Carbon emission detection method suitable for gobi desert area

Abstract

The invention discloses a carbon emission detection method suitable for a gobi desert area, and relates to the technical field of carbon emission monitoring and detection. The method comprises the following steps: deploying a ground sensor and an unmanned aerial vehicle in a gobi desert area, and planning a flight path of the unmanned aerial vehicle according to data collected by the ground sensor; calculating and dividing a high emission area and a low emission area based on a target area carbon emission matrix; meanwhile, adjusting the flight path of the unmanned aerial vehicle to obtain multispectral remote sensing image data; generating an air carbon concentration grid based on multispectral remote sensing image data and data collected by a ground sensor; further, a space-time modeling algorithm and a meteorological disturbance modulation mechanism are adopted to predict the carbon concentration, and the monitoring frequency of the unmanned aerial vehicle is adjusted based on the predicted carbon concentration value; and calculating a comprehensive risk index based on the predicted carbon concentration value, and carrying out risk grade division on the gobi desert area. The problems that an existing carbon emission detection method cannot adapt to the specific environment of the gobi desert, and comprehensive and accurate carbon emission analysis is difficult to achieve are solved.

Description

Technical Field

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

[0002] Gobi desert areas are widely distributed in Asia, North Africa, America and other places, and are characterized by arid and less rainfall, scarce vegetation, and drastic surface temperature differences. With the aggravation of climate change problems, the carbon emission problem in Gobi desert areas has gradually attracted attention. Due to its extreme climate conditions in Gobi desert areas, such as large temperature differences, frequent sandstorms, and scarce vegetation, the monitoring of carbon emissions faces many challenges. In Gobi desert areas, carbon emission sources are scattered and the climate environment is complex. Traditional carbon emission detection methods are difficult to provide accurate real-time data in such environments, and traditional carbon emission monitoring methods usually rely on ground sensor networks or meteorological data, and these methods are often difficult to ensure stability and high precision in special areas such as Gobi deserts. Therefore, how to cope with the complex geographical environment and extreme climate conditions in Gobi desert areas and provide an accurate, real-time and highly adaptable carbon emission detection technology has become a technical problem to be solved 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 to solve the problems that existing carbon emission detection methods cannot adapt to the specific environment of Gobi deserts, such as drastic temperature changes and frequent extreme weather; and carbon emission monitoring often only relies on a single data collection method, such as single-point sensors or remote sensing data, lacking a multi-level and multi-dimensional comprehensive monitoring system, and it is difficult to achieve comprehensive and accurate carbon emission analysis.

[0004] A carbon emission detection method suitable for Gobi desert areas described in the present invention specifically includes the following steps: S1. Deploy ground sensors and unmanned aerial vehicles (UAVs) in Gobi desert areas, plan the flight trajectories of UAVs according to the data collected by the ground sensors; calculate the carbon emission matrix of the target area, and divide high and low emission areas based on the values of the elements in the target area carbon emission matrix. At the same time, adjust the UAV flight path to obtain multi-spectral remote sensing image data; generate an aerial carbon concentration grid based on the multi-spectral remote sensing image data and the data collected by the ground sensors; S2. Based on the aerial carbon concentration grid, use a spatio-temporal modeling algorithm and a meteorological disturbance modulation mechanism to predict the carbon concentration, and adjust the UAV monitoring frequency based on the predicted carbon concentration value; S3. Based on the predicted carbon concentration value, calculate a comprehensive risk index and divide the risk levels of Gobi desert areas.

[0005] Preferably, the S1 specifically includes: When the data collected by the ground sensors exceeds the threshold, the current area is marked as the target area, and a flight mission is triggered to send the UAV to the target area for monitoring to obtain multi-spectral remote sensing image data.

[0006] Preferably, the 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 the ground sensors to represent the carbon emission weights of each target area.

[0007] Preferably, the S1 specifically includes: Based on the historical carbon emission over-standard frequency, carbon concentration fluctuation and distance factor of the target area, calculate the values of the elements in the carbon emission matrix of the target area.

[0008] Preferably, the S1 specifically includes: According to the values of the elements in the carbon emission matrix of the target area, divide the high and low emission areas; divide the target areas with the values of the elements in the carbon emission matrix higher than the standard threshold into high emission areas, and divide the target areas with the values of the elements in the carbon emission matrix lower than the standard threshold into low emission areas.

[0009] Preferably, the S2 specifically includes: Based on the carbon concentration value, spatial gradient, change rate of temperature and humidity over time at the current moment and in the area, and the surface disturbance function extracted from the multi-spectral remote sensing image obtained by the UAV, calculate the predicted carbon concentration value.

[0010] Preferably, the S3 specifically includes: Based on the predicted carbon concentration value and the data collected by the ground sensors, conduct a composite risk judgment by integrating the time change intensity, spatial gradient mutation and error feedback to obtain a comprehensive risk index.

[0011] Preferably, the S3 specifically includes: Based on the comprehensive risk index and the regional risk threshold, divide the gobi desert area into normal area, mild fluctuation area, moderate anomaly area and high anomaly area.

[0012] The beneficial effects of the technical solution of the present invention are: 1. It improves the comprehensiveness and adaptability of carbon emission monitoring in the gobi desert area. By combining the ground sensor network and UAV aerial remote sensing, an integrated air-ground multi-source monitoring system is constructed, which makes up for the defects of traditional carbon emission detection such as difficult site layout, insufficient coverage and single data in desert and sandy environments, and greatly enhances the adaptability of the carbon emission detection system to the complex environment of the gobi desert.

[0013] 2. Dynamically high-precision modeling of carbon emission distribution is achieved. By constructing a carbon emission matrix, multi-dimensional assessment of the carbon emission intensity in the target area is realized, and high- and low-emission areas are accurately divided. At the same time, the flight path of the drone is adjusted in real time according to the carbon emission matrix to ensure the maximization of monitoring efficiency and improve data coverage and timeliness.

[0014] 3. It has strong carbon concentration prediction and spatio-temporal evolution analysis capabilities. By using spatio-temporal modeling algorithms and meteorological perturbation modulation mechanisms, the changing trend of carbon concentration is predicted in advance, and pre-response and resource scheduling for abnormal areas are realized, providing forward-looking support for energy conservation and emission reduction.

[0015] 4. Through the dynamic risk level division based on the comprehensive risk index, intelligent response from mild fluctuations to highly abnormal is realized, providing a decision-making basis for regional governance, UAV scheduling optimization, and even manual inspection. Description of the Drawings

[0016] Figure 1 It is a flowchart of a carbon emission detection method suitable for Gobi desert areas according to the present invention. Detailed Embodiments

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0019] The following specifically describes the specific solution of a carbon emission detection method suitable for Gobi desert areas provided by the present invention with reference to the accompanying drawings.

[0020] Refer to the appendix Figure 1 , which shows a flowchart of a carbon emission detection method suitable for Gobi desert areas provided by an embodiment of the present invention. This method includes the following parts: S1. Deploy ground sensors and drones in the Gobi desert area. According to the data collected by the ground sensors, plan the flight trajectory of the drones; calculate the carbon emission matrix of the target area, and based on the values of the elements in the carbon emission matrix of the target area, divide high- and low-emission areas, and at the same time adjust the flight path of the drones to obtain multi-spectral remote sensing image data; generate an aerial carbon concentration grid based on the multi-spectral remote sensing image data and the data collected by the ground sensors.

[0021] S101. Deploy a ground sensor network and install ground sensors in different areas of the gobi desert region, specifically including: in the desert storm belt, set up CO2 / CH4 gas compound sensors using the annual average wind speed data of the wind corridor; in the ecological restoration area, covering degraded forest and grass, artificial oases, and monitor the carbon sink effect of the artificial restoration area; in the artificial construction area, such as desert highways, industrial experimental areas, deploy multi-point array probes to monitor human emission behaviors. Technical configuration details for installing ground sensors: Each sensor is equipped with an NDIR type CO2 measurement module (high temperature stable), a methane sensing chip, a PM sensor, and has a wind speed and wind direction module; the shell is coated with a nano anti-sand and anti-corrosion coating, with a self-cleaning glass window and an automatic dust scraping mechanism (small motor + timed vibration). Data is sampled every 30s and transmitted to the edge node through the LoRa + gateway aggregation method, and then uploaded to the drone through the public network / satellite.

[0022] Based on the data collected by the ground sensors, that is, the actual carbon concentration values obtained, plan the flight route for the drone. When the monitored data exceeds the threshold, that is, when the CO2 concentration value > 480 ppm, mark the current area as the target area. At this time, trigger the flight mission and send the drone to the target area for further monitoring.

[0023] S102. Use drone remote sensing technology for detection to obtain multi-spectral remote sensing image data. The drone is equipped with the following modules: a multi-spectral imaging system (infrared / ultraviolet / visible light three channels) for analyzing surface vegetation / thermal field changes; through a micro CO2, CH4 gas probe, to achieve aerial carbon gas distribution mapping; a barometer + temperature and humidity meter for constructing an atmospheric structure model at the flight level.

[0024] Flight strategy and path control: The initial route planning uses a Z-shaped or spiral pattern to cover the target area; before flying, the carbon emission detection system in the gobi desert region will load the historical carbon emission map and the wind speed data of the historical high wind speed area. The high wind speed area is a local area planned in the gobi desert region. Calculate the carbon emission matrix of the target area. Specifically, according to the two-dimensional scoring matrix constructed from the historical carbon emission data and the data collected by the 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.

[0025] Before calculating each element in the carbon emission matrix using the weighted formula, 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 those skilled in the art, such as maximum-minimum normalization, to convert it into a dimensionless form to achieve comparability and fusion, and ensure the mathematical rationality and engineering applicability of the weighted formula.

[0026] Each element in the carbon emission matrix is determined by the following weighted formula: , where is the element in the th row and the th column of the carbon emission matrix, is the coordinate point of the target area; represents the historical carbon emission over - standard frequency of the target area after normalization. The historical carbon emission over - standard frequency of the target area comes from historical carbon emission data, including the long - term sampling data of ground sensors and the statistical results of the number of times exceeding the carbon concentration threshold (such as CO2>480 ppm) in the previous flight monitoring records of drones. Combining with the historical carbon emission map, spatial aggregation and frequency superposition analysis are carried out to form the over - standard frequency distribution of the corresponding area; is the variance of the carbon concentration change of the target area after normalization, representing the carbon concentration fluctuation; is the inverse ratio of the distance between the target area and the position of the nearest ground sensor, that is, the distance factor; , , are weight coefficients set according to the expert experience method, used to control the relative influence of the historical carbon emission over - standard frequency, carbon concentration fluctuation and distance factor, and are preferably set to , , ; In different implementation scenarios, it can be dynamically adjusted according to expert experience within the range of .

[0027] Set the normalized score value corresponding to the CO2 concentration threshold, such as 480 ppm, as the standard threshold.

[0028] According to the value of the element in the carbon emission matrix, high - and low - emission areas are divided. The target area with the value of the element in the carbon emission matrix higher than the standard threshold is divided into the high - emission area, and the target area with the value of the element in the carbon emission matrix lower than the standard threshold is divided into the low - emission area.

[0029] The carbon emission detection system in the whole gobi desert area will dynamically adjust the flight path of the drone: automatically increase the flight altitude in the high - wind speed area; The high - emission area is taken as the key monitoring object, and the carbon emission detection system will automatically increase the monitoring frequency and flight coverage priority of the high - emission area; Reduce the frequency in the low - emission area; Each flight lasts 30 - 45 minutes, and the data obtained is uploaded in real - time along the way.

[0030] Combined with drone remote sensing and ground sensors, it overcomes the impact of the complex geographical environment of the Gobi Desert on data collection and realizes carbon emission monitoring. The multi-spectral remote sensing image data obtained by the drone and the data obtained by the ground sensors are processed in real time through an on-board AI chip, compressed into block coordinates, and an "aerial carbon concentration grid" is generated using existing technologies, and the "aerial carbon concentration grid" is uploaded to S2.

[0031] S2. Based on the aerial carbon concentration grid, a spatio-temporal modeling algorithm and a meteorological disturbance modulation mechanism are used to predict the carbon concentration, and the monitoring frequency of the drone is adjusted based on the predicted carbon concentration value.

[0032] On the basis of the constructed "aerial carbon concentration grid", the carbon emission detection system in the Gobi Desert area further adopts a spatio-temporal modeling algorithm and a meteorological disturbance modulation mechanism to predict the carbon concentration, and outputs the carbon concentration evolution trend and distribution map of a certain area in the future.

[0033] Before predicting the carbon concentration, for 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., preprocessing methods well-known to those skilled in the art, such as min-max or Z-score standardization, are used for dimensionless processing to ensure the dimensional consistency in the prediction process.

[0034] The carbon concentration prediction formula is: , where, is the predicted carbon concentration value at the future moment in the area ; is the preprocessed carbon concentration value at the current moment in the area ; is the current concentration weight factor, determined by experimental data; is the preprocessed current wind speed (after combining direction), and the current wind speed is measured in real time by the wind speed and wind direction modules installed on the ground sensors; is the square of the preprocessed current wind speed, representing that the greater the wind force, the stronger the driving effect on the diffusion of carbon gas; represents the degree of non-uniformity of the carbon concentration in the current area in space; is the spatial gradient of the preprocessed carbon concentration, indicating the carbon concentration change rate in the area at the current moment ; It is an adjustment amount of the influence of wind diffusion on the spatial carbon concentration distribution. The greater the current wind speed, the easier it is for gas to be diffused. However, if the current carbon concentration has changed significantly in space, that is, the spatial gradient of the carbon concentration is large, it indicates that this area is already a perturbation center. At this time, the marginal effect of further wind diffusion may become smaller; It is the wind disturbance coefficient, which is used to measure the contribution weight of wind to diffusion and is determined through experimental data; It is the rate of change of temperature over time; It is the current temperature after preprocessing; It is the rate of change of humidity over time; It is the current humidity after preprocessing; It is the cross-perturbation weight of temperature and humidity fluctuations, which is determined through experimental data; It represents the degree of drastic change in temperature and humidity conditions within the current time period; It is the surface disturbance function extracted from the multi-spectral remote sensing image obtained by the drone after preprocessing, such as the surface thermal anomaly index; It is the image feature influence factor, which is determined through experimental data; Introducing the surface disturbance function as an external disturbance factor into carbon concentration prediction makes up for the limitations of pure meteorological parameters. If abnormal surfaces are found in the multi-spectral remote sensing image, such as high heat, pollution sources, dust, etc., then a high value is output, and by multiplying by it controls the "modulation ability" of the multi-spectral remote sensing image on carbon concentration prediction.

[0035] The current concentration weight factor involved in the carbon concentration prediction formula , the wind disturbance coefficient , the image feature influence factor , the cross-perturbation weight of temperature and humidity fluctuations , and the time change sensitivity adjustment coefficient , the spatial disturbance adjustment coefficient , the prediction error importance coefficient are determined specifically as follows: First, continuously collect data for at least six months in the gobi desert area, including data collected by ground sensors and multi-spectral remote sensing image data obtained by drones. Preprocess the data collected by ground sensors and multi-spectral remote sensing image data obtained by drones to construct a comprehensive spatio-temporal dataset. Second, divide the spatio-temporal dataset 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 actually monitored carbon concentration value, use optimization algorithms such as gradient descent (such as stochastic gradient descent) to perform iterative training on the training set to search for and determine the optimal values of each of the above coefficients. Finally, use the test set to verify the determined coefficients and evaluate the prediction accuracy until the root mean square error meets the preset requirements set according to the expert experience method.

[0036] If the predicted carbon concentration value exceeds the standard threshold, the monitoring frequency of the drone will be increased.

[0037] S3. Based on the predicted carbon concentration value, calculate the comprehensive risk index and classify the risk levels of the gobi desert area.

[0038] S301. To ensure a sensitive response to abnormal carbon emissions, based on the predicted carbon concentration value and the carbon concentration value actually obtained by the ground sensors, design a composite risk judgment formula that combines "time change intensity + spatial gradient mutation + error feedback" to obtain the comprehensive risk index. Before calculating the comprehensive risk index, perform dimensionless processing on the data and parameters involved in the operation process through the existing normalization method, that is, the data and parameters involved in the following formula operation process are in a dimensionless state. The specific formula is: , Among them, is the comprehensive risk index at the current moment in the area ; is the time change rate calculated from the continuously sampled data of the ground sensors, used to describe the fluctuation amplitude of the carbon emission concentration in a certain area per unit time, so as to reflect 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 the high timeliness of the calculation; comes from the carbon concentration spatial gradient field distribution data collected by the drone remote sensing platform at various heights, and is used to reflect the carbon emission concentration at different heights; represents the normalized deviation between the predicted carbon concentration value and the actually monitored carbon concentration value, and is used to reflect the accuracy of the carbon emission detection system's understanding of the future emission trend. It has a very high strategy triggering value - the larger the deviation, the higher the risk of prediction distortion, and the carbon emission detection system should intervene preferentially; is the actually monitored carbon concentration value of the ground sensor; is the carbon concentration value predicted by the spatio-temporal 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 change sensitivity adjustment coefficient, determined by experimental data; is the spatial disturbance adjustment coefficient, determined by experimental data; is the prediction error importance coefficient, determined by experimental data.

[0039] S302. Based on the comprehensive risk index, classify the risk levels of the gobi desert area. When When it is in this range, it is designated as the normal area and represented in green, and no processing is required; when it is in this range, it is designated as the mild fluctuation area and represented in yellow, and relevant staff need to be reminded to pay more attention to this area and increase the monitoring frequency; when it is in this range, it is designated as the moderate anomaly area and represented in orange, and a warning needs to be issued and the path priority flight area needs to be updated; the path priority flight area refers to the area where the comprehensive risk index is at moderate anomaly or above according to the risk level classification result of the carbon emission detection system, which is designated as the key target area for current monitoring. The carbon emission detection system will give priority to dispatching drones to cruise, collect data and continuously track this area to ensure real-time monitoring and rapid response to key emission areas; when it is in this range, it is designated as the high anomaly area and represented in red. The carbon emission detection system will trigger the alarm mechanism and adjust the monitoring strategy, including: increasing the flight frequency and coverage density of drones, preferentially planning the path of the high anomaly area, extending the flight time, adjusting the route coverage range, and at the same time pushing inspection suggestions to the manual duty system to enhance the multi-source joint monitoring ability of this area. is the area risk threshold preset according to the distribution law of the historical comprehensive risk index from the database.

[0040] In summary, a carbon emission detection method suitable for the gobi desert area is completed.

[0041] The sequence of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0042] Each embodiment in this specification is described in a progressive manner. For the same or similar parts between each embodiment, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0043] The above embodiments are only used to illustrate the technical solutions of the present invention and do not limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A carbon emission detection method suitable for the Gobi desert area, characterized in that, Specifically, it includes the following steps: S1. Deploy ground sensors and drones in the gobi desert area. According to the data collected by the ground sensors, plan the flight trajectory of the drones; calculate the carbon emission matrix of the target area, and based on the values of the elements in the target area carbon emission matrix, divide the high and low emission areas, and at the same time adjust the drone flight path to obtain multi-spectral remote sensing image data; Generate an aerial carbon concentration grid based on the multi-spectral remote sensing image data and the data collected by the ground sensors; S2. Based on the aerial carbon concentration grid, use a spatio-temporal modeling algorithm and a meteorological disturbance modulation mechanism to predict the carbon concentration, and adjust the drone monitoring frequency based on the predicted carbon concentration value; S3. Based on the predicted carbon concentration value, calculate the comprehensive risk index and divide the risk levels of the gobi desert area.

2. The carbon emission detection method suitable for the gobi desert area according to claim 1, characterized in that The S1 specifically includes: When the data collected by the ground sensors exceeds the threshold, mark the current area as the target area, trigger the flight mission, send the drones to the target area for monitoring, and obtain multi-spectral remote sensing image data.

3. The carbon emission detection method suitable for the Gobi desert region according to claim 2, characterized in that The S1 specifically includes: During the process of calculating the target area carbon emission matrix, construct a two-dimensional scoring matrix according to the historical carbon emission data and the data collected by the ground sensors to represent the carbon emission weights of each target area.

4. The carbon emission detection method suitable for the gobi desert area according to claim 3, characterized in that The S1 specifically includes: Based on the historical carbon emission over-standard frequency, carbon concentration fluctuation and distance factor of the target area, calculate the values of the elements in the target area carbon emission matrix.

5. The carbon emission detection method suitable for the Gobi desert area according to claim 4, characterized in that, The S1 specifically includes: According to the values of the elements in the target area carbon emission matrix, conduct high and low emission area division; divide the target areas with the values of the elements in the carbon emission matrix higher than the standard threshold into high emission areas, and divide the target areas with the values of the elements in the carbon emission matrix lower than the standard threshold into low emission areas.

6. The carbon emission detection method suitable for the gobi desert area according to claim 1, characterized in that, The S2 specifically includes: Based on the carbon concentration value, spatial gradient, change rate of temperature and humidity over time at the current moment and in the area, and the surface disturbance function extracted from the multi-spectral remote sensing image obtained by the drones, calculate the predicted carbon concentration value.

7. The carbon emission detection method suitable for the gobi desert area according to claim 6, characterized in that, The S3 specifically includes: Based on the predicted carbon concentration value and the data collected by the ground sensors, conduct a composite risk judgment integrating time-varying intensity, spatial gradient mutation and error feedback to obtain the comprehensive risk index.

8. The carbon emission detection method suitable for the Gobi desert area according to claim 7, wherein The S3 specifically includes: Based on the comprehensive risk index and the regional risk threshold, divide the gobi desert area into normal area, mild fluctuation area, moderate anomaly area and high anomaly area.

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