A methane emission detection system and method

Abstract

The application discloses a methane emission detection system and method, a communication module is used for extracting a preset frequency band signal from a communication signal, and the preset frequency band signal is converted into a digital signal and then decoded to obtain first real-time information; a fuzzy positioning module is used for obtaining the first real-time information, determining a methane emission target area and a methane emission amount in the target area according to the first real-time information; a target acquisition module is used for dividing the methane emission area into a plurality of emission units, obtaining a first methane emission interval and a first methane emission amount; a machine learning module is used for performing machine learning training and verification on the methane emission amount in the methane emission interval based on a training data model, and finally obtaining a second methane emission interval and a second methane emission amount by using the trained data model; and a methane collection module is used for collecting the methane emitted in the methane emission interval. The application can improve the detection precision and reduce the methane emission.

Description

Technical Field

[0001] This application relates to a methane emission detection system and method, belonging to the field of methane emission detection technology. Background Technology

[0002] Methane, the second largest greenhouse gas in the atmosphere, accounts for 25%–28% of direct anthropogenic greenhouse gas emissions and has played a role in global warming second only to CO2. As climate change intensifies global warming, governments, academia, and industry are increasingly concerned about the greenhouse effect and reduction potential of methane emissions. According to publicly available reports, the oil and gas industry accounts for approximately 24% of total global anthropogenic methane emissions. The IPCC points out that reducing methane emissions is a low-cost and technically feasible emission reduction strategy, and that reducing methane emissions can effectively slow the global warming trajectory, buying time to address CO2 emissions.

[0003] The detection and quantification of methane emissions from oil and gas production is the foundation of methane research. The key to solving the problem of inaccurate methane emission detection in the oil and gas industry lies in improving the accuracy of detection (including time and space factors). Improving the accuracy of emission detection and quantification, as well as the recycling and utilization of methane emissions, are currently key aspects of methane emission control. Summary of the Invention

[0004] According to one aspect of this application, a methane emission detection system is provided. This system accurately detects the location and emission volume of methane emissions through a communication module, a fuzzy positioning module, a target acquisition module, a machine learning module, and a methane collection module, thereby improving detection accuracy and effectively collecting the emitted methane, thus reducing methane emissions.

[0005] A methane emission detection system, characterized in that it comprises:

[0006] The communication module is used to extract signals of a preset frequency band from the communication signals, convert the signals of the preset frequency band into digital signals and then decode them to obtain the first real-time information;

[0007] A fuzzy positioning module is used to acquire first real-time information and determine the methane emission target area and the methane emission volume within the target area based on the first real-time information.

[0008] The target acquisition module is used to divide the methane emission area into several emission units and obtain the first methane emission interval and the first methane emission volume based on the maximum inter-class variance algorithm.

[0009] The machine learning module is used to train and validate the methane emission amount within the methane emission range based on the trained data model, and finally use the trained data model to obtain the second methane emission range and the second methane emission amount.

[0010] A methane collection module is used to collect methane emitted within the methane emission range.

[0011] Furthermore, the communication module includes a terrestrial communication module and a satellite communication module;

[0012] The satellite communication module establishes location information and transmission requirements with the ground communication module. The satellite communication module is used to receive communication signals and obtain signals from the preset frequency band of methane emissions in the communication signals and send them to the ground communication module.

[0013] The ground communication module includes a ground base station and one or more ground terminals. The ground base station is used to receive signals from the preset frequency band, and the ground terminal converts the signals from the preset frequency band into digital signals and then decodes them to obtain first real-time information.

[0014] Furthermore, the first real-time information includes first location information and first displacement information;

[0015] The first location information is used to determine the methane emission target area;

[0016] The first displacement information is used to determine the methane displacement within the target area.

[0017] Furthermore, the methane collection module includes:

[0018] Methane aggregation layer, used to aggregate methane;

[0019] The methane sensing module is used to collect methane concentration signals at multiple height locations and send multiple methane concentration signals.

[0020] The control module is used to receive the plurality of methane concentration signals, and when the plurality of methane concentration signals reach the corresponding target concentration value, control the methane collection mechanism to collect the methane in the methane accumulation layer.

[0021] Furthermore, the machine learning module includes:

[0022] The data model is based on several dynamic network units constructed from methane emission regions. The dynamic network units include several emission intervals and the corresponding methane emissions for each interval.

[0023] The dynamic allocation module allocates the training resource set of the methane emission area to the dynamic network unit according to the set rules based on the progress of the task.

[0024] The monitoring module is used to monitor the learning status of dynamic network units for new tasks;

[0025] The result acquisition module is used to acquire the dynamic network units multiple times to form a trained training data model, and to acquire the second emission range and the second methane emission.

[0026] Furthermore, the detection system also includes an inversion module to reduce signal interference caused by natural factors;

[0027] The inversion module includes a simulation unit, a verification unit, and a correction unit;

[0028] The simulation unit is used to simulate the distribution and coupling relationship of the first real-time information and generate real-time information parameter samples.

[0029] The verification unit is used to calculate the predicted parameter sample by deriving the methane column concentration, and to analyze the actual accuracy of the real-time information parameter sample by combining the predicted parameter sample obtained by inversion.

[0030] The correction unit is used to correct the information parameter sample based on the analysis results obtained by the verification unit.

[0031] The correction unit includes state correction and parameter correction. The state correction is used to correct the processing of its signals, and the parameter correction is used to correct position information and displacement information.

[0032] According to another aspect of this application, the present invention provides a method for detecting methane emissions, characterized by comprising the following steps:

[0033] S1: Use the communication module to extract the signal of the preset frequency band from the communication signal, and convert the signal of the preset frequency band into a digital signal and then decode it to obtain the first real-time information;

[0034] S2: Obtain first real-time information, and determine the methane emission target area and the methane emission volume within the target area based on the first real-time information;

[0035] S3: Divide the methane emission area into several emission units, and obtain the first methane emission interval and the first methane emission volume based on the maximum inter-class variance algorithm;

[0036] S4: Obtain the training data model, perform machine learning training on the methane emission amount within the methane emission range and verify it, and finally obtain the trained training data model to obtain the second methane emission range and the second methane emission amount.

[0037] S5: Collect methane emitted within the methane emission range using a methane collection device.

[0038] Furthermore, S1 also includes S1.1: inverting the obtained first real-time information to reduce signal interference caused by natural factors.

[0039] The beneficial effects that this application can produce include:

[0040] 1) The methane emission detection system and method provided in this application include a communication module, an inversion module, a fuzzy positioning module, a target acquisition module, a machine learning module, and a methane collection module. The system extracts and decodes the communication signal of methane emissions to obtain first real-time information. This first real-time information is then inverted to obtain high-precision first real-time information. Based on the first real-time information, a methane emission target area and the methane emission volume within the target area are determined. Machine learning training is performed on the methane emission volume within the methane emission range, and the training is validated to obtain a trained data model. This model then obtains a second methane emission range and a second methane emission volume, and finally collects the methane emitted within the methane emission range. This method not only accurately detects methane emissions and obtains precise and effective emission information, but also collects the emitted methane gas, reducing methane emissions and mitigating their impact on the atmosphere. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the overall structure of a methane emission detection system according to one embodiment of this application. Detailed Implementation

[0042] The present application is described in detail below with reference to the embodiments, but the present application is not limited to these embodiments.

[0043] See Figure 1 A methane emission detection system, characterized in that it comprises:

[0044] The communication module is used to extract signals of a preset frequency band from the communication signals, convert the signals of the preset frequency band into digital signals and then decode them to obtain the first real-time information;

[0045] A fuzzy positioning module is used to acquire first real-time information and determine the methane emission target area and the methane emission volume within the target area based on the first real-time information.

[0046] Fuzzy positioning can achieve a positioning accuracy of about 10 meters. It's important to note that fuzzy positioning systems can only perform zero-dimensional presence detection and one-dimensional linear positioning; they cannot achieve two-dimensional or three-dimensional positioning. Fuzzy positioning base stations calculate location by using the RSSI (Signal Strength Attenuation) of the positioning tag to reach the base station. Because signal strength is easily affected by environmental interference, only fuzzy positioning is possible.

[0047] The target acquisition module is used to divide the methane emission area into several emission units and obtain the first methane emission interval and the first methane emission volume based on the maximum inter-class variance algorithm.

[0048] Specifically, the Otsu's between-class variance algorithm defines two variances: intra-class variance (the weighted sum of the variances of the regions on either side of the threshold) and inter-class variance (the weighted variance of the distance between the mean of the data on either side of the threshold and the overall mean). Inter-class variance characterizes the dispersion of the data on either side of the threshold; a larger inter-class variance indicates a greater difference between the foreground and background, resulting in better binarization. Intra-class variance characterizes the dispersion of the data within two regions; we want the data within the two segmented regions to be as similar as possible, i.e., a smaller intra-class variance is better. Since the sum of the intra-class and inter-class variances is a constant, maximizing the inter-class variance is equivalent to minimizing the intra-class variance.

[0049] The machine learning module is used to train and validate the methane emission amount within the methane emission range based on the trained data model, and finally use the trained data model to obtain the second methane emission range and the second methane emission amount.

[0050] By learning through machine learning modules, the division and calculation of methane emission ranges and emissions can be made more accurate.

[0051] It is worth noting that the first methane emission range and the first methane emission amount refer to the initial emission range and its corresponding emission amount, while the second methane emission range and the second methane emission amount refer to the new emission range and its corresponding emission amount obtained through machine learning. The two may be similar or different. In the process of machine learning, the emission range and emission amount are dynamically changing.

[0052] A methane collection module is used to collect methane emitted within the methane emission range.

[0053] The above modules have effectively detected the location of methane emissions, and methane can be recovered and reused in areas with high methane emissions, thus saving energy and reducing emissions.

[0054] The communication module includes a terrestrial communication module and a satellite communication module;

[0055] The satellite communication module establishes location information and transmission requirements with the ground communication module. The satellite communication module is used to receive communication signals and obtain signals from the preset frequency band of methane emissions in the communication signals and send them to the ground communication module.

[0056] The ground communication module includes a ground base station and one or more ground terminals. The ground base station is used to receive signals from the preset frequency band, and the ground terminal converts the signals from the preset frequency band into digital signals and then decodes them to obtain first real-time information.

[0057] The first real-time information includes first location information and first displacement information;

[0058] The first location information is used to determine the methane emission target area;

[0059] The first displacement information is used to determine the methane displacement within the target area.

[0060] The methane collection module includes:

[0061] Methane aggregation layer, used to aggregate methane;

[0062] The methane sensing module is used to collect methane concentration signals at multiple height locations and send multiple methane concentration signals.

[0063] The control module is used to receive the plurality of methane concentration signals, and when the plurality of methane concentration signals reach the corresponding target concentration value, control the methane collection mechanism to collect the methane in the methane accumulation layer.

[0064] Specifically, the methane accumulation layer is the height distance where the methane concentration and content are highest or relatively high. A methane recovery enclosure layer can be set at this height distance to form a relatively sealed space, which is conducive to the effective recovery of methane and improves the methane recovery rate. The methane sensing module is used for monitoring. When the control module receives the methane concentration signal, it recovers methane according to the target concentration value. The recovered methane can be transported to other places for utilization.

[0065] The machine learning module includes:

[0066] The data model is based on several dynamic network units constructed from methane emission regions. The dynamic network units include several emission intervals and the corresponding methane emissions for each interval.

[0067] The dynamic allocation module allocates the training resource set of the methane emission area to the dynamic network unit according to the set rules based on the progress of the task.

[0068] The monitoring module is used to monitor the learning status of dynamic network units for new tasks;

[0069] The result acquisition module is used to acquire the dynamic network units multiple times to form a trained training data model, and to acquire the second emission range and the second methane emission.

[0070] Specifically, the data model constructs the methane emission area into several dynamic network units. The dynamic allocation module sets allocation rules for the dynamic network units and performs corresponding allocation. These allocation rules can be adjusted according to detection time, area units, etc. The monitoring module monitors the learning status of each new task allocated according to the rules. Finally, the result acquisition module uses the trained data model to obtain the second emission range and the second methane emission.

[0071] The detection system also includes an inversion module to reduce signal interference caused by natural factors;

[0072] The inversion module includes a simulation unit, a verification unit, and a correction unit;

[0073] The simulation unit is used to simulate the distribution and coupling relationship of the first real-time information and generate real-time information parameter samples.

[0074] Specifically, the distribution and coupling relationship of the first real-time information refers to the relationship between the location distribution and the displacement distribution. For example, at time A, the displacement corresponding to point a is a', and at time B, the displacement corresponding to point a is b'; where both the location and displacement change with time or other factors.

[0075] The verification unit is used to calculate the predicted parameter sample by deriving the methane column concentration, and to analyze the actual accuracy of the real-time information parameter sample by combining the predicted parameter sample obtained by inversion.

[0076] Specifically, the regional spectral signals obtained through satellite observation and the methane column concentration obtained through concentration inversion need to be combined with meteorological conditions such as wind direction and speed at the observation site, ground monitoring data, and atmospheric transport models for emission inversion. A model also needs to be established to correct for methane emissions. Methane satellite remote sensing inversion can be divided into the following steps: ① Forward model design, establishing models of atmospheric flow, mixing, and deposition transport processes; ② Inversion process and inversion within the scene range; ③ Spatial column concentration analytical inversion; ④ Inversion result correction and uncertainty analysis. Methane column concentration inversion can be performed using physical methods and data-driven methods: physical methods establish models of radiative transport between the ground, atmosphere, and instrument; data-driven methods extract information from images using statistical methods for inversion. Atmospheric radiative transport models used for inversion include VLIDORT, LBLRTM, and SCIAMACHY.

[0077] The correction unit is used to correct the information parameter sample based on the analysis results obtained by the verification unit.

[0078] The correction unit includes state correction and parameter correction. The state correction is used to correct the processing of its signals, and the parameter correction is used to correct position information and displacement information.

[0079] A method for detecting methane emissions, characterized by comprising the following steps:

[0080] S1: Use the communication module to extract the signal of the preset frequency band from the communication signal, and convert the signal of the preset frequency band into a digital signal and then decode it to obtain the first real-time information;

[0081] S2: Obtain first real-time information, and determine the methane emission target area and the methane emission volume within the target area based on the first real-time information;

[0082] S3: Divide the methane emission area into several emission units, and obtain the first methane emission interval and the first methane emission volume based on the maximum inter-class variance algorithm;

[0083] S4: Obtain the training data model, perform machine learning training on the methane emission amount within the methane emission range and verify it, and finally obtain the trained training data model to obtain the second methane emission range and the second methane emission amount.

[0084] S5: Collect methane emitted within the methane emission range using a methane collection device.

[0085] S1 also includes S1.1: Inverting the obtained first real-time information to reduce signal interference caused by natural factors such as the atmosphere.

[0086] The above description is merely a few embodiments of this application and is not intended to limit this application in any way. Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any changes or modifications made by those skilled in the art without departing from the scope of the technical solution of this application using the disclosed technical content are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A methane emission detection system, characterized in that, include: The communication module is used to extract signals of a preset frequency band from the communication signals, convert the signals of the preset frequency band into digital signals and then decode them to obtain the first real-time information; A fuzzy positioning module is used to acquire first real-time information and determine the methane emission target area and the methane emission volume within the target area based on the first real-time information. The target acquisition module is used to divide the methane emission area into several emission units and obtain the first methane emission interval and the first methane emission volume based on the maximum inter-class variance algorithm. The machine learning module is used to train and validate the methane emission amount within the methane emission range based on the trained data model, and finally use the trained data model to obtain the second methane emission range and the second methane emission amount. A methane collection module is used to collect methane emitted within the methane emission range; The communication module includes a terrestrial communication module and a satellite communication module; The satellite communication module establishes location information and transmission requirements with the ground communication module. The satellite communication module is used to receive communication signals and obtain signals from the preset frequency band of methane emissions in the communication signals and send them to the ground communication module. The ground communication module includes a ground base station and one or more ground terminals. The ground base station is used to receive signals from the preset frequency band, and the ground terminal converts the signals from the preset frequency band into digital signals and then decodes them to obtain first real-time information.

2. The methane emission detection system according to claim 1, characterized in that, The first real-time information includes first location information and first displacement information; The first location information is used to determine the methane emission target area; The first displacement information is used to determine the methane displacement within the target area.

3. The methane emission detection system according to claim 1, characterized in that, The methane collection module includes: Methane aggregation layer, used to aggregate methane; The methane sensing module is used to collect methane concentration signals at multiple height locations and send multiple methane concentration signals. The control module is used to receive the plurality of methane concentration signals, and when the plurality of methane concentration signals reach the corresponding target concentration value, control the methane collection mechanism to collect the methane in the methane accumulation layer.

4. The methane emission detection system according to claim 1, characterized in that, The machine learning module includes: The data model is based on several dynamic network units constructed from methane emission regions. The dynamic network units include several emission intervals and the corresponding methane emissions for each interval. The dynamic allocation module allocates the training resource set of the methane emission area to the dynamic network unit according to the set rules based on the progress of the task. The monitoring module is used to monitor the learning status of dynamic network units for new tasks; The result acquisition module is used to acquire the dynamic network units multiple times to form a trained training data model, and to acquire the second emission range and the second methane emission.

5. A methane emission detection system according to claim 1 or 2, characterized in that, The detection system also includes an inversion module to reduce signal interference caused by natural factors; The inversion module includes a simulation unit, a verification unit, and a correction unit; The simulation unit is used to simulate the distribution and coupling relationship of the first real-time information and generate real-time information parameter samples. The verification unit is used to calculate the predicted parameter sample by deriving the methane column concentration, and to analyze the actual accuracy of the real-time information parameter sample by combining the predicted parameter sample obtained by inversion. A calibration unit is used to calibrate the information parameter samples based on the analysis results obtained by the verification unit; the calibration unit includes state calibration and parameter calibration, the state calibration is used to calibrate the signal processing, and the parameter calibration is used to calibrate the position information and displacement information.

6. A method for detecting methane emissions, characterized in that, Specifically, the following steps are included: S1: Use the communication module to extract the signal of the preset frequency band from the communication signal, and convert the signal of the preset frequency band into a digital signal and then decode it to obtain the first real-time information; S2: Obtain first real-time information, and determine the methane emission target area and the methane emission volume within the target area based on the first real-time information; S3: Divide the methane emission area into several emission units, and obtain the first methane emission interval and the first methane emission volume based on the maximum inter-class variance algorithm; S4: Obtain the training data model, perform machine learning training on the methane emission amount within the methane emission range and verify it, and finally obtain the trained training data model to obtain the second methane emission range and the second methane emission amount. S5: Collect methane emitted within the methane emission zone using a methane collection mechanism; The communication module includes a terrestrial communication module and a satellite communication module; The satellite communication module establishes location information and transmission requirements with the ground communication module. The satellite communication module is used to receive communication signals and obtain signals from the preset frequency band of methane emissions in the communication signals and send them to the ground communication module. The ground communication module includes a ground base station and one or more ground terminals. The ground base station is used to receive signals from the preset frequency band, and the ground terminal converts the signals from the preset frequency band into digital signals and then decodes them to obtain first real-time information.

7. The methane emission detection method according to claim 6, characterized in that, S1 also includes S1.1: Inverting the obtained first real-time information to reduce signal interference caused by natural factors.

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