A method and system for real-time prediction of landfill methane release

Abstract

The present invention relates to the technical field of methane data analysis, and specifically to a method and system for real-time prediction of methane release from landfills, comprising the following steps: collecting meteorological conditions, soil conditions and methane concentration data, assigning a time label to each data point, calculating the rate of change of methane concentration between each consecutive time point, and classifying the rate of change using the time label to generate a methane release rate analysis result. In the present invention, a more detailed methane release rate analysis is achieved by calculating the rate of change of methane concentration and classifying it according to the time label. The key environmental variables that affect the methane release rate are effectively identified, and a dynamic simulation model is constructed based on the prediction results of the future trend of methane release intensity. The sensitivity index is further used to analyze the impact of meteorological and soil conditions on methane release, and key factors are selected to make the model more in line with the actual situation. With the real-time update of environmental data, the dynamic model is continuously adjusted to effectively reduce the prediction error.

Description

Technical Field

[0001] The present invention relates to the technical field of methane data analysis, and in particular to a method and system for real-time prediction of methane release from a landfill. Background Art

[0002] Methane data analysis is the technical field of collecting, processing and interpreting data related to methane emissions. It is mainly used in environmental science and climate change research to analyze the release patterns of methane in natural and anthropogenic environments. The field includes the use of sensors, satellite remote sensing technology and ground measurement equipment to monitor and predict methane releases. In addition, statistical models, machine learning algorithms and big data technologies are used to analyze data to identify emission sources, assess emissions and predict future methane emission trends.

[0003] Among them, the real-time prediction of landfill methane release method monitors and predicts methane emissions in landfills in real time. It includes installing a sensor network in the landfill to collect data on key variables such as gas concentration, temperature, humidity, etc. in real time, analyzing real-time data in combination with external factors such as meteorological conditions, and predicting the generation and release patterns of methane, thereby helping managers take timely control measures to reduce methane emissions.

[0004] Existing technologies mainly collect data such as gas concentration, temperature, and humidity in landfills in real time through sensor networks, and analyze them in combination with meteorological conditions. However, in terms of data analysis of methane release, it is difficult to accurately capture subtle changes in the rate of change of methane concentration, resulting in a relatively rough analysis of the methane release rate, which is not conducive to accurately identifying key environmental variables that affect methane release. In addition, existing technologies are difficult to adapt to changes in environmental conditions in real time, resulting in deviations between the predicted results and the actual situation. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for real-time prediction of landfill methane release.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for real-time prediction of landfill methane release, comprising the following steps:

[0007] S1: Collect meteorological conditions, soil conditions and methane concentration data, assign a time label to each data point, calculate the methane concentration change rate between each consecutive time point, and use the time label to classify the change rate to generate the methane release rate analysis results;

[0008] S2: Based on the methane release rate analysis results, identify the environmental variables that affect the methane release rate, predict the future trend of the methane release intensity according to the time series data of the environmental variables, and obtain a preliminary prediction result of the methane release intensity;

[0009] S3: Based on the preliminary prediction results of methane release intensity, a dynamic simulation model of methane release is constructed, the sensitivity index of the model is calculated to analyze the influence of meteorological conditions and soil conditions, key factors affecting methane release are selected, and dynamic sensitivity analysis results are generated;

[0010] S4: Based on the dynamic sensitivity analysis results, the dynamic simulation model of methane release is updated according to the current environmental data, the model is adjusted to match the new data points, the difference between the predicted and measured data is compared, and an optimized methane release prediction result is generated.

[0011] As a further solution of the present invention, the step of obtaining the methane concentration change rate between each consecutive time point is specifically as follows:

[0012] S111: Collect meteorological conditions, soil temperature, humidity, and methane concentration data, and assign a time label to each data point using the formula:

[0013]

[0014] Calculate the Change rate of methane concentration at time point ;

[0015] in, It is The methane concentration at a given time point, It is The methane concentration at a given time point, It is The time stamp of the time point, It is Time stamp of the time point;

[0016] S112: According to the Change rate of methane concentration at time point , using the formula:

[0017]

[0018] Calculate the Average methane release rate over the same time period , and obtain the analysis results of methane release rate;

[0019] in, It is The number of data points in the class time period, It is An indexed collection of time-class time periods.

[0020] As a further solution of the present invention, the step of obtaining the environmental variables that affect the methane release rate is specifically:

[0021] S211: According to the Average methane release rate over the same time period , using the formula:

[0022]

[0023] Compute environment variables In the Correlation coefficient between time period and methane release rate ;

[0024] in, Is an environment variable In the The relevance weight of the time period is obtained through statistical analysis. is the index of the time period, is the total number of time periods, It is a set that considers all environment variables;

[0025] S212: According to the correlation coefficient , using the formula:

[0026]

[0027] Calculate each environment variable The average correlation coefficient .

[0028] As a further solution of the present invention, the step of obtaining the future trend of the predicted methane release intensity is specifically:

[0029] S221: According to each environment variable The average correlation coefficient , combined with time series data of environmental variables , using the formula:

[0030]

[0031] Calculate a single future time point Prediction of methane release intensity ;

[0032] in, Is an environment variable At a future time data;

[0033] S222: According to the single future time point Prediction of methane release intensity , using the formula:

[0034]

[0035] Integrate to get future time points Future trends in the intensity of methane release , and obtain the preliminary prediction results of methane release intensity;

[0036] in, is the adjustment factor, determined through historical trend analysis and data fitting. is the adjustment factor, which is determined by averaging historical data. is the number of time points used for the average calculation.

[0037] As a further solution of the present invention, the step of obtaining the sensitivity index of the calculation model is specifically as follows:

[0038] S311: According to the future time point Future trends in the intensity of methane release , using the formula:

[0039]

[0040] Build dynamic simulation models and calculate time points Simulated methane release ;

[0041] in, is the model scaling factor, determined by minimizing the model fitting error, is the attenuation influence coefficient, adjusted based on experimental data, is the decay rate parameter, obtained through data analysis, Used to represent exponential decay, Represents the time interval from the start of the simulation to the present;

[0042] S312: According to the time point Simulated methane release , using the formula:

[0043]

[0044] Computational model in time Sensitivity index ;

[0045] in, variables representing meteorological or soil conditions, is the change in meteorological or soil conditions, is the model output for the input The partial derivative of is the sensitivity enhancement factor, determined by data analysis, Used to handle positive and negative effects of changing quantities.

[0046] As a further solution of the present invention, the step of obtaining the key factors affecting the methane release amount is specifically:

[0047] S321: According to the model, at time Sensitivity index , using the formula:

[0048] Calculate time points Rating of the degree of impact of environmental factors ;

[0049] in, , is the weight coefficient, determined through data analysis and expert consultation, It's time point The absolute value of the sensitivity index, is the total number of time points, It’s a point in time;

[0050] S322: According to the time point Rating of the degree of impact of environmental factors , using the formula:

[0051]

[0052] Calculate the highest value in the rating , and obtain the results of dynamic sensitivity analysis;

[0053] in, For all Select the maximum value from .

[0054] As a further solution of the present invention, the step of obtaining the difference between the comparison prediction and the measured data is specifically:

[0055] S411: According to the highest value in the rating , using the formula:

[0056]

[0057] Update at time The model output is ;

[0058] in, It is real-time environmental data. , is the adjustment factor, obtained through performance analysis and current data trends;

[0059] S412: According to the time point The model output is , using the formula:

[0060]

[0061] Calculate time points The difference between the predicted and measured data under , and obtain the optimized methane release prediction result;

[0062] in, is the measured methane release data, indicating the time point Measured methane release,

[0063] , is the calibration coefficient, obtained through statistical analysis and model regression.

[0064] A real-time prediction system for landfill methane release, the real-time prediction system for landfill methane release is used to execute the above-mentioned real-time prediction method for landfill methane release, the system comprising:

[0065] The data processing module collects data on meteorological conditions, soil conditions, and methane concentrations, assigns a time tag to each data point, prepares the data for preliminary analysis, and generates data processing results;

[0066] The rate analysis module calculates the methane concentration change rate between consecutive time points based on the data processing results, and classifies the change rate using time tags to generate a methane release rate analysis result;

[0067] The trend prediction module predicts key environmental variables and future trends of methane release intensity based on the methane release rate analysis results, and generates a preliminary prediction result of methane release intensity;

[0068] The sensitivity analysis module constructs a dynamic simulation model of methane release based on the preliminary prediction results of the methane release intensity, calculates the sensitivity index of the model to analyze the influence of meteorological conditions and soil conditions, and generates dynamic sensitivity analysis results;

[0069] The model optimization module uses the results of the dynamic sensitivity analysis to update the dynamic simulation model of methane release according to the current environmental data, adjusts the model to match the new data points, and generates optimized methane release prediction results.

[0070] Compared with the prior art, the advantages and positive effects of the present invention are:

[0071] In the present invention, a more detailed analysis of the methane release rate is achieved by calculating the rate of change of methane concentration and classifying it according to time tags. The key environmental variables that affect the methane release rate are effectively identified. A dynamic simulation model is constructed based on the prediction results of the future trend of methane release intensity. The influence of meteorological and soil conditions on methane release is further analyzed using sensitivity indicators, and key factors are selected to make the model more in line with the actual situation. With the real-time update of environmental data, the dynamic model is continuously adjusted, the accuracy is continuously improved, and the prediction error is effectively reduced, thereby greatly improving the accuracy of methane release prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0073] Figure 2 A flow chart of the steps for obtaining the methane concentration change rate between each consecutive time point according to the present invention;

[0074] Figure 3 A flow chart of the steps for obtaining environmental variables that affect the rate of methane release for the present invention;

[0075] Figure 4 A flow chart of the steps for obtaining the future trend of the methane release intensity predicted by the present invention;

[0076] Figure 5 A flow chart of the steps for obtaining the sensitivity index of the calculation model of the present invention;

[0077] Figure 6 A flow chart of the steps for obtaining the key factors affecting the methane release amount selected for the present invention;

[0078] Figure 7 The figure is a flow chart of the steps for obtaining the difference between the predicted and measured data according to the present invention. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0080] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0081] Embodiment 1

[0082] See also Figure 1 The present invention provides a technical solution: a method for real-time prediction of landfill methane release, comprising the following steps:

[0083] S1: Collect meteorological conditions, soil conditions and methane concentration data, assign a time label to each data point, calculate the methane concentration change rate between each consecutive time point, and use the time label to classify the change rate to generate the methane release rate analysis results;

[0084] S2: Based on the results of methane release rate analysis, identify the environmental variables that affect the methane release rate, predict the future trend of methane release intensity based on the time series data of environmental variables, and obtain the preliminary prediction results of methane release intensity;

[0085] S3: Based on the preliminary prediction results of methane release intensity, a dynamic simulation model of methane release is constructed, the sensitivity index of the model is calculated to analyze the influence of meteorological conditions and soil conditions, the key factors affecting methane release are selected, and dynamic sensitivity analysis results are generated;

[0086] S4: Based on the results of dynamic sensitivity analysis, the dynamic simulation model of methane release is updated according to the current environmental data, the model is adjusted to match the new data points, the difference between the predicted and measured data is compared, and the optimized methane release prediction results are generated;

[0087] The results of the methane release rate analysis include charts of methane concentration changes in different time periods, methane release rate indicators divided by category, and preliminary trend classifications. The preliminary prediction results of methane release intensity include trend prediction charts of key environmental variables, lists of environmental factors affecting methane release, and predicted values ​​of methane release intensity at multiple time points. The results of the dynamic sensitivity analysis include the influence ranking of key environmental factors, the numerical results of the sensitivity analysis, and the contribution of each factor to changes in methane release. The optimized methane release prediction results include performance comparisons before and after model adjustment, numerical differences between predictions and measurements, and prediction update information based on the latest data.

[0088] See also Figure 2 , the specific steps for calculating the methane concentration change rate between each consecutive time point are:

[0089] S111: Collect meteorological conditions, soil temperature, humidity, and methane concentration data, and assign a time label to each data point using the formula:

[0090]

[0091] Calculate the Change rate of methane concentration at time point ;

[0092] in, It is The methane concentration at a given time point, It is The methane concentration at a given time point, It is The time stamp of the time point, It is Time stamp of the time point;

[0093] S112: According to Change rate of methane concentration at time point , using the formula:

[0094]

[0095] Calculate the Average methane release rate over the same time period , and obtain the analysis results of methane release rate;

[0096] in, It is The number of data points in the class time period, It is An indexed collection of time-class time periods.

[0097] The calculation process is as follows:

[0098] For the formula:

[0099]

[0100] The methane concentration and time point data in the landfill are as follows:

[0101] Time , methane concentration , time point , methane concentration , time point , methane concentration .

[0102] from arrive Rate of change: .

[0103] from arrive Rate of change: .

[0104] The results showed that within each hour, the methane concentration increased , indicating that the methane release rate is consistent in the two time periods.

[0105] For the formula:

[0106]

[0107] The time periods are divided into two categories:

[0108] category : Time period , ,category : Time period , .

[0109] category The average rate of: ;

[0110]

[0111] category The average rate of: ;

[0112]

[0113] The average methane release rate in each time period is , indicating that the methane release rate remained consistent in two different time periods. The calculation of the methane concentration change rate provides a preliminary understanding of the methane release rate in each time period. The average rate calculated by classification further summarizes the rate data within the time period, ensuring the comprehensiveness and accuracy of the analysis results. Through these calculations, the trend of methane release and its changes can be effectively evaluated and predicted.

[0114] See also Figure 3 , the steps to identify the environmental variables that affect the rate of methane release are as follows:

[0115] S211: According to Average methane release rate over the same time period , using the formula:

[0116]

[0117] Compute environment variables In the Correlation coefficient between time period and methane release rate ;

[0118] in, Is an environment variable In the The relevance weight of the time period is obtained through statistical analysis. It is the index of the time period, which is used to distinguish different time periods in the data set. is the total number of time periods, It is a set that considers all environment variables;

[0119] S212: According to the correlation coefficient , using the formula:

[0120]

[0121] Calculate each environment variable The average correlation coefficient , the average correlation coefficient The higher the value, the greater the impact.

[0122] The calculation process is as follows:

[0123] The environmental variables are temperature, humidity and wind speed. The data of each environmental variable and the methane release rate during the three monitored time periods are as follows:

[0124] (Methane release rate, unit: ppm / hour): , , .

[0125] (Temperature dependence weight): , , .

[0126] (Weight of humidity correlation): , , .

[0127] (Wind speed correlation weight): , , .

[0128] For the formula:

[0129]

[0130] Temperature correlation coefficient :

[0131]

[0132] Correlation coefficient of humidity :

[0133]

[0134] Correlation coefficient of wind speed :

[0135]

[0136] Using the above obtained The average correlation coefficient was calculated for each environmental variable.

[0137] For the formula:

[0138]

[0139] Average correlation coefficient of temperature : .

[0140] Average correlation coefficient of humidity : .

[0141] Average correlation coefficient of wind speed : .

[0142] Average correlation coefficient of temperature Temperature was shown to have the greatest impact on methane release rates and is therefore considered the most critical environmental variable. Methods and calculation of correlation coefficients It is an effective way to locate the factors affecting methane release.

[0143] See also Figure 4 , the steps for obtaining the future trend of methane release intensity prediction are as follows:

[0144] S221: According to each environment variable The average correlation coefficient , combined with time series data of environmental variables , using the formula:

[0145]

[0146] Calculate a single future time point Prediction of methane release intensity ;

[0147] in, Is an environment variable At a future time data;

[0148] S222: Based on a single future time point Prediction of methane release intensity , using the formula:

[0149]

[0150] Integrate to get future time points Future trends in the intensity of methane release , and obtain the preliminary prediction results of methane release intensity;

[0151] in, is the adjustment factor, which is determined through historical trend analysis and data fitting to adjust the impact of the immediate forecast value. It is an adjustment factor, which is determined by averaging historical data to balance the impact of historical data on future forecasts. is the number of time points used for the average calculation, which determines the size of the historical time window and is determined based on data availability.

[0152] The calculation process is as follows:

[0153] Using environment variables The average correlation coefficient and future time series data of this environment variable , predicting the intensity of methane release.

[0154] For the formula:

[0155]

[0156] Select temperature as an environment variable , the average correlation coefficient of the temperature calculated previously .

[0157] Temperature data for three future time points : .

[0158] Methane release intensity prediction at time point 1: .

[0159] Methane release intensity prediction at time point 2: .

[0160] Methane release intensity prediction at time point 3: .

[0161] Comprehensive instant prediction value and based on previous The average predicted values ​​at different time points are used to comprehensively assess the intensity of future methane release.

[0162] For the formula:

[0163]

[0164] Adjustment coefficient setting: , .

[0165] Compute the average predicted value for the first two time points:

[0166]

[0167] calculate :

[0168]

[0169]

[0170] Calculations show that the predicted intensity of methane release At the third time point, approximately ppm / hour, this result is obtained by synthesizing the real-time data and the historical average through the set adjustment coefficient.

[0171] See also Figure 5 , the steps to obtain the sensitivity index of the calculation model are as follows:

[0172] S311: Based on future time points Future trends in the intensity of methane release , using the formula:

[0173]

[0174] Build dynamic simulation models and calculate time points Simulated methane release ;

[0175] in, is the model scaling factor, which is determined by minimizing the error between historical data and model fitting. is the attenuation influence coefficient, which indicates the influence of time attenuation on the methane release simulation, and is adjusted based on experimental data. It is the decay rate parameter, which determines how fast the decay effect changes over time. It is the most suitable rate obtained through data analysis. is the natural logarithm base, used to represent exponential decay, Represents the time interval from the start of the simulation to the present;

[0176] S312: According to the time point Simulated methane release , using the formula:

[0177]

[0178] Computational model in time Sensitivity index ,if A large value of indicates that the model is very sensitive to changes in the corresponding meteorological or soil conditions, that is, a small change in these conditions will lead to a significant change in methane release. A small value of or close to zero means that these conditions have a low impact on methane release;

[0179] in, variables representing meteorological or soil conditions, is the change in meteorological or soil conditions, is the model output for the input The partial derivative of is the sensitivity enhancement factor, which is determined through data analysis and is used to adjust the model's response sensitivity to small environmental changes. It is the absolute value of the change and is used to handle the positive and negative effects of the change.

[0180] The calculation process is as follows:

[0181] Preliminary prediction results based on the intensity of methane release Build dynamic simulation models.

[0182] For the formula:

[0183]

[0184] Setting parameters: ppm / hour, (Model scaling factor, determined by minimizing the error between historical data and model fitting), (Attenuation influence coefficient, adjusted based on experimental data), (decay rate parameter, the most adaptive rate obtained through data analysis), (Simulation time point).

[0185] Model output calculation:

[0186]

[0187]

[0188] Set sensitivity and variable relationships: (express right sensitivity), (express right slight dependency on ).

[0189] Sensitivity index calculation:

[0190]

[0191]

[0192]

[0193] Sensitivity calculation:

[0194]

[0195]

[0196] Simulated methane release :result ppm / hour shows the methane release from the model at the fifth time unit, taking into account the time decay effect.

[0197] Sensitivity index :result Indicates that in the environment variable When the temperature changes by 1.0 unit, the predicted methane emission will increase by about 0.259 ppm / hour. This value shows that the model is relatively sensitive to changes in environmental variables, and appropriate environmental changes can lead to significant changes in methane emission.

[0198] See also Figure 6 , the steps for obtaining the key factors affecting methane release are as follows:

[0199] S321: According to the model at time Sensitivity index , using the formula:

[0200]

[0201] Calculate time points Rating of the degree of impact of environmental factors ;

[0202] in, , is a weight coefficient, determined through data analysis and expert consultation, used to adjust the relative importance of a single time point versus the long-term trend. It's time point The absolute value of the sensitivity index is used to evaluate the overall impact of all considered time points. is the total number of time points, It’s a point in time;

[0203] S322: According to the time point Rating of the degree of impact of environmental factors , using the formula:

[0204]

[0205] Calculate the highest value in the rating , and obtain the results of dynamic sensitivity analysis;

[0206] in, For all Select the maximum value to represent the most critical environmental factor.

[0207] The calculation process is as follows:

[0208] Based on the previously calculated sensitivity index A preliminary screen was conducted to identify environmental factors that have a significant impact on methane emissions.

[0209] For the formula:

[0210]

[0211] Setting parameters: , (Weight coefficient, determined through historical data analysis and expert consultation, balancing the impact of a single time point and the overall trend), (the total number of time points considered, representing the total evaluation time interval), set the sensitivity index for other time points: .

[0212] Calculation process:

[0213]

[0214]

[0215]

[0216] Based on comprehensive rating , the factor with the highest sensitivity score was selected as the key factor.

[0217] For the formula:

[0218]

[0219] The comprehensive scores for the three time points are as follows: .

[0220]

[0221] result

[0222] The results show that the environmental factors at the third time point have the most significant impact on methane release among the time points considered, which means that the environmental factors at this time point should be considered as the key influencing factors among all the environmental factors evaluated.

[0223] See also Figure 7 , the specific steps for obtaining the difference between the predicted and measured data are:

[0224] S411: Based on the highest value in the rating , using the formula:

[0225]

[0226] Update at time The model output is ;

[0227] in, It is real-time environmental data, including weather and soil changes, , is the adjustment factor, obtained through performance analysis and current data trends, to ensure that the model reflects the changes in real-time data;

[0228] S412: According to the time point The model output is , using the formula:

[0229]

[0230] Calculate time points The difference between the predicted and measured data under , and obtain the optimized methane release prediction result;

[0231] in, is the measured methane release data, indicating the time point The actual measured methane release,

[0232] , It is the calibration coefficient, which is optimized by minimizing the error to ensure the accuracy of the prediction results. It is obtained through statistical analysis and model regression to adjust the relationship between the model output and the actual observed data.

[0233] The calculation process is as follows:

[0234] Based on the latest meteorological and soil data, the model is updated to reflect immediate environmental changes.

[0235] For the formula:

[0236]

[0237] Setting parameters: (real-time environmental data, such as the latest measured temperature or humidity changes), , (The adjustment coefficient is obtained based on historical performance analysis and recent data trends to ensure that the model accurately reflects real-time data changes).

[0238] Calculation process:

[0239]

[0240]

[0241] Based on the updated model output and the measured data, the difference between the predicted and measured data is calculated.

[0242] For the formula:

[0243]

[0244] Setting parameters: (Updated model output), (Methane release data is at time point The actual measured value), , (Calibration coefficients are optimized by minimizing the error to ensure the accuracy of the prediction results).

[0245] Calculation process:

[0246]

[0247]

[0248] Indicates that the updated model predicts at time point The methane release is 0.407ppm / hour. The difference between the predicted and measured values ​​is 0.007 ppm / hour, indicating that the model prediction is very close to the actual situation, reflecting the accuracy of the model and the effective integration of real-time data.

[0249] A real-time prediction system for landfill methane release, which is used to execute the above-mentioned real-time prediction method for landfill methane release, and the system comprises:

[0250] The data processing module collects data on meteorological conditions, soil conditions, and methane concentrations, assigns a time tag to each data point, prepares the data for preliminary analysis, and generates data processing results;

[0251] The rate analysis module calculates the methane concentration change rate between consecutive time points based on the data processing results, and uses time tags to classify the change rate to generate methane release rate analysis results;

[0252] The trend prediction module predicts key environmental variables and future trends of methane release intensity based on the methane release rate analysis results, and generates preliminary prediction results of methane release intensity;

[0253] The sensitivity analysis module builds a dynamic simulation model of methane release based on the preliminary prediction results of methane release intensity, calculates the sensitivity index of the model to analyze the influence of meteorological conditions and soil conditions, and generates dynamic sensitivity analysis results;

[0254] The model optimization module uses the results of the dynamic sensitivity analysis to update the dynamic simulation model of methane release according to the current environmental data, adjusts the model to match the new data points, and generates optimized methane release prediction results.

[0255] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for real-time prediction of landfill methane release, characterized in that: The following steps are involved: Collect meteorological conditions, soil conditions and methane concentration data, assign a time label to each data point, calculate the rate of change of methane concentration between each consecutive time point, and use the time label to classify the rate of change to generate the methane release rate analysis results; The steps for calculating the methane concentration change rate between each consecutive time point are specifically as follows: Collect meteorological conditions, soil temperature, humidity, and methane concentration data and assign a time label to each data point using the formula: Calculate the Change rate of methane concentration at time point ; in, It is The methane concentration at a given time point, It is The methane concentration at a given time point, It is The time stamp of the time point, It is Time stamp of the time point; According to the said Change rate of methane concentration at time point , using the formula: Calculate the Average methane release rate over the same time period , and obtain the analysis results of methane release rate; in, It is The number of data points in the class time period, It is The index collection of class time periods; Based on the methane release rate analysis results, the environmental variables that affect the methane release rate are identified, and according to the time series data of the environmental variables, the future trend of the methane release intensity is predicted to obtain a preliminary prediction result of the methane release intensity; The steps of obtaining the environmental variables that affect the methane release rate are specifically as follows: According to the said Average methane release rate over the same time period , using the formula: Compute environment variables In the Correlation coefficient between time period and methane release rate ; in, Is an environment variable In the The relevance weight of the time period is obtained through statistical analysis. is the index of the time period, is the total number of time periods, It is a set that considers all environment variables; According to the correlation coefficient , using the formula: Calculate each environment variable The average correlation coefficient ; The steps for obtaining the future trend of the predicted methane release intensity are specifically as follows: According to each environment variable The average correlation coefficient , combined with time series data of environmental variables , using the formula: Calculate a single future time point Prediction of methane release intensity ; in, Is an environment variable At a future time data; According to the single future time point Prediction of methane release intensity , using the formula: Integrate to get future time points Future trends in the intensity of methane release , and obtain the preliminary prediction results of methane release intensity; in, is the adjustment factor, determined through historical trend analysis and data fitting. is the adjustment factor, which is determined by averaging historical data. is the number of time points used for the average calculation; Based on the preliminary prediction results of methane release intensity, a dynamic simulation model of methane release is constructed, the sensitivity index of the model is calculated to analyze the influence of meteorological conditions and soil conditions, key factors affecting methane release are selected, and dynamic sensitivity analysis results are generated; The steps for obtaining the sensitivity index of the calculation model are specifically as follows: According to the future time point Future trends in the intensity of methane release , using the formula: Build dynamic simulation models and calculate time points Simulated methane release ; in, is the model scaling factor, determined by minimizing the model fitting error, is the attenuation influence coefficient, adjusted based on experimental data, is the decay rate parameter, obtained through data analysis, Used to represent exponential decay, Represents the time interval from the start of the simulation to the present; According to the time point Simulated methane release , using the formula: Computational model in time Sensitivity index ; in, variables representing meteorological or soil conditions, is the change in meteorological or soil conditions, is the model output for the input The partial derivative of is the sensitivity enhancement factor, determined by data analysis, Used to handle the positive and negative effects of changes; The steps for obtaining the key factors affecting the methane release amount are specifically as follows: According to the model, at time Sensitivity index , using the formula: Calculate time points Rating of the degree of impact of environmental factors ; in, , is the weight coefficient, determined through data analysis and expert consultation, It's time point The absolute value of the sensitivity index, is the total number of time points, It’s a point in time; According to the time point Rating of the degree of impact of environmental factors , using the formula: Calculate the highest value in the rating , and obtain the results of dynamic sensitivity analysis; in, For all Select the maximum value among; Based on the dynamic sensitivity analysis results, the dynamic simulation model of methane release is updated according to the current environmental data, the model is adjusted to match the new data points, the difference between the predicted and measured data is compared, and the optimized methane release prediction results are generated.

2. The method for real-time prediction of landfill methane release according to claim 1, characterized in that: The steps for obtaining the difference between the predicted and measured data are specifically as follows: According to the highest value in the rating , using the formula: Update at time The model output is ; in, It is real-time environmental data. , is the adjustment factor, obtained through performance analysis and current data trends; According to the time point The model output is , using the formula: Calculate time points The difference between the predicted and measured data under , and obtain the optimized methane release prediction result; in, is the measured methane release data, indicating the time point Measured methane release, , is the calibration coefficient, obtained through statistical analysis and model regression.

3. A real-time prediction system for landfill methane release, characterized in that: According to any one of claims 1 to 2, the method for real-time prediction of landfill methane release comprises: The data processing module collects data on meteorological conditions, soil conditions, and methane concentrations, assigns a time tag to each data point, prepares the data for preliminary analysis, and generates data processing results; The rate analysis module calculates the methane concentration change rate between consecutive time points based on the data processing results, and classifies the change rate using time tags to generate a methane release rate analysis result; The trend prediction module predicts key environmental variables and future trends of methane release intensity based on the methane release rate analysis results, and generates a preliminary prediction result of methane release intensity; The sensitivity analysis module constructs a dynamic simulation model of methane release based on the preliminary prediction results of the methane release intensity, calculates the sensitivity index of the model to analyze the influence of meteorological conditions and soil conditions, and generates dynamic sensitivity analysis results; The model optimization module uses the results of the dynamic sensitivity analysis to update the dynamic simulation model of methane release according to the current environmental data, adjusts the model to match the new data points, and generates optimized methane release prediction results.

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