Reservoir carbon emission data analysis method based on big data technology

Abstract

The invention discloses a reservoir carbon emission data analysis method based on a big data technology, and relates to the technical field of reservoir carbon emission data analysis, and the method comprises the following steps: obtaining reservoir carbon emission monitoring data through a collection device, preprocessing the reservoir carbon emission monitoring data, and calculating the vegetation coverage rate of a reservoir region; the method comprises the following steps: respectively acquiring influence indexes of each reservoir carbon emission monitoring data on carbon emission, constructing a reservoir carbon emission monitoring model by adopting a random forest algorithm, evaluating a reservoir carbon emission level, sending out a corresponding signal and transmitting the signal to a client, according to the method, the unmanned aerial vehicle and multispectral sensor collaborative acquisition technology, the random forest algorithm modeling technology, the multi-source data fusion analysis technology and the modern information technology are closely combined, the influence index of each reservoir carbon emission monitoring data on the carbon emission is accurately obtained, and the real-time and comprehensive monitoring of the reservoir carbon emission is achieved; and the intelligent degree in the reservoir carbon emission process is obviously enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir carbon emission data analysis, and in particular to a reservoir carbon emission data analysis method based on big data technology. Background Art

[0002] In the field of reservoir carbon emission research, the existing analysis methods have many limitations. On the one hand, in traditional research, the monitoring of the carbon exchange process between reservoir water and the atmosphere is not comprehensive enough, which makes it impossible to accurately quantify the carbon emission flux. The current mainstream carbon emission estimation models are mostly based on simple linear relationships and cannot fully consider the comprehensive impact of complex ecological and environmental factors of reservoirs on carbon emissions. With the rapid development of information technology, big data technology has gradually been widely used in many fields. In terms of reservoir carbon emission analysis, the application of big data technology is in the exploratory stage and has not formed a mature and complete analysis system based on big data technology. Therefore, using big data technology to integrate multi-source data, explore the key factors affecting reservoir carbon emissions, and build a more accurate analysis model has become an important research direction at present;

[0003] Although existing technologies have made great progress in the analysis of reservoir carbon emissions data, there are still some problems that need to be optimized. The existing data analysis technology for reservoir carbon emissions is single and cannot combine the multi-dimensional data of the reservoir to analyze the reservoir carbon emissions, resulting in inaccurate reservoir carbon emissions analysis results. Summary of the invention

[0004] The purpose of the present invention is to provide a reservoir carbon emission data analysis method based on big data technology to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a reservoir carbon emission data analysis method based on big data technology, comprising the following steps:

[0006] Step 1: Obtain reservoir carbon emission monitoring data through collection equipment, providing data support for subsequent monitoring of reservoir carbon emissions;

[0007] Step 2: Pre-process reservoir carbon emission monitoring data and calculate the vegetation coverage rate in the reservoir area, which provides a prerequisite for establishing the index foundation of the impact of vegetation coverage rate on carbon emissions;

[0008] Step 3: Obtain the impact index of each reservoir carbon emission monitoring data on carbon emissions through reservoir carbon emission monitoring data, which provides technical support for solving the problem that the existing data analysis technology for reservoir carbon emissions is single and cannot combine reservoir multi-dimensional data to analyze reservoir carbon emissions, resulting in inaccurate reservoir carbon emission analysis results;

[0009] Step 4: Use the random forest algorithm to build a reservoir carbon emission monitoring model;

[0010] Step 5: Analyze the reservoir carbon emission monitoring data, evaluate the reservoir carbon emission level, and send a corresponding signal to the client.

[0011] A further improvement of the technical solution of the present invention is that in step 1, the process of obtaining reservoir carbon emission monitoring data through the collection device includes:

[0012] The collection equipment includes drones, multispectral sensors, total stations, ArcGIS software, thermocouple thermometers, gas samplers, infrared carbon dioxide sensors, LI-7700 methane analyzers, Kjeldahl flasks, spectrophotometers, high-temperature combustion tubes and non-dispersive infrared absorbers; the reservoir carbon emission monitoring data include the area of ​​the reservoir carbon emission research area, the area of ​​the vegetation coverage area in the reservoir drawdown zone, the soil-air interface area in the reservoir drawdown zone, the reservoir temperature, the reservoir carbon emissions and water quality data, wherein the water quality data include the total nitrogen, total phosphorus and organic carbon concentrations in the reservoir.

[0013] A further improvement of the technical solution of the present invention is that in step 1, the process of obtaining the area of ​​the reservoir carbon emission research area, the area of ​​the vegetation coverage area of ​​the reservoir drawdown zone, the soil-air interface area of ​​the reservoir drawdown zone and the reservoir temperature includes:

[0014] Determine the reservoir carbon emission research area, plan a flight route for the drone according to the shape and area of ​​the reservoir carbon emission research area, and carry out a multispectral sensor on the drone. According to the planned flight route, collect the reflectance of the infrared band and near-infrared band around the reservoir. Calculate the normalized vegetation index through the formula NDVI = (NIR-Red) (NIR+Red), where NIR is the near-infrared band, Red is the infrared band reflectance, and NDVI is the normalized vegetation index. A positive value of the normalized vegetation index indicates that the corresponding area is covered with vegetation. Extract the vegetation coverage area, use a total station to collect the three-dimensional coordinate data of the vegetation coverage area and the soil-air interface of the reservoir carbon emission research area and the reservoir drawdown zone, and import the three-dimensional coordinate data measured by the total station into ArcGIS software to obtain the area of ​​the reservoir carbon emission research area, the area of ​​the vegetation coverage area of ​​the reservoir drawdown zone, and the area of ​​the soil-air interface of the reservoir drawdown zone;

[0015] According to the reservoir environment, a thermocouple thermometer is selected and calibrated. The thermocouple thermometer is fixedly installed in the center of the reservoir, and the reservoir temperature is collected based on the Seebeck effect.

[0016] A further improvement of the technical solution of the present invention is that in step 1, the process of obtaining reservoir carbon emissions and water quality data includes:

[0017] Use a gas sampler to collect gas samples at different locations on the surface of the reservoir, use an infrared carbon dioxide sensor to collect carbon dioxide concentrations at 8:00 and 17:00 every day, and use the LI-7700 methane analyzer to collect methane concentrations at 8:00 and 17:00 every day in real time. According to the carbon dioxide density and methane density, calculate the carbon dioxide mass and methane mass at 8:00 and 17:00 every day. Use a total station to collect the reservoir volume. The process of calculating and obtaining carbon emissions is as follows:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] Among them, C 1 and C 2 The carbon dioxide concentrations at 8:00 and 17:00 every day, C 3 and C 4 The methane concentrations at 8:00 and 17:00 every day are respectively, is the mass change of carbon dioxide, is the mass change of methane, and are the molar masses of carbon dioxide and methane respectively, V is the volume of the reservoir, and are the amounts of carbon dioxide and methane, respectively, and n is the carbon emission of the reservoir;

[0024] The Kjeldahl nitrogen determination method is adopted. A reservoir water sample is taken and placed in a Kjeldahl flask. The volume of the reservoir water sample is recorded. Concentrated sulfuric acid and a catalyst are added. Under heating conditions, all nitrogen-containing compounds in the reservoir water sample are converted into ammonium salts. After the solution is cooled, a sodium hydroxide solution is added. The ammonium ions are converted into ammonia gas. The Kjeldahl flask is connected to a distillation device, heated and distilled. A boric acid solution is used to absorb the distilled ammonia gas. An indicator is added to the boric acid solution that has absorbed ammonia gas. The solution is titrated with a standard hydrochloric acid solution. The color of the solution changes from green to dark red, and the titration end point is reached. The total nitrogen content of the reservoir is calculated according to the dosage and concentration of the standard hydrochloric acid solution and the volume of the reservoir water sample.

[0025] Mix potassium persulfate with the water quality measurement sample to convert various forms of phosphorus in the water quality measurement sample into orthophosphate. Under acidic conditions, add ammonium molybdate and potassium antimony tartrate to the water quality measurement sample to generate blue phosphomolybdenum blue. Use a spectrophotometer to measure the absorbance, and calculate the total phosphorus content of the reservoir based on the standard curve.

[0026] Collect reservoir water samples and inject them into the TOC instrument to measure the organic carbon content of the reservoir.

[0027] A further improvement of the technical solution of the present invention is that in step 2, the process of preprocessing the reservoir carbon emission monitoring data and calculating the vegetation coverage rate of the reservoir area includes:

[0028] Based on the Laida criterion, outliers in reservoir carbon emission monitoring data are removed and reservoir carbon emission monitoring data are standardized;

[0029] Using the area of ​​the reservoir carbon emission study area, the vegetation coverage area of ​​the reservoir drawdown zone, and the soil-air interface area of ​​the reservoir drawdown zone, the process of calculating the vegetation coverage rate of the reservoir area is as follows:

[0030]

[0031] Among them, A is the vegetation coverage rate in the reservoir area, A t is the vegetation coverage area of ​​the reservoir drawdown zone, A 0 is the area of ​​reservoir carbon emission research.

[0032] A further improvement of the technical solution of the present invention is that in step 3, the process of obtaining the impact index of vegetation coverage on carbon emissions includes:

[0033] The carbon emissions per unit area of ​​the vegetation coverage area in the reservoir drawdown zone and the carbon emissions per unit area of ​​the soil-air interface in the reservoir drawdown zone are extracted. The process of calculating the carbon emissions per unit area of ​​the vegetation coverage area in the reservoir drawdown zone and the carbon emissions per unit area of ​​the soil-air interface in the reservoir drawdown zone is as follows:

[0034] T t =E t ×A t

[0035] T u =E u ×Au

[0036] Among them, E t and E u are the carbon emissions per unit area of ​​the vegetation coverage area in the reservoir drawdown zone and the carbon emissions per unit area of ​​the soil-air interface in the reservoir drawdown zone, A t and A u are the vegetation coverage area and the soil-air interface area of ​​the reservoir drawdown zone, respectively. t and T u They are the carbon emissions from the vegetation-covered area in the reservoir drawdown zone and the carbon emissions from the soil-air interface in the reservoir drawdown zone;

[0037] The process of calculating the impact index of vegetation coverage on carbon emissions by using the carbon emissions of the vegetation coverage area in the reservoir drawdown zone and the carbon emissions of the soil-air interface in the reservoir drawdown zone includes:

[0038]

[0039] Among them, I is the impact index of vegetation coverage on carbon emissions, T t and T u are the carbon emissions from the vegetation coverage area in the reservoir drawdown zone and the carbon emissions from the soil-air interface in the reservoir drawdown zone, respectively. A is the vegetation coverage rate in the reservoir area.

[0040] A further improvement of the technical solution of the present invention is that in step 3, the process of obtaining the temperature influence index on carbon emissions through the reservoir temperature and carbon emissions includes:

[0041] Using the linear regression model, taking the reservoir temperature as the independent variable and carbon emissions as the dependent variable, the linear regression expression y=a+bx is set, and the linear regression model parameters are calculated by the least squares method to obtain the impact index of reservoir temperature on carbon emissions. The specific calculation process is as follows:

[0042]

[0043] Among them, b is the impact index of temperature on carbon emissions, x i is the reservoir temperature, y i is the carbon emission, and n is the number of reservoir temperature collection and the number of carbon emission collection.

[0044] A further improvement of the technical solution of the present invention is that in step 3, the process of obtaining the impact index of water quality on carbon emissions by using water quality data includes:

[0045] According to the reservoir carbon emission index, weights are assigned to the total nitrogen, total phosphorus and organic carbon concentrations in the reservoir, and the process of calculating the impact index of water quality data on carbon emissions is as follows:

[0046] Y=w N ×y N +w P ×y P +w C ×y C

[0047] Among them, Y is the impact index of water quality on carbon emissions, w N is the total nitrogen weight in the reservoir, w P is the total phosphorus weight in the reservoir, w C is the organic carbon concentration in the reservoir, y N is the actual total nitrogen content in the reservoir, y P is the actual total phosphorus content in the reservoir, yC is the actual reservoir organic carbon concentration.

[0048] A further improvement of the technical solution of the present invention is that in step 4, the process of using the random forest algorithm to construct a reservoir carbon emission monitoring model includes:

[0049] The vegetation coverage rate of the reservoir area and its impact index on carbon emissions, the reservoir temperature and its impact index on carbon emissions, and the water quality data and its impact index on carbon emissions are used as data sets and divided into training and test sets in a ratio of 7:3;

[0050] Initialize the random forest model parameters, take the reservoir carbon emission monitoring data as input features, and take the impact index of each reservoir carbon emission monitoring data on carbon emissions as output features;

[0051] The training set data is used to train the random forest model. Through iterative training, the nonlinear relationship between the vegetation coverage rate in the reservoir area and the index of the impact of the vegetation coverage rate on carbon emissions, the nonlinear relationship between the reservoir temperature and the index of the impact of the temperature on carbon emissions, and the nonlinear relationship between the water quality data and the index of the impact of water quality on carbon emissions are learned to obtain the trained random forest model.

[0052] The test set data is input into the trained random forest model, the error between the output value of the random forest model and the actual value is evaluated, the parameters of the random forest model are adjusted according to the evaluation results, the performance of the random forest model is optimized, and the reservoir carbon emission monitoring model is obtained.

[0053] A further improvement of the technical solution of the present invention is that in step 5, the process of evaluating the carbon emission level of the reservoir and transmitting the corresponding signal to the client includes:

[0054] Combined with the reservoir carbon emission monitoring model, the reservoir carbon emission monitoring data is analyzed. When the vegetation coverage rate has a low impact on carbon emissions, the index is lower than 0.3. When the vegetation coverage rate has a low impact on carbon emissions, the index is between 0.3 and 0.6. It indicates that the vegetation coverage rate has a medium impact on carbon emissions, and a reservoir vegetation coverage warning signal is issued to the client. When the vegetation coverage rate has a high carbon emission index, an emergency signal of reservoir vegetation coverage is issued to the client.

[0055] When the temperature impact index on carbon emissions is lower than 0.3, it indicates that the temperature has a low impact on carbon emissions; when the temperature impact index on carbon emissions is between 0.3 and 0.7, it indicates that the temperature has a medium impact on carbon emissions, and a reservoir temperature warning signal is issued to the client; when the temperature impact index on carbon emissions is higher than 0.7, a reservoir temperature emergency signal is issued to the client;

[0056] When the water quality impact index on carbon emissions is lower than 0.2, it indicates that the water quality has a low impact on carbon emissions; when the water quality impact index on carbon emissions is between 0.2 and 0.6, it indicates that the water quality has a medium impact on carbon emissions, and a reservoir water quality warning signal is issued to the client; when the water quality impact index on carbon emissions is higher than 0.7, a reservoir water quality emergency signal is issued to the client.

[0057] The beneficial effects of the present invention are as follows: a reservoir carbon emission data analysis method based on big data technology in the present invention, compared with the traditional reservoir carbon emission data analysis method using big data technology, the drone and multi-spectral sensor collaborative acquisition technology, random forest algorithm modeling technology, and multi-source data fusion analysis technology in the method of the present invention are closely combined with modern information technology, and the impact index of various reservoir carbon emission monitoring data on carbon emissions is accurately obtained, thereby achieving real-time and comprehensive monitoring of reservoir carbon emissions. By constructing a reservoir carbon emission monitoring model and learning the nonlinear relationship between various reservoir carbon emission monitoring data and the carbon emission impact index, the problem that traditional methods cannot fully consider the comprehensive impact of complex factors of reservoirs on carbon emissions, resulting in inaccurate analysis results, is solved, ensuring that the method in the present invention can refine the dynamic monitoring standard of reservoir carbon emissions used for big data technology within a more precise range, so that the monitored data becomes a more accurate indicator under the same conditions. The development and application of this method significantly enhances the degree of intelligence in the reservoir carbon emission process. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0059] Figure 1 This is a flow chart of a reservoir carbon emission data analysis method based on big data technology in the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 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 creative work are within the scope of protection of the present invention.

[0061] like Figure 1 As shown, the present invention provides a reservoir carbon emission data analysis method based on big data technology, comprising the following steps:

[0062] Step 1: Obtain reservoir carbon emission monitoring data through collection equipment, providing data support for subsequent monitoring of reservoir carbon emissions;

[0063] Step 2: Pre-process reservoir carbon emission monitoring data and calculate the vegetation coverage rate in the reservoir area, which provides a prerequisite for establishing the index foundation of the impact of vegetation coverage rate on carbon emissions;

[0064] Step 3: Obtain the impact index of each reservoir carbon emission monitoring data on carbon emissions through reservoir carbon emission monitoring data, which provides technical support for solving the problem that the existing data analysis technology for reservoir carbon emissions is single and cannot combine reservoir multi-dimensional data to analyze reservoir carbon emissions, resulting in inaccurate reservoir carbon emission analysis results;

[0065] Step 4: Use the random forest algorithm to build a reservoir carbon emission monitoring model;

[0066] Step 5: Analyze the reservoir carbon emission monitoring data, evaluate the reservoir carbon emission level, and send a corresponding signal to the client.

[0067] Preferably, in step 1, the process of obtaining reservoir carbon emission monitoring data through collection equipment includes:

[0068] Among them, the collection equipment includes drones, multispectral sensors, total stations, ArcGIS software, thermocouple thermometers, gas samplers, infrared carbon dioxide sensors, LI-7700 methane analyzers, Kjeldahl flasks, spectrophotometers, high-temperature combustion tubes and non-dispersive infrared absorbers. The reservoir carbon emission monitoring data include the area of ​​the reservoir carbon emission research area, the vegetation coverage area of ​​the reservoir drawdown zone, the soil-air interface area of ​​the reservoir drawdown zone, the reservoir temperature, the reservoir carbon emissions and water quality data. The water quality data include the total nitrogen, total phosphorus and organic carbon concentrations in the reservoir.

[0069] Preferably, in step 1, the process of obtaining the area of ​​the reservoir carbon emission research region, the area of ​​the vegetation coverage area in the reservoir drawdown zone, the soil-air interface area in the reservoir drawdown zone, and the reservoir temperature includes:

[0070] Determine the reservoir carbon emission research area, plan a flight route for the drone according to the shape and area of ​​the reservoir carbon emission research area, and carry out a multispectral sensor on the drone. According to the planned flight route, collect the reflectance of the infrared band and near-infrared band around the reservoir. Calculate the normalized vegetation index through the formula NDVI = (NIR-Red) (NIR+Red), where NIR is the near-infrared band, Red is the infrared band reflectance, and NDVI is the normalized vegetation index. A positive value of the normalized vegetation index indicates that the corresponding area is covered with vegetation. Extract the vegetation coverage area, use a total station to collect the three-dimensional coordinate data of the vegetation coverage area and the soil-air interface of the reservoir carbon emission research area and the reservoir drawdown zone, and import the three-dimensional coordinate data measured by the total station into ArcGIS software to obtain the area of ​​the reservoir carbon emission research area, the area of ​​the vegetation coverage area of ​​the reservoir drawdown zone, and the area of ​​the soil-air interface of the reservoir drawdown zone;

[0071] According to the reservoir environment, a thermocouple thermometer is selected and calibrated. The thermocouple thermometer is fixedly installed in the center of the reservoir, and the reservoir temperature is collected based on the Seebeck effect.

[0072] Preferably, in step 1, the process of obtaining reservoir carbon emissions and water quality data includes:

[0073] Use a gas sampler to collect gas samples at different locations on the surface of the reservoir, use an infrared carbon dioxide sensor to collect carbon dioxide concentrations at 8:00 and 17:00 every day, and use the LI-7700 methane analyzer to collect methane concentrations at 8:00 and 17:00 every day in real time. According to the carbon dioxide density and methane density, calculate the carbon dioxide mass and methane mass at 8:00 and 17:00 every day. Use a total station to collect the reservoir volume. The process of calculating and obtaining carbon emissions is as follows:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] Among them, C 1 and C 2 The carbon dioxide concentrations at 8:00 and 17:00 every day, C 3 and C t The methane concentrations at 8:00 and 17:00 every day are respectively, is the mass change of carbon dioxide, is the mass change of methane, and are the molar masses of carbon dioxide and methane respectively, V is the volume of the reservoir, and are the amounts of carbon dioxide and methane, respectively, and n is the carbon emission of the reservoir;

[0080] The Kjeldahl nitrogen determination method is adopted. A reservoir water sample is taken and placed in a Kjeldahl flask. The volume of the reservoir water sample is recorded. Concentrated sulfuric acid and a catalyst are added. Under heating conditions, all nitrogen-containing compounds in the reservoir water sample are converted into ammonium salts. After the solution is cooled, a sodium hydroxide solution is added. The ammonium ions are converted into ammonia gas. The Kjeldahl flask is connected to a distillation device, heated and distilled. A boric acid solution is used to absorb the distilled ammonia gas. An indicator is added to the boric acid solution that has absorbed ammonia gas. The solution is titrated with a standard hydrochloric acid solution. The color of the solution changes from green to dark red, and the titration end point is reached. The total nitrogen content of the reservoir is calculated according to the dosage and concentration of the standard hydrochloric acid solution and the volume of the reservoir water sample.

[0081] Mix potassium persulfate with the water quality measurement sample to convert various forms of phosphorus in the water quality measurement sample into orthophosphate. Under acidic conditions, add ammonium molybdate and potassium antimony tartrate to the water quality measurement sample to generate blue phosphomolybdenum blue. Use a spectrophotometer to measure the absorbance, and calculate the total phosphorus content of the reservoir based on the standard curve.

[0082] Collect reservoir water samples and inject them into the TOC instrument to measure the organic carbon content of the reservoir.

[0083] A further improvement of the technical solution of the present invention is that in step 2, the process of preprocessing the reservoir carbon emission monitoring data and calculating the vegetation coverage rate of the reservoir area includes:

[0084] Based on the Laida criterion, outliers in reservoir carbon emission monitoring data are removed and reservoir carbon emission monitoring data are standardized;

[0085] Using the area of ​​the reservoir carbon emission study area, the vegetation coverage area of ​​the reservoir drawdown zone, and the soil-air interface area of ​​the reservoir drawdown zone, the process of calculating the vegetation coverage rate of the reservoir area is as follows:

[0086]

[0087] Among them, A is the vegetation coverage rate in the reservoir area, A t is the vegetation coverage area of ​​the reservoir drawdown zone, A 0 is the area of ​​reservoir carbon emission research.

[0088] Preferably, in step 3, the process of obtaining the impact index of vegetation coverage on carbon emissions includes:

[0089] The carbon emissions per unit area of ​​the vegetation coverage area in the reservoir drawdown zone and the carbon emissions per unit area of ​​the soil-air interface in the reservoir drawdown zone are extracted. The process of calculating the carbon emissions per unit area of ​​the vegetation coverage area in the reservoir drawdown zone and the carbon emissions per unit area of ​​the soil-air interface in the reservoir drawdown zone is as follows:

[0090] T t =E t ×A t

[0091] T u =E u ×A

[0092] Among them, E t and E u are the carbon emissions per unit area of ​​the vegetation coverage area in the reservoir drawdown zone and the carbon emissions per unit area of ​​the soil-air interface in the reservoir drawdown zone, A t and A u are the vegetation coverage area and the soil-air interface area of ​​the reservoir drawdown zone, respectively. t and T u They are the carbon emissions from the vegetation-covered area in the reservoir drawdown zone and the carbon emissions from the soil-air interface in the reservoir drawdown zone;

[0093] The process of calculating the impact index of vegetation coverage on carbon emissions by using the carbon emissions of the vegetation coverage area in the reservoir drawdown zone and the carbon emissions of the soil-air interface in the reservoir drawdown zone includes:

[0094]

[0095] Among them, I is the impact index of vegetation coverage on carbon emissions, T t and T u are the carbon emissions from the vegetation coverage area in the reservoir drawdown zone and the carbon emissions from the soil-air interface in the reservoir drawdown zone, respectively. A is the vegetation coverage rate in the reservoir area.

[0096] Preferably, in step 3, the process of obtaining the temperature impact index on carbon emissions through reservoir temperature and carbon emissions includes:

[0097] Using the linear regression model, taking the reservoir temperature as the independent variable and carbon emissions as the dependent variable, the linear regression expression y=a+bx is set, and the linear regression model parameters are calculated by the least squares method to obtain the impact index of reservoir temperature on carbon emissions. The specific calculation process is as follows:

[0098]

[0099] Among them, b is the impact index of temperature on carbon emissions, x i is the reservoir temperature, y iis the carbon emission, and n is the number of reservoir temperature collection and the number of carbon emission collection.

[0100] Preferably, in step 3, the process of using water quality data to obtain the impact index of water quality on carbon emissions includes:

[0101] According to the reservoir carbon emission index, weights are assigned to the total nitrogen, total phosphorus and organic carbon concentrations in the reservoir, and the process of calculating the impact index of water quality data on carbon emissions is as follows:

[0102] Y=w N ×y N +w P ×y P +w C ×y C

[0103] Among them, Y is the impact index of water quality on carbon emissions, w N is the total nitrogen weight in the reservoir, w P is the total phosphorus weight in the reservoir, w C is the organic carbon concentration in the reservoir, y N is the actual total nitrogen content in the reservoir, y P is the actual total phosphorus content in the reservoir, y C is the actual reservoir organic carbon concentration.

[0104] Preferably, in step 4, the process of using the random forest algorithm to construct a reservoir carbon emission monitoring model includes:

[0105] The vegetation coverage rate of the reservoir area and its impact index on carbon emissions, the reservoir temperature and its impact index on carbon emissions, and the water quality data and its impact index on carbon emissions are used as data sets and divided into training and test sets in a ratio of 7:3;

[0106] Initialize the random forest model parameters, take the reservoir carbon emission monitoring data as input features, and take the impact index of each reservoir carbon emission monitoring data on carbon emissions as output features;

[0107] The training set data is used to train the random forest model. Through iterative training, the nonlinear relationship between the vegetation coverage rate in the reservoir area and the index of the impact of the vegetation coverage rate on carbon emissions, the nonlinear relationship between the reservoir temperature and the index of the impact of the temperature on carbon emissions, and the nonlinear relationship between the water quality data and the index of the impact of water quality on carbon emissions are learned to obtain the trained random forest model.

[0108] The test set data is input into the trained random forest model, the error between the output value of the random forest model and the actual value is evaluated, the parameters of the random forest model are adjusted according to the evaluation results, the performance of the random forest model is optimized, and the reservoir carbon emission monitoring model is obtained.

[0109] Preferably, in step 5, the process of evaluating the carbon emission level of the reservoir and transmitting a corresponding signal to the client includes:

[0110] Combined with the reservoir carbon emission monitoring model, the reservoir carbon emission monitoring data is analyzed. When the vegetation coverage rate has a low impact on carbon emissions, the index is lower than 0.3. When the vegetation coverage rate has a low impact on carbon emissions, the index is between 0.3 and 0.6. It indicates that the vegetation coverage rate has a medium impact on carbon emissions, and a reservoir vegetation coverage warning signal is issued to the client. When the vegetation coverage rate has a high carbon emission index, an emergency signal of reservoir vegetation coverage is issued to the client.

[0111] When the temperature impact index on carbon emissions is lower than 0.3, it indicates that the temperature has a low impact on carbon emissions; when the temperature impact index on carbon emissions is between 0.3 and 0.7, it indicates that the temperature has a medium impact on carbon emissions, and a reservoir temperature warning signal is issued to the client; when the temperature impact index on carbon emissions is higher than 0.7, a reservoir temperature emergency signal is issued to the client;

[0112] When the water quality impact index on carbon emissions is lower than 0.2, it indicates that the water quality has a low impact on carbon emissions; when the water quality impact index on carbon emissions is between 0.2 and 0.6, it indicates that the water quality has a medium impact on carbon emissions, and a reservoir water quality warning signal is issued to the client; when the water quality impact index on carbon emissions is higher than 0.7, a reservoir water quality emergency signal is issued to the client.

[0113] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A reservoir carbon emission data analysis method based on big data technology, characterized in that: The following steps are involved: Step 1: Obtain reservoir carbon emission monitoring data through collection equipment; Step 2: Preprocess reservoir carbon emission monitoring data and calculate the vegetation coverage rate in the reservoir area; Step 3: Obtain the impact index of each reservoir carbon emission monitoring data on carbon emissions through reservoir carbon emission monitoring data; Step 4: Use the random forest algorithm to build a reservoir carbon emission monitoring model; Step 5: Analyze the reservoir carbon emission monitoring data, evaluate the reservoir carbon emission level, and send a corresponding signal to the client.

2. According to claim 1, a reservoir carbon emission data analysis method based on big data technology is characterized by: In step 1, the process of obtaining reservoir carbon emission monitoring data through collection equipment includes: The collection equipment includes drones, multispectral sensors, total stations, ArcGIS software, thermocouple thermometers, gas samplers, infrared carbon dioxide sensors, LI-7700 methane analyzers, Kjeldahl flasks, spectrophotometers, high-temperature combustion tubes and non-dispersive infrared absorbers; the reservoir carbon emission monitoring data include the area of ​​the reservoir carbon emission research area, the area of ​​the vegetation coverage area in the reservoir drawdown zone, the soil-air interface area in the reservoir drawdown zone, the reservoir temperature, the reservoir carbon emissions and water quality data, wherein the water quality data include the total nitrogen, total phosphorus and organic carbon concentrations in the reservoir.

3. The reservoir carbon emission data analysis method based on big data technology according to claim 2 is characterized by: In the step 1, the process of obtaining the area of ​​the reservoir carbon emission research region, the area of ​​the vegetation coverage area in the reservoir drawdown zone, the soil-air interface area in the reservoir drawdown zone, and the reservoir temperature includes: Determine the reservoir carbon emission research area, plan a flight route for the drone according to the shape and area of ​​the reservoir carbon emission research area, and carry out a multispectral sensor on the drone. According to the planned flight route, collect the reflectance of the infrared band and near-infrared band around the reservoir. Calculate the normalized vegetation index through the formula NDVI = (NIR-Red) (NIR+Red), where NIR is the near-infrared band, Red is the infrared band reflectance, and NDVI is the normalized vegetation index. A positive value of the normalized vegetation index indicates that the corresponding area is covered with vegetation. Extract the vegetation coverage area, use a total station to collect the three-dimensional coordinate data of the vegetation coverage area and the soil-air interface of the reservoir carbon emission research area and the reservoir drawdown zone, and import the three-dimensional coordinate data measured by the total station into ArcGIS software to obtain the area of ​​the reservoir carbon emission research area, the area of ​​the vegetation coverage area of ​​the reservoir drawdown zone, and the area of ​​the soil-air interface of the reservoir drawdown zone; According to the reservoir environment, a thermocouple thermometer is selected and calibrated. The thermocouple thermometer is fixedly installed in the center of the reservoir, and the reservoir temperature is collected based on the Seebeck effect.

4. The reservoir carbon emission data analysis method based on big data technology according to claim 3 is characterized by: In step 1, the process of obtaining reservoir carbon emissions and water quality data includes: Use a gas sampler to collect gas samples at different locations on the surface of the reservoir, use an infrared carbon dioxide sensor to collect carbon dioxide concentrations at 8:00 and 17:00 every day, and use the LI-7700 methane analyzer to collect methane concentrations at 8:00 and 17:00 every day in real time. According to the carbon dioxide density and methane density, calculate the carbon dioxide mass and methane mass at 8:00 and 17:00 every day. Use a total station to collect the reservoir volume. The process of calculating and obtaining carbon emissions is as follows: Among them, C1 and C2 are the carbon dioxide concentrations at 8:00 and 17:00 every day, respectively, and C3 and C4 are the methane concentrations at 8:00 and 17:00 every day, respectively. is the mass change of carbon dioxide, is the mass change of methane, and are the molar masses of carbon dioxide and methane respectively, V is the volume of the reservoir, and are the amounts of carbon dioxide and methane, respectively, and n is the carbon emission of the reservoir; The Kjeldahl nitrogen determination method is adopted. A reservoir water sample is taken and placed in a Kjeldahl flask. The volume of the reservoir water sample is recorded. Concentrated sulfuric acid and a catalyst are added. Under heating conditions, all nitrogen-containing compounds in the reservoir water sample are converted into ammonium salts. After the solution is cooled, a sodium hydroxide solution is added. The ammonium ions are converted into ammonia gas. The Kjeldahl flask is connected to a distillation device, heated and distilled. A boric acid solution is used to absorb the distilled ammonia gas. An indicator is added to the boric acid solution that has absorbed ammonia gas. The solution is titrated with a standard hydrochloric acid solution. The color of the solution changes from green to dark red, and the titration end point is reached. The total nitrogen content of the reservoir is calculated according to the dosage and concentration of the standard hydrochloric acid solution and the volume of the reservoir water sample. Mix potassium persulfate with the water quality measurement sample to convert various forms of phosphorus in the water quality measurement sample into orthophosphate. Under acidic conditions, add ammonium molybdate and potassium antimony tartrate to the water quality measurement sample to generate blue phosphomolybdenum blue. Use a spectrophotometer to measure the absorbance, and calculate the total phosphorus content of the reservoir based on the standard curve. Collect reservoir water samples and inject them into the TOC instrument to measure the organic carbon content of the reservoir.

5. The reservoir carbon emission data analysis method based on big data technology according to claim 4 is characterized by: In the step 2, the process of preprocessing the reservoir carbon emission monitoring data and calculating the vegetation coverage rate of the reservoir area includes: Based on the Laida criterion, outliers in reservoir carbon emission monitoring data are removed and reservoir carbon emission monitoring data are standardized; Using the area of ​​the reservoir carbon emission study area, the vegetation coverage area of ​​the reservoir drawdown zone, and the soil-air interface area of ​​the reservoir drawdown zone, the process of calculating the vegetation coverage rate of the reservoir area is as follows: Among them, A is the vegetation coverage rate in the reservoir area, A t is the vegetation coverage area of ​​the reservoir drawdown zone, and A0 is the area of ​​the reservoir carbon emission research area.

6. The reservoir carbon emission data analysis method based on big data technology according to claim 5 is characterized by: In step 3, the process of obtaining the impact index of vegetation coverage on carbon emissions includes: The carbon emissions per unit area of ​​the vegetation coverage area in the reservoir drawdown zone and the carbon emissions per unit area of ​​the soil-air interface in the reservoir drawdown zone are extracted. The process of calculating the carbon emissions per unit area of ​​the vegetation coverage area in the reservoir drawdown zone and the carbon emissions per unit area of ​​the soil-air interface in the reservoir drawdown zone is as follows: T t =E t ×A t T u =E u ×A Among them, E t and E u are the carbon emissions per unit area of ​​the vegetation coverage area in the reservoir drawdown zone and the carbon emissions per unit area of ​​the soil-air interface in the reservoir drawdown zone, A t and A u are the vegetation coverage area and the soil-air interface area of ​​the reservoir drawdown zone, respectively. t and T u They are the carbon emissions from the vegetation-covered area in the reservoir drawdown zone and the carbon emissions from the soil-air interface in the reservoir drawdown zone; The process of calculating the impact index of vegetation coverage on carbon emissions by using the carbon emissions of the vegetation coverage area in the reservoir drawdown zone and the carbon emissions of the soil-air interface in the reservoir drawdown zone includes: Among them, I is the impact index of vegetation coverage on carbon emissions, T t and T u are the carbon emissions from the vegetation coverage area in the reservoir drawdown zone and the carbon emissions from the soil-air interface in the reservoir drawdown zone, respectively. A is the vegetation coverage rate in the reservoir area.

7. The reservoir carbon emission data analysis method based on big data technology according to claim 6 is characterized by: In step 3, the process of obtaining the temperature impact index on carbon emissions through reservoir temperature and carbon emissions includes: Using the linear regression model, taking the reservoir temperature as the independent variable and carbon emissions as the dependent variable, the linear regression expression y=a+bx is set, and the linear regression model parameters are calculated by the least squares method to obtain the impact index of reservoir temperature on carbon emissions. The specific calculation process is as follows: Among them, b is the impact index of temperature on carbon emissions, x i is the reservoir temperature, y i is the carbon emission, and n is the number of reservoir temperature collection and the number of carbon emission collection.

8. The reservoir carbon emission data analysis method based on big data technology according to claim 7 is characterized by: In step 3, the process of using water quality data to obtain the impact index of water quality on carbon emissions includes: According to the reservoir carbon emission index, weights are assigned to the total nitrogen, total phosphorus and organic carbon concentrations in the reservoir, and the process of calculating the impact index of water quality data on carbon emissions is as follows: Y=w N ×y N +w P ×y P +w C ×y C Among them, Y is the impact index of water quality on carbon emissions, w N is the total nitrogen weight in the reservoir, w P is the total phosphorus weight in the reservoir, w C is the organic carbon concentration in the reservoir, y N is the actual total nitrogen content in the reservoir, y P is the actual total phosphorus content in the reservoir, y C is the actual reservoir organic carbon concentration.

9. The reservoir carbon emission data analysis method based on big data technology according to claim 8 is characterized by: In step 4, the process of using the random forest algorithm to construct a reservoir carbon emission monitoring model includes: The vegetation coverage rate of the reservoir area and its impact index on carbon emissions, the reservoir temperature and its impact index on carbon emissions, and the water quality data and its impact index on carbon emissions are used as data sets and divided into training and test sets in a ratio of 7:3; Initialize the random forest model parameters, take the reservoir carbon emission monitoring data as input features, and take the impact index of each reservoir carbon emission monitoring data on carbon emissions as output features; The training set data is used to train the random forest model. Through iterative training, the nonlinear relationship between the vegetation coverage rate in the reservoir area and the index of the impact of the vegetation coverage rate on carbon emissions, the nonlinear relationship between the reservoir temperature and the index of the impact of the temperature on carbon emissions, and the nonlinear relationship between the water quality data and the index of the impact of water quality on carbon emissions are learned to obtain the trained random forest model. The test set data is input into the trained random forest model, the error between the output value of the random forest model and the actual value is evaluated, the parameters of the random forest model are adjusted according to the evaluation results, the performance of the random forest model is optimized, and the reservoir carbon emission monitoring model is obtained.

10. The reservoir carbon emission data analysis method based on big data technology according to claim 9 is characterized by: In step 5, the process of evaluating the carbon emission level of the reservoir and transmitting the corresponding signal to the client includes: Combined with the reservoir carbon emission monitoring model, the reservoir carbon emission monitoring data is analyzed. When the vegetation coverage rate has a low impact on carbon emissions, the index is lower than 0.

3. When the vegetation coverage rate has a low impact on carbon emissions, the index is between 0.3 and 0.

6. It indicates that the vegetation coverage rate has a medium impact on carbon emissions, and a reservoir vegetation coverage warning signal is issued to the client. When the vegetation coverage rate has a high carbon emission index, an emergency signal of reservoir vegetation coverage is issued to the client. When the temperature impact index on carbon emissions is lower than 0.3, it indicates that the temperature has a low impact on carbon emissions; when the temperature impact index on carbon emissions is between 0.3 and 0.7, it indicates that the temperature has a medium impact on carbon emissions, and a reservoir temperature warning signal is issued to the client; when the temperature impact index on carbon emissions is higher than 0.7, a reservoir temperature emergency signal is issued to the client; When the water quality impact index on carbon emissions is lower than 0.2, it indicates that the water quality has a low impact on carbon emissions; when the water quality impact index on carbon emissions is between 0.2 and 0.6, it indicates that the water quality has a medium impact on carbon emissions, and a reservoir water quality warning signal is issued to the client; when the water quality impact index on carbon emissions is higher than 0.7, a reservoir water quality emergency signal is issued to the client.

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