021] Based on the comprehensive risk index and regional risk threshold, the Gobi desert area is divided into normal area, slightly fluctuating area, moderately abnormal area and highly abnormal area.

[0022] The beneficial effects of the technical solution of the present invention are:

[0023] 1. It has improved the comprehensiveness and adaptability of carbon emission monitoring in Gobi desert areas. By combining ground sensor networks with drone aerial remote sensing, an integrated air-ground multi-source monitoring system has been constructed. This has made up for the shortcomings of traditional carbon emission detection in desert and sandy environments, such as difficulty in deployment, insufficient coverage, and single data. It has greatly enhanced the adaptability of the carbon emission detection system to the complex environment of the Gobi desert.

[0024] 2. Dynamic, high-precision modeling of carbon emissions distribution was achieved. By constructing a carbon emissions matrix, a multi-dimensional assessment of carbon emission intensity in target areas was achieved, accurately demarcating high- and low-emission zones. Furthermore, drone flight paths were adjusted in real time based on the carbon emissions matrix to maximize monitoring efficiency and improve data coverage and timeliness.

[0025] 3. It has strong carbon concentration prediction and spatiotemporal evolution analysis capabilities, adopts spatiotemporal modeling algorithms and meteorological disturbance modulation mechanisms to predict carbon concentration change trends in advance, realizes preemptive response and resource scheduling in abnormal areas, and provides forward-looking support for energy conservation and emission reduction.

[0026] 4. Through dynamic risk level classification based on a comprehensive risk index, intelligent responses from mild fluctuations to high anomalies can be achieved, providing a decision-making basis for regional governance, drone scheduling optimization, and even manual inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a carbon emission detection method suitable for Gobi desert areas according to the present invention. DETAILED DESCRIPTION

[0028] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0029] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0030] The following describes in detail a specific scheme of a carbon emission detection method suitable for Gobi desert areas provided by the present invention with reference to the accompanying drawings.

[0031] Refer to the attached Figure 1 , which shows a flow chart of a carbon emission detection method suitable for Gobi desert areas provided by one embodiment of the present invention. The method includes the following parts:

[0032] S1. Deploy ground sensors and drones in the Gobi Desert area. Plan the drone's flight trajectory based on the data collected by the ground sensors. Calculate the target area's carbon emission matrix and divide it into high and low emission zones based on the values ​​of the elements in the target area's carbon emission matrix. At the same time, adjust the drone's flight path and obtain multispectral remote sensing image data. Generate an aerial carbon concentration grid based on the multispectral remote sensing image data and data collected by the ground sensors.

[0033] S101. Deploy a ground sensor network. Ground sensors will be installed in various areas of the Gobi Desert. Specifically, in the desert storm belt, CO2 / CH4 gas combination sensors will be deployed using annual average wind speed data from wind corridors. In ecological restoration areas, degraded forests and grasslands and artificial oases will be covered to monitor the carbon sequestration effects of artificially restored areas. In artificially constructed areas, such as desert highways and industrial experimental areas, multi-point array probes will be deployed to monitor anthropogenic emissions. Technical configuration details for the installed ground sensors: Each sensor is equipped with an NDIR CO2 measurement module (high-temperature stable), a methane sensor chip, a PM2.5 sensor, and wind speed and direction modules. The housing features a nano-sand and corrosion-resistant coating, self-cleaning glass windows, and an automatic dust removal mechanism (a small motor and timed vibration). Data is sampled every 30 seconds and transmitted to edge nodes via LoRa and gateway aggregation. It is then uploaded to drones via the public network / satellite.

[0034] Based on the data collected by ground sensors, that is, the actual carbon concentration value obtained, the flight route is planned for the drone. When the monitored data exceeds the threshold, that is, the CO2 concentration value is greater than 480 ppm, the current area is marked as the target area. At this time, the flight mission is triggered and the drone is sent to the target area for further monitoring.

[0035] S102. Use UAV remote sensing technology to conduct detection and obtain multispectral remote sensing image data. The UAV is equipped with the following modules: a multispectral imaging system (infrared / ultraviolet / visible light three-channel) for analyzing surface vegetation / thermal field changes; micro CO2 and CH4 gas probes for aerial carbon gas distribution mapping; a barometer + thermometer and hygrometer for constructing an atmospheric structure model at the flight level.

[0036] Flight strategy and path control: Initial route planning uses a zigzag or spiral pattern to cover the target area. Before flight, the carbon emission monitoring system in the Gobi Desert area will load historical carbon emission maps and historical high wind speed data. High wind speed areas are planned areas in the Gobi Desert area. The carbon emission matrix of the target area is calculated. Specifically, a two-dimensional scoring matrix is ​​constructed based on historical carbon emission data and data collected by ground sensors, denoted as , to represent the carbon emission weight of each target area. The historical carbon emission data comes from the historical carbon emission map loaded by the carbon emission detection system.

[0037] Before using the weighted formula to calculate the elements in each carbon emission matrix, the carbon emission detection system will normalize the data used in the calculation process, such as the carbon concentration and distance of the target area, through normalization methods well known to technicians in this field, such as maximum and minimum normalization, and convert them into dimensionless form to achieve comparability and integration, and ensure the mathematical rationality and engineering applicability of the weighted formula.

[0038] Each element in the carbon emissions matrix Determined by the following weighted formula:

[0039] ,

[0040] in, It is the first in the carbon emission matrix Row and Elements of the column, is the coordinate point of the target area; Represents the target area after normalization The historical frequency of carbon emissions exceeding the standard, target areas The frequency of historical carbon emissions exceeding the standard is derived from historical carbon emissions data, including long-term sampling data from ground sensors and statistics of the number of times carbon concentration thresholds (such as CO2>480 ppm) exceeded in previous drone flight monitoring records. This is combined with historical carbon emissions maps for spatial aggregation and frequency overlay analysis to form a frequency distribution of exceeding standards in the corresponding region. is the target area after normalization The variance of carbon concentration change indicates the fluctuation of carbon concentration; is the target area after normalization The inverse value of the distance to the nearest ground sensor position, i.e., the distance factor; , , The weight coefficient is set according to the expert experience method to control the relative influence of the frequency of historical carbon emission exceeding the standard, carbon concentration fluctuation and distance factor. It is preferably set to , , ; In different implementation scenarios, based on expert experience Dynamic adjustment within the range.

[0041] The normalized score value corresponding to the CO2 concentration threshold, such as 480 ppm, is set as the standard threshold.

[0042] According to the values ​​of the elements in the carbon emission matrix, high and low emission areas are divided. The target areas where the values ​​of the elements in the carbon emission matrix are higher than the standard threshold are divided into high emission areas, and the target areas where the values ​​of the elements in the carbon emission matrix are lower than the standard threshold are divided into low emission areas.

[0043] The carbon emission detection system in the entire Gobi desert area will dynamically adjust the drone's flight path: automatically increase the flight altitude in high wind speed areas; high emission areas are key monitoring targets, and the carbon emission detection system will automatically increase the monitoring frequency and flight coverage priority in high emission areas; reduce the frequency in low emission areas; each flight lasts 30-45 minutes, and the acquired data is uploaded in real time along the way.

[0044] Combining drone remote sensing with ground sensors overcomes the complex Gobi Desert geography that hinders data collection and enables carbon emissions monitoring. Multispectral remote sensing image data acquired by drones and data from ground sensors are processed in real time by an onboard AI chip, compressed into block coordinates, and then generated using existing technology to create an "aerial carbon concentration grid" that is uploaded to S2.

[0045] S2. Based on the airborne carbon concentration grid, a spatiotemporal modeling algorithm and a meteorological disturbance modulation mechanism are used to predict carbon concentrations, and the frequency of drone monitoring is adjusted based on the predicted carbon concentration values.

[0046] Based on the constructed "aerial carbon concentration grid", the carbon emission detection system in the Gobi desert area further adopts spatiotemporal modeling algorithms and meteorological disturbance modulation mechanisms to predict carbon concentration and output the carbon concentration evolution trend and distribution map of a certain area in the future.

[0047] Before predicting the carbon concentration, the data and parameters required in the prediction process, such as the carbon concentration value of the area at the current moment, the current wind speed, temperature, humidity, etc., are de-dimensionalized using preprocessing methods well known to those skilled in the art, such as min-max or Z-score normalization, to ensure dimensional consistency during the prediction process.

[0048] The carbon concentration prediction formula is:

[0049] ,

[0050] in, In the future In the area Predicted carbon concentration values; It is at the present moment In the area Carbon concentration value after pretreatment; is the current concentration weighting factor, determined by experimental data; It is the current wind speed after preprocessing (including direction synthesis). The current wind speed is measured in real time by the wind speed and wind direction module installed on the ground sensor; It is the square of the current wind speed after preprocessing, which means that the stronger the wind, the stronger the driving force on carbon gas diffusion; Indicates the spatial unevenness of regional carbon concentration; is the spatial gradient of carbon concentration after pretreatment, indicating that at the current moment In the area The rate of change of carbon concentration within It is a measure of how wind diffusion regulates the spatial distribution of carbon concentration. The greater the current wind speed, the easier it is for the gas to diffuse. However, if the current carbon concentration has already changed dramatically in space, that is, if the spatial gradient of carbon concentration is large, it means that the area is already the center of disturbance. At this time, further wind diffusion may have a smaller marginal effect. is the wind disturbance coefficient, which is used to measure the contribution of wind to diffusion and is determined by experimental data; is the rate of change of temperature with time; is the current temperature after pretreatment; is the rate of change of humidity over time; is the current humidity after pretreatment; is the cross-disturbance weight of temperature and humidity fluctuations, determined by experimental data; Indicates the degree of drastic changes in temperature and humidity conditions during the current time period; It is a surface disturbance function extracted from the multispectral remote sensing image acquired by UAV after preprocessing, such as the surface thermal anomaly index; is the image feature influencing factor, determined by experimental data; The surface disturbance function is introduced as an external disturbance factor into the carbon concentration prediction, which makes up for the limitations of pure meteorological parameters. If abnormal surface conditions such as high heat, pollution sources, dust, etc. are found in multispectral remote sensing images, then Output high value, by multiplying , which controls the “modulation ability” of multispectral remote sensing images for carbon concentration prediction.

[0051] Current concentration weighting factor involved in carbon concentration prediction formula , wind disturbance coefficient , image feature influencing factors , temperature and humidity fluctuation cross disturbance weight , and the time-varying sensitivity adjustment coefficient involved in S301 , spatial disturbance adjustment coefficient , prediction error importance coefficient The specific determination is as follows: First, data is collected continuously for at least six months in the Gobi Desert area, including data collected by ground sensors and multispectral remote sensing image data obtained by drones. The data collected by ground sensors and the multispectral remote sensing image data obtained by drones are preprocessed to construct a comprehensive spatiotemporal dataset. Second, the spatiotemporal dataset is divided into a training set and a test set. In order to minimize the root mean square error between the predicted value of carbon concentration and the actual monitored carbon concentration value, an optimization algorithm such as gradient descent (such as stochastic gradient descent) is used to iteratively train on the training set to search and determine the optimal value of each of the above coefficients. Finally, the determined coefficients are verified using the test set, and the accuracy of the prediction is evaluated until the root mean square error meets the preset requirements set according to the expert experience method.

[0052] If the predicted carbon concentration value exceeds the standard threshold, the frequency of drone monitoring will be increased.

[0053] S3. Based on the predicted carbon concentration value, calculate the comprehensive risk index and classify the Gobi desert area into risk levels.

[0054] S301. To ensure a sensitive response to carbon emission anomalies, a composite risk assessment formula integrating "temporal variation intensity + spatial gradient mutation + error feedback" is designed based on the predicted carbon concentration values ​​and the actual carbon concentration values ​​obtained by ground sensors to obtain a comprehensive risk index. Before calculating the comprehensive risk index, the data and parameters involved in the calculation process are de-dimensionalized using existing normalization methods. That is, the data and parameters involved in the calculation process of the following formula are all dimensionless. The specific formula is:

[0055] ,

[0056] in, It is at the present moment In the area Comprehensive risk index; It is the time change rate calculated from the continuous sampling data of ground sensors, which is used to characterize the fluctuation amplitude of carbon emission concentration in a certain area per unit time, so as to reflect the possible sudden emission events or atmospheric disturbance behaviors. The sampling frequency of the ground sensor network nodes in S1 (such as once every 30 seconds) and the existing data timestamp alignment mechanism ensure that High timeliness of calculation; The carbon concentration spatial gradient field distribution data collected by the UAV remote sensing platform at various altitudes is used to reflect the carbon emission concentration at different altitudes; The normalized deviation between the predicted carbon concentration and the actual monitored carbon concentration is used to reflect the accuracy of the carbon emission monitoring system's understanding of future emission trends. It has a very high policy triggering value. The larger the deviation, the higher the risk of prediction distortion, and the carbon emission monitoring system should be given priority intervention. is the carbon concentration value actually monitored by the ground sensor; It is the carbon concentration value predicted by the spatiotemporal modeling algorithm and the meteorological disturbance modulation mechanism; Is a constant used to avoid the denominator being zero, and its value range is ; is the time variation sensitivity adjustment coefficient, which is determined by experimental data; is the spatial perturbation adjustment coefficient, determined by experimental data; is the prediction error importance coefficient, which is determined by experimental data.

[0057] S302. Based on the comprehensive risk index, the Gobi desert area is divided into risk levels. When , it is defined as a normal area, indicated by green, and no treatment is required; when When the temperature is slightly fluctuating, it is designated as a mild fluctuation area and indicated in yellow. Relevant staff need to be reminded to pay more attention to the area and increase the monitoring frequency. When the risk level is 0.05, it is designated as a moderate abnormal area, indicated by orange, and an early warning is required and the path priority flight zone is updated; the path priority flight zone refers to the area with a comprehensive risk index of moderate abnormality or above, which is designated as the key target area for current monitoring based on the risk level classification results of the carbon emission detection system. The carbon emission detection system will give priority to dispatching drones to conduct cruise flights, data collection and continuous tracking in this area to ensure real-time monitoring and rapid response to key emission areas; when ... When the abnormality is detected, it is designated as a highly abnormal area, indicated in red. The carbon emission detection system will trigger the alarm mechanism and adjust the monitoring strategy, including: increasing the frequency and coverage density of drone flights, prioritizing the planning of routes in highly abnormal areas, extending flight time, adjusting the route coverage, and pushing inspection suggestions to the manual duty system to strengthen the multi-source linkage monitoring capabilities of the area. It is a regional risk threshold preset based on the distribution pattern of the historical comprehensive risk index derived from the database.

[0058] In summary, a carbon emission detection method suitable for Gobi desert areas has been completed.

[0059] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0061] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A carbon emission detection method suitable for Gobi desert areas, characterized in that: The specific steps include: S1. Deploy ground sensors and drones in the Gobi Desert. Plan the drone's flight path based on the data collected by the ground sensors, namely the actual carbon concentration values. Calculate the carbon emission matrix for the target area and, based on the values ​​of the elements in the matrix, divide the target area into high and low emission zones. Simultaneously, adjust the drone's flight path and acquire multispectral remote sensing image data. Generate aerial carbon concentration grids based on multispectral remote sensing imagery data and data collected by ground sensors; S2. Based on the aerial carbon concentration grid, using a spatiotemporal modeling algorithm and a meteorological disturbance modulation mechanism, the carbon concentration is predicted based on the current time and regional carbon concentration values, spatial gradients, the rates of change of temperature and humidity over time, and the surface disturbance function extracted from the multispectral remote sensing images acquired by the drone. The predicted carbon concentration value is obtained, and the drone monitoring frequency is adjusted based on the predicted carbon concentration value; The carbon concentration prediction formula is: ， in, In the future In the area Predicted carbon concentration values; It is at the present moment In the area Carbon concentration value after pretreatment; is the current concentration weight factor; is the current wind speed after preprocessing; is the spatial gradient of carbon concentration after pretreatment; is the wind disturbance coefficient; is the current temperature after pretreatment; is the current humidity after pretreatment; is the cross-disturbance weight of temperature and humidity fluctuations; It is the surface disturbance function extracted from the multispectral remote sensing image acquired by the UAV after preprocessing; is the image feature influencing factor; S3. Based on the predicted carbon concentration value and the data collected by ground sensors, a composite risk judgment is made by integrating the temporal change intensity, spatial gradient mutation and error feedback. At the same time, the temporal change sensitivity adjustment coefficient, the spatial disturbance adjustment coefficient and the prediction error importance coefficient are combined to obtain a comprehensive risk index and classify the Gobi desert area into risk levels.

2. The carbon emission detection method suitable for Gobi desert areas according to claim 1, characterized in that: Said S1 specifically includes: When the data collected by the ground sensor exceeds the threshold, the current area is marked as the target area, and the flight mission is triggered to send the UAV to the target area for monitoring and obtain multispectral remote sensing image data.

3. The carbon emission detection method suitable for Gobi desert areas according to claim 2, characterized in that: Said S1 specifically includes: In the process of calculating the carbon emission matrix of the target area, a two-dimensional scoring matrix is ​​constructed based on the historical carbon emission data and the data collected by ground sensors to represent the carbon emission weight of each target area.

4. The carbon emission detection method suitable for Gobi desert areas according to claim 3, characterized in that: Said S1 specifically includes: Based on the historical frequency of carbon emission violations, carbon concentration fluctuations and distance factors in the target area, the values ​​of the elements in the carbon emission matrix of the target area are calculated.

5. The carbon emission detection method suitable for Gobi desert areas according to claim 4, characterized in that: Said S1 specifically includes: According to the values ​​of the elements in the carbon emission matrix of the target area, high and low emission areas are divided; the target area where the values ​​of the elements in the carbon emission matrix are higher than the standard threshold is divided into a high emission area, and the target area where the values ​​of the elements in the carbon emission matrix are lower than the standard threshold is divided into a low emission area.

6. The carbon emission detection method suitable for Gobi desert areas according to claim 1, characterized in that: Said S3 specifically includes: Based on the comprehensive risk index and regional risk threshold, the Gobi desert area is divided into normal area, slightly fluctuating area, moderately abnormal area and highly abnormal area.

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