Method and device for predicting information on atmospheric pollutants from biomass combustion

Abstract

The embodiment of the present application provides a kind of biomass combustion atmospheric pollutant parameter information prediction method and device, the method comprises: obtaining the fire point monitoring data corresponding to first biomass combustion;According to the fire point monitoring data corresponding to first biomass combustion and pollutant diffusion model, the pollutant diffusion simulation data corresponding to first biomass combustion is obtained;Pollutant diffusion simulation data includes the diffusion position of pollutant and the diffusion time corresponding to each diffusion position;According to the diffusion position of pollutant, diffusion time and pollutant prediction model, the pollutant parameter information corresponding to each diffusion position at any time is predicted.The method of the embodiment of the present application predicts the change condition of the parameter information of atmospheric pollutant of biomass combustion with diffusion time by pollutant prediction model, realizes the prediction of the parameter information of atmospheric pollutant of biomass combustion.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method and apparatus for predicting atmospheric pollutant parameters from biomass combustion. Background Technology

[0002] With increasing societal focus on atmospheric environment and climate effects, the need for predicting pollutant transport and diffusion is becoming increasingly urgent to meet the demands of social life and industrial production. Related research indicates that atmospheric particulate matter generated from biomass combustion in my country accounts for approximately 15% of the total atmospheric particulate matter, making it a significant source. Therefore, predicting the transport and diffusion of pollutants (aerosols) generated from biomass combustion is of great importance.

[0003] In related technologies, there is a lack of effective prediction methods for the transport and diffusion of pollutants generated from biomass combustion. Therefore, how to accurately and effectively predict the transport and diffusion of pollutants is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of the present invention provide a method and apparatus for predicting atmospheric pollutant parameters from biomass combustion.

[0005] Specifically, the embodiments of the present invention provide the following technical solutions:

[0006] In a first aspect, embodiments of the present invention provide a method for predicting atmospheric pollutant parameter information from biomass combustion, including:

[0007] Obtain the ignition point monitoring data corresponding to the first biomass combustion;

[0008] Based on the ignition monitoring data and pollutant diffusion model corresponding to the first biomass combustion, pollutant diffusion simulation data corresponding to the first biomass combustion is obtained; the pollutant diffusion simulation data includes the diffusion location of the pollutants and the diffusion time corresponding to each diffusion location;

[0009] Based on the diffusion location, diffusion time, and pollutant prediction model, predict the pollutant parameter information corresponding to each diffusion location at any given time.

[0010] Further, training data is acquired, which includes pollutant parameter information at each moment corresponding to the second biomass combustion.

[0011] The pollutant parameter information at each time point is aggregated and fitted to obtain the pollutant prediction model.

[0012] Further, the pollutant parameter information for each moment corresponding to the second biomass combustion in the training data is obtained, including:

[0013] Acquire monitoring data of pollutants generated by the second biomass combustion and the forward trajectory of pollutant emissions corresponding to the second biomass combustion;

[0014] The monitoring data of pollutants generated by the second biomass combustion are matched with the forward trajectory of the pollutant emissions to determine the matching result;

[0015] Based on the matching results, pollutant parameter information at each time point is obtained.

[0016] Further, the monitoring data of the pollutants includes at least one of the following: the monitoring location of the pollutants and the monitoring time of the pollutants; the matching of the monitoring data of the pollutants generated by the second biomass combustion with the forward trajectory of the pollutant emissions to obtain the matching result includes:

[0017] If the time difference between the monitoring time of the pollutant and the target trajectory point in the pollutant emission forward trajectory is less than a first threshold, and the distance between the monitoring location of the pollutant and the target trajectory point is less than a second threshold, the matching result is determined to be a successful match; the target trajectory point is any trajectory point in the forward trajectory.

[0018] Furthermore, if the matching result is successful, obtaining pollutant parameter information at each time point based on the matching result includes:

[0019] If at least one target trajectory point in the pollutant monitoring data and the pollutant emission forward trajectory is successfully matched, the position of the target trajectory point closest to the fire point in the pollutant emission forward trajectory is determined as the target position, and the time corresponding to the target position is determined as the target time.

[0020] Based on the target time and the monitoring data of the pollutants, obtain pollutant parameter information at each time point.

[0021] Furthermore, the acquisition of monitoring data on pollutants generated from the second biomass combustion includes:

[0022] Obtain pre-existing monitoring data on pollutants;

[0023] Based on the monitoring data of the pollutants, the optical thickness threshold of the pollutants, and the turbidity threshold of the pollutants, the monitoring data of the pollutants generated by the second biomass combustion are obtained;

[0024] Obtaining the forward trajectory of the pollutant emission includes:

[0025] Obtain the ignition point monitoring data corresponding to the second biomass combustion;

[0026] Based on the fire point monitoring data and pollutant transport model, the forward trajectory of pollutant emissions is obtained.

[0027] Further, obtaining the forward trajectory of pollutant emissions based on the fire point monitoring data and the pollutant transport model includes:

[0028] When multiple pollutant emission forward trajectories are obtained based on multiple fire point monitoring data and there is an overlapping area among the pollutant emission forward trajectories, the fire point radiation energy value corresponding to each pollutant emission forward trajectory is determined.

[0029] The pollutant emission forward trajectory corresponding to the largest fire point radiation energy value among all the fire point radiation energy values ​​is taken as the obtained pollutant emission forward trajectory.

[0030] Furthermore, the pollutant parameter information includes at least one of the following:

[0031] Pollutant optical thickness, Escull index, single scattering albedo, asymmetry factor, particle spectral distribution, complex refractive index, percentage of black carbon, radiative forcing, and radiative forcing efficiency.

[0032] Secondly, embodiments of the present invention also provide a device for predicting pollutant parameter information, comprising:

[0033] The acquisition module is used to acquire the ignition monitoring data corresponding to the first biomass combustion.

[0034] The processing module is used to obtain pollutant diffusion simulation data corresponding to the first biomass combustion based on the ignition monitoring data and pollutant diffusion model corresponding to the first biomass combustion; the pollutant diffusion simulation data includes the diffusion location of the pollutants and the diffusion time corresponding to each diffusion location;

[0035] The prediction module is used to predict pollutant parameter information corresponding to each diffusion location at any given time based on the diffusion location, diffusion time, and pollutant prediction model.

[0036] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for predicting pollutant parameter information as described in the first aspect.

[0037] Fourthly, embodiments of the present invention also provide a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for predicting pollutant parameter information as described in the first aspect.

[0038] Fifthly, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the method for predicting pollutant parameter information as described in the first aspect.

[0039] The method and apparatus for predicting atmospheric pollutant parameters from biomass combustion provided in this invention acquire ignition monitoring data corresponding to biomass combustion and determine the diffusion location of pollutants and the diffusion time corresponding to each diffusion location based on a pollutant diffusion model. Since the pollutant prediction model can predict the changes in pollutant parameters with diffusion time, by inputting the diffusion location of pollutants and the diffusion time corresponding to each diffusion location into the pollutant prediction model, the pollutant prediction model can accurately predict the atmospheric pollutant parameters from biomass combustion at any given time based on the diffusion time of the pollutants. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating the method for predicting atmospheric pollutant parameters from biomass combustion provided in this embodiment of the invention.

[0042] Figure 2 This is another flowchart illustrating the method for predicting atmospheric pollutant parameters from biomass combustion provided in this embodiment of the invention.

[0043] Figure 3 This is a schematic diagram of the structure of the biomass combustion air pollutant parameter information prediction device provided in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0046] The method of this invention can be applied to environmental monitoring scenarios to predict atmospheric pollutant parameters from biomass combustion.

[0047] In related technologies, there is a lack of effective prediction methods for the transport and diffusion of pollutants generated from biomass combustion. Therefore, how to accurately and effectively predict the transport and diffusion of pollutants is a problem that urgently needs to be solved by those skilled in the art.

[0048] The method for predicting atmospheric pollutant parameters from biomass combustion according to this invention obtains ignition monitoring data corresponding to biomass combustion and determines the diffusion location of pollutants and the diffusion time corresponding to each diffusion location based on a pollutant diffusion model. Since the pollutant prediction model can predict the changes in pollutant parameters with diffusion time, by inputting the diffusion location of pollutants and the diffusion time corresponding to each diffusion location into the pollutant prediction model, the pollutant prediction model can accurately predict the atmospheric pollutant parameters from biomass combustion at any given time based on the diffusion time of the pollutants.

[0049] The following is combined with Figures 1-4 The technical solution of the present invention will be described in detail with reference to specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0050] Figure 1 This is a flowchart illustrating an embodiment of the method for predicting atmospheric pollutant parameters from biomass combustion provided by this invention. Figure 1 As shown, the method provided in this embodiment includes:

[0051] Step 101: Obtain the ignition monitoring data corresponding to the first biomass combustion;

[0052] Specifically, with the continuous increase of on-orbit remote sensing sensors, satellite remote sensing can conduct large-scale repeated observations of the Earth system with a certain temporal and spatial resolution. In this embodiment of the invention, the infrared band of the remote sensing sensor is used to monitor the thermal anomaly information of the Earth's surface, thereby obtaining information related to the location and energy of fire points related to biomass combustion, thus achieving the purpose of obtaining fire point monitoring data through satellite remote sensing.

[0053] Step 102: Based on the ignition monitoring data and pollutant diffusion model corresponding to the first biomass combustion, obtain the pollutant diffusion simulation data corresponding to the first biomass combustion; the pollutant diffusion simulation data includes the diffusion location of pollutants and the diffusion time corresponding to each diffusion location;

[0054] Specifically, by inputting real-time data from satellite fire point monitoring into the pollutant diffusion model, the diffusion mode can be used to simulate the aging and transmission range of pollutants, that is, to simulate the diffusion location of pollutants and the diffusion time corresponding to each diffusion location. Optionally, the present invention does not limit the pollutant diffusion model, and the simulation time is generally set to 120 hours.

[0055] Step 103: Based on the diffusion location, diffusion time, and pollutant prediction model, predict the pollutant parameter information corresponding to each diffusion location at any given time.

[0056] Specifically, the pollutant prediction model is used to predict how the parameters of pollutants generated by biomass combustion change with aging time. Aging time refers to the duration of diffusion of pollutants into the atmosphere. As aging time increases, the parameters of the pollutants also change; for example, the optical thickness of the pollutants changes with aging time. In this embodiment of the invention, real-time data from satellite fire point monitoring is input into the pollutant diffusion model to obtain the diffusion range of pollutants generated by biomass combustion. This involves obtaining the diffusion locations of the pollutants and the corresponding diffusion times at each location, and then inputting this data into the pollutant prediction model. This allows for the prediction of the atmospheric pollutant parameters at each diffusion location at any given time. In other words, based on the input data of the pollutant diffusion locations and times, the pollutant prediction model can accurately predict the parameters of pollutants at any location and time. Furthermore, based on these parameters, the characteristics of downstream pollutant transport areas can be predicted. Optionally, the prediction content includes parameters such as atmospheric visibility, PM2.5 concentration, PM10 concentration, and regional climate. The prediction time is generally set to 72 hours.

[0057] The method described in the above embodiment acquires ignition monitoring data corresponding to biomass combustion and determines the diffusion location of pollutants and the diffusion time corresponding to each diffusion location based on the pollutant diffusion model. Since the pollutant prediction model can predict the changes in pollutant parameters over diffusion time, by inputting the pollutant diffusion location and the diffusion time corresponding to each diffusion location into the pollutant prediction model, the pollutant prediction model can accurately predict the biomass combustion atmospheric pollutant parameters corresponding to each diffusion location at any given time based on the pollutant diffusion time.

[0058] In one embodiment, the method for predicting pollutant parameter information further includes:

[0059] Acquire training data, which includes pollutant parameter information for each time point corresponding to the second biomass combustion;

[0060] The pollutant parameter information at various times is aggregated and fitted to obtain a pollutant prediction model.

[0061] Specifically, the pollutant prediction model can predict how pollutant parameters change over diffusion time (aging time). This model is trained using training data, which includes biomass combustion air pollutant parameters at various times corresponding to biomass combustion. In other words, the biomass combustion air pollutant parameters at different diffusion times are used as training data for the model. Further aggregation and fitting of these parameters yields the pollutant prediction model. Optionally, the biomass combustion air pollutant parameters at different diffusion times are segmented according to time, and then the model is applied to each segment. The median value of pollutant parameters is selected as the value of biomass combustion air pollutant parameters for that time period. Finally, a fitting method is used to fit the changes in biomass combustion air pollutant parameters with diffusion time for each time period. Optionally, linear fitting is selected. If the goodness of fit is less than 0.8, polynomial fitting is used, with the polynomial degree increasing until the goodness of fit is greater than or equal to 0.8. This achieves the construction of a pollutant prediction model through clustering and fitting. Then, based on the pollutant prediction model, the biomass combustion air pollutant parameters corresponding to each diffusion location at any time can be accurately predicted.

[0062] The method described in the above embodiment obtains the biomass combustion air pollutant parameter information at each time corresponding to biomass combustion, and aggregates and fits the biomass combustion air pollutant parameter information at each time. In other words, it trains the pollutant prediction model based on a large amount of processed biomass combustion air pollutant parameter information, so that the trained pollutant prediction model can accurately predict the biomass combustion air pollutant parameter information.

[0063] In one embodiment, obtaining pollutant parameter information for each moment corresponding to the second biomass combustion in the training data includes:

[0064] Acquire monitoring data of pollutants generated by the second biomass combustion and the forward trajectory of pollutant emissions corresponding to the second biomass combustion;

[0065] The monitoring data of pollutants generated by the second biomass combustion are matched with the forward trajectory of pollutant emissions to determine the matching results;

[0066] Based on the matching results, obtain pollutant parameter information for each time point.

[0067] Specifically, in order to improve the prediction accuracy of the pollutant prediction model, this application embodiment uses the pollutant parameter information at each moment corresponding to the combustion of products in the actual environment as the training data of the pollutant prediction model.

[0068] Optionally, monitoring data on pollutants generated from the second biomass combustion are obtained, including:

[0069] Obtain pre-obtained monitoring data on pollutants;

[0070] Based on the monitoring data of pollutants, the optical thickness threshold of pollutants, and the turbidity threshold of pollutants, the monitoring data of pollutants generated by the second biomass combustion are obtained.

[0071] Obtain the forward trajectory of pollutant emissions, including:

[0072] Obtain ignition monitoring data corresponding to the second biomass combustion;

[0073] Based on fire point monitoring data and pollutant transport models, the forward trajectory of pollutant emissions is obtained.

[0074] Specifically, satellite monitoring data of pollutants can be acquired, such as satellite monitoring data of aerosols. This data can then be filtered according to aerosol optical thickness (OPS). Generally, the OPS threshold is set to 0.5. Data with OPS values ​​less than this threshold are considered to have insufficient accuracy in remote sensing inversion data, and data that does not meet the OPS parameter requirements are discarded. Alternatively, the satellite monitoring data can be further filtered based on the Escullant index. Generally, the Escullant index threshold is set to 1.4. Data with an Escullant index less than this threshold are considered not to belong to the biomass combustion-based aerosol type, and data that does not meet the Escullant index requirements are discarded. This process yields monitoring data for pollutants generated by biomass combustion, making the training data for the pollutant prediction model more accurate.

[0075] Furthermore, satellite fire point monitoring data corresponding to biomass combustion can also be obtained, and the satellite fire point monitoring data can be input into the pollutant transport model to construct the forward trajectory of pollutant emissions from biomass combustion; optionally, the pollutant transport model can be a commonly used transport model, such as the HYSPLIT model, and this embodiment is not limited to it.

[0076] To accurately obtain training data for the pollutant prediction model and improve its accuracy, this embodiment processes satellite monitoring data to obtain monitoring data of pollutants generated by biomass combustion. It then uses satellite fire point monitoring data corresponding to biomass combustion to obtain the forward trajectory of pollutant emissions. The pollutant monitoring data and the forward trajectory are matched, meaning that the parameter information of pollutants generated by biomass combustion at various times is verified, matched, and obtained from two data dimensions: satellite monitoring data and satellite fire point monitoring data. This makes the determined atmospheric pollutant parameter information at each time point more accurate, thus making the training data for the pollutant prediction model more accurate, and consequently, the prediction results more accurate.

[0077] The method described in the above embodiments acquires pollutant monitoring data and satellite fire point monitoring data corresponding to biomass combustion, and matches the pollutant monitoring data with the pollutant emission forward trajectory obtained from the satellite fire point monitoring data. That is, it verifies, matches and acquires the parameter information of pollutants generated by biomass combustion at each time point from two data dimensions: satellite monitoring data of pollutants and satellite fire point monitoring data corresponding to biomass combustion. This makes the determined atmospheric pollutant parameter information of biomass combustion at each time point more accurate, which in turn makes the training data of the pollutant prediction model more accurate, and thus makes the prediction results of the pollutant prediction model more accurate.

[0078] In one embodiment, the pollutant monitoring data includes at least one of the following: the monitoring location of the pollutant and the monitoring time of the pollutant; matching the monitoring data of the pollutants generated by the second biomass combustion with the pollutant emission forward trajectory to obtain the matching result includes:

[0079] If the time difference between the pollutant monitoring time and the target trajectory point in the pollutant emission forward trajectory is less than the first threshold, and the distance between the pollutant monitoring location and the target trajectory point is less than the second threshold, the matching result is determined to be a successful match; the target trajectory point is any trajectory point in the forward trajectory.

[0080] Specifically, in this embodiment, the parameter information of pollutants generated by biomass combustion at various times is verified, matched, and acquired from two data dimensions: satellite monitoring data of pollutants and satellite fire point monitoring data corresponding to biomass combustion. This is to obtain the atmospheric pollutant parameter information of biomass combustion at various times and use it as training data for the pollutant prediction model. Optionally, the monitoring data of pollutants generated by biomass combustion can be matched with the pollutant emission trajectory. That is, the satellite monitoring data of pollutants after screening is matched with the pollutant emission trajectory corresponding to biomass combustion in time and space. The matching criteria are generally set as follows: distance less than 50km and time difference less than 15 minutes. For example, if the distance between the latitude and longitude location information of pollutant monitoring data a and the latitude and longitude location of trajectory point A in the pollutant emission trajectory is less than 50km, and the time difference between the acquisition time of pollutant monitoring data a and the time difference between the acquisition time of pollutant monitoring data a and the trajectory point A in the pollutant emission trajectory is less than 15 minutes, then the matching result is considered successful. In this case, the atmospheric pollutant parameter information of biomass combustion at that time can be used as training data for the pollutant prediction model, thereby making the prediction results of the prediction model more accurate.

[0081] Optionally, if the matching result is successful, pollutant parameter information at each time point is obtained based on the matching result, including:

[0082] If the monitoring data of pollutants and at least one target trajectory point in the pollutant emission forward trajectory are successfully matched, the position of the target trajectory point closest to the fire point in the pollutant emission forward trajectory is determined as the target position, and the time corresponding to the target position is determined as the target time.

[0083] Based on the target time and pollutant monitoring data, obtain pollutant parameter information for each time point.

[0084] Specifically, in order to accurately obtain training data for the pollutant prediction model and make the prediction results of the pollutant prediction model more accurate, in this embodiment, the satellite monitoring data of the filtered pollutants are matched with the pollutant emission forward trajectory corresponding to biomass combustion in time and space. If the monitoring data of the pollutants and at least one target trajectory point in the pollutant emission forward trajectory are successfully matched, the position of the target trajectory point closest to the fire point in the pollutant emission forward trajectory is determined as the target position, and the time corresponding to the target position is determined as the target time. Then, the biomass combustion atmospheric pollutant parameter information corresponding to the target time can be used as training samples to train the pollutant prediction model.

[0085] In the method described above, when the monitoring data of pollutants and at least one target trajectory point in the pollutant emission forward trajectory are successfully matched, the position of the target trajectory point closest to the ignition point in the pollutant emission forward trajectory is determined as the target position, and the time corresponding to the target position is determined as the target time. Then, the biomass combustion atmospheric pollutant parameter information corresponding to the target time can be used as a training sample to train the pollutant prediction model, thereby obtaining more accurate training data and making the prediction results of the pollutant prediction model more accurate.

[0086] Optionally, based on fire point monitoring data and pollutant transport models, the forward trajectory of pollutant emissions is obtained, including:

[0087] When multiple pollutant emission trajectories are obtained based on multiple fire point monitoring data and there are overlapping areas among the pollutant emission trajectories, the fire point radiation energy value corresponding to each pollutant emission trajectories is determined.

[0088] The pollutant emission forward trajectory corresponding to the largest fire point radiation energy value among all fire point radiation energy values ​​is taken as the obtained pollutant emission forward trajectory.

[0089] Specifically, when multiple pollutant emission forward trajectories are obtained based on multiple fire point monitoring data, if there is an overlapping area among the multiple pollutant emission forward trajectories, the forward trajectory of the point with the largest radiation energy value corresponding to the fire point is selected as the obtained pollutant emission forward trajectory.

[0090] The method described above, when multiple pollutant emission forward trajectories overlap, uses the pollutant emission forward trajectory corresponding to the maximum fire point radiation energy value as the obtained pollutant emission forward trajectory. This allows for the matching of the pollutant emission forward trajectory corresponding to the maximum fire point radiation energy value with satellite monitoring data of the pollutants, quickly and accurately obtaining the matching results and pollutant parameter information at each time point.

[0091] Optionally, the pollutant parameter information includes at least one of the following:

[0092] Pollutant optical thickness, Escull index, single scattering albedo, asymmetry factor, particle spectral distribution, complex refractive index, percentage of black carbon, radiative forcing, and radiative forcing efficiency.

[0093] Specifically, in this embodiment, the atmospheric pollutant parameters of biomass combustion at various diffusion locations at any given time are predicted using a prediction model. These parameters include pollutant optical thickness, Escull index, single-scatter albedo, asymmetry factor, particle spectral distribution, complex refractive index, black carbon percentage, radiative forcing, and radiative forcing efficiency. Radiative forcing refers to the net change in radiation vertically to the tropopause caused by external forcing, such as changes in internal climate system variations (e.g., changes in carbon dioxide concentration or solar radiation). The unit is watts per square meter. Radiative forcing is a measure of the degree to which a factor alters the energy balance of the Earth's atmosphere, and it is also an index reflecting the importance of that factor in potential climate change mechanisms. Aerosol radiative forcing efficiency refers to the direct radiative forcing generated per unit aerosol optical thickness at a wavelength of 500 nm. It is a comprehensive expression of aerosol radiative forcing. Optionally, the radiative forcing can be linearly fitted to the optical thickness, and the slope is the aerosol radiative forcing efficiency.

[0094] For example, such as Figure 2 The flowchart of the method for predicting atmospheric pollutant parameters from biomass combustion is as follows:

[0095] 1. Acquire long-term aerosol satellite monitoring data (aerosol satellite remote sensing products) and fire point satellite monitoring data (fire point satellite remote sensing products) corresponding to biomass combustion, and select a suitable pollutant transport and diffusion model (e.g., the open-source HYSPLIT model can be selected);

[0096] 2. Set a threshold and filter aerosol satellite monitoring data according to aerosol optical thickness (AOD). Generally, the AOD threshold is set to 0.5. When the AOD value is less than the specified threshold, the aerosol satellite monitoring data is considered to have low inversion accuracy and is discarded.

[0097] 3. Set a threshold and filter aerosol satellite monitoring data according to the Escullant index. Generally, the Escullant index threshold is set to 1.4. When the Escullant index is less than the specified threshold, it is considered that it does not belong to the aerosol type mainly caused by biomass combustion and is discarded.

[0098] 4. Incorporate satellite monitoring data of fire points corresponding to biomass combustion into the pollutant transport model to construct the forward trajectory of pollutant emissions from biomass combustion.

[0099] 5. Match the screened aerosol satellite monitoring data with the constructed forward trajectory of biomass combustion pollutant emissions in time and space. The matching criteria are generally set as follows: distance less than 50km, time difference less than 15 minutes, to determine the aging time.

[0100] 6. Determine the aging time for the matched aerosol product data. If only one forward trajectory is matched, set the transmission time corresponding to the nearest matching point on that forward trajectory as its aging time (target time). If multiple forward trajectories are matched, first filter based on the radiant energy value (FRP) of the fire point corresponding to the forward trajectory, select the forward trajectory with the largest FRP as the optimal matching forward trajectory, and then return to the case of matching only one forward trajectory to determine its aging time.

[0101] 7. Extract biomass combustion aerosol parameter information from aerosol products after a certain aging time, segmented by time period. Specific parameter information includes: aerosol optical thickness, Escull index, single scattering albedo, asymmetry factor, particle spectral distribution, complex refractive index, percentage of black carbon, radiative forcing, and radiative forcing efficiency.

[0102] 8. Simulation and Prediction of Aerosol Aging and Transport Processes in Biomass Combustion

[0103] A pollutant prediction model (biomass combustion aerosol aging model) is constructed using clustering (k-means method) and fitting (polynomial fitting) methods.

[0104] For the segmented extraction of biomass combustion aerosol parameters, the median value of each parameter in each segment is selected as the parameter value for that segment. A fitting method is used between segments to fit the changes in each parameter over aging time. Linear fitting is preferred; if the goodness of fit is less than 0.8, polynomial fitting is used, with the polynomial degree increasing incrementally until the goodness of fit is greater than or equal to 0.8. This leads to the construction of a pollutant prediction model (biomass combustion aerosol aging model) to describe the changes in biomass combustion aerosol parameters (including aerosol optical thickness, Escull index, single scattering albedo, asymmetry factor, particle spectral distribution, complex refractive index, percentage of black carbon, radiative forcing, and radiative forcing efficiency) over aging time.

[0105] 9. Incorporate real-time satellite monitoring data of the fire point corresponding to biomass combustion into the pollutant diffusion model, and use the diffusion mode to simulate the aging and transmission range of biomass combustion aerosols. The simulation time is generally set to 120 hours.

[0106] 10. The pollutant prediction model (biomass combustion aerosol aging model) is incorporated into the simulation results of the biomass combustion aerosol aging transport range (pollutant diffusion model) to predict the characteristics of downstream transport areas of biomass combustion aerosols. Specific predictions include parameters such as atmospheric visibility, PM2.5 concentration, PM10 concentration, and regional climate. The prediction time is generally set to 72 hours. This embodiment extracts biomass combustion aerosol information from aerosol satellite remote sensing products and accurately simulates and predicts the aging transport process of biomass combustion aerosols.

[0107] The following describes the device for predicting atmospheric pollutant parameters from biomass combustion provided by the present invention. The device for predicting atmospheric pollutant parameters from biomass combustion described below can be referred to in correspondence with the method for predicting atmospheric pollutant parameters from biomass combustion described above.

[0108] Figure 3 This is a schematic diagram of the structure of the pollutant parameter information prediction device provided by the present invention. The pollutant parameter information prediction device provided in this embodiment includes:

[0109] The acquisition module 710 is used to acquire the ignition monitoring data corresponding to the first biomass combustion.

[0110] The processing module 720 is used to obtain pollutant diffusion simulation data corresponding to the first biomass combustion based on the ignition monitoring data and pollutant diffusion model corresponding to the first biomass combustion; the pollutant diffusion simulation data includes the diffusion location of pollutants and the diffusion time corresponding to each diffusion location;

[0111] The prediction module 730 is used to predict pollutant parameter information corresponding to each diffusion location at any given time based on the diffusion location, diffusion time, and pollutant prediction model.

[0112] Optionally, the processing module 720 is specifically used to: acquire training data, which includes pollutant parameter information at each moment corresponding to the second biomass combustion;

[0113] The pollutant parameter information at various times is aggregated and fitted to obtain a pollutant prediction model.

[0114] Optionally, the processing module 720 is specifically used to: acquire monitoring data of pollutants generated by the second biomass combustion and the forward trajectory of pollutant emissions corresponding to the second biomass combustion;

[0115] The monitoring data of pollutants generated by the second biomass combustion are matched with the forward trajectory of pollutant emissions to determine the matching results;

[0116] Based on the matching results, obtain pollutant parameter information for each time point.

[0117] Optionally, the processing module 720 is specifically used to: determine the matching result as a successful match if the time difference between the monitoring time of the pollutant and the target trajectory point in the pollutant emission forward trajectory is less than a first threshold, and the distance between the monitoring location of the pollutant and the target trajectory point is less than a second threshold; the target trajectory point is any trajectory point in the forward trajectory.

[0118] Optionally, the processing module 720 is specifically used to: when the monitoring data of pollutants and at least one target trajectory point in the pollutant emission forward trajectory are successfully matched, determine the position of the target trajectory point in the pollutant emission forward trajectory that is closest to the fire point as the target position, and determine the time corresponding to the target position as the target time.

[0119] Based on the target time and pollutant monitoring data, obtain pollutant parameter information for each time point.

[0120] Optionally, the processing module 720 is specifically used to: when the monitoring data of pollutants and at least one target trajectory point in the pollutant emission forward trajectory are successfully matched, determine the position of the target trajectory point in the pollutant emission forward trajectory that is closest to the fire point as the target position, and determine the time corresponding to the target position as the target time.

[0121] Based on the target time and pollutant monitoring data, obtain pollutant parameter information for each time point.

[0122] Optionally, the processing module 720 is specifically used to: determine the fire point radiation energy value corresponding to each pollutant emission trajectory when multiple pollutant emission forward trajectories are obtained based on multiple fire point monitoring data and there is an overlapping area between each pollutant emission forward trajectories;

[0123] The pollutant emission forward trajectory corresponding to the largest fire point radiation energy value among all fire point radiation energy values ​​is taken as the obtained pollutant emission forward trajectory.

[0124] Optionally, the pollutant parameter information includes at least one of the following:

[0125] Pollutant optical thickness, Escull index, single scattering albedo, asymmetry factor, particle spectral distribution, complex refractive index, percentage of black carbon, radiative forcing, and radiative forcing efficiency.

[0126] The apparatus of this invention is used to execute the method in any of the foregoing method embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0127] Figure 4A schematic diagram of the physical structure of an electronic device is provided. This electronic device may include a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, communication interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a method for predicting pollutant parameter information. This method includes: acquiring ignition point monitoring data corresponding to a first biomass combustion; acquiring pollutant diffusion simulation data corresponding to the first biomass combustion based on the ignition point monitoring data and a pollutant diffusion model; the pollutant diffusion simulation data includes the diffusion location of pollutants and the diffusion time corresponding to each diffusion location; and predicting the pollutant parameter information corresponding to each diffusion location at any given time based on the pollutant diffusion location, diffusion time, and pollutant prediction model.

[0128] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0129] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the prediction method for biomass combustion atmospheric pollutant parameter information provided by the above methods, the method comprising: acquiring ignition monitoring data corresponding to a first biomass combustion; acquiring pollutant diffusion simulation data corresponding to the first biomass combustion based on the ignition monitoring data corresponding to the first biomass combustion and a pollutant diffusion model; the pollutant diffusion simulation data including the diffusion location of pollutants and the diffusion time corresponding to each diffusion location; and predicting pollutant parameter information corresponding to each diffusion location at any given time based on the diffusion location of pollutants, the diffusion time, and the pollutant prediction model.

[0130] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the above-described methods for predicting atmospheric pollutant parameters from biomass combustion. The method includes: acquiring ignition monitoring data corresponding to a first biomass combustion; acquiring pollutant diffusion simulation data corresponding to the first biomass combustion based on the ignition monitoring data and a pollutant diffusion model; the pollutant diffusion simulation data includes the diffusion location of pollutants and the diffusion time corresponding to each diffusion location; and predicting pollutant parameter information corresponding to each diffusion location at any given time based on the diffusion location, diffusion time, and pollutant prediction model.

[0131] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0132] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting atmospheric pollutant parameters from biomass combustion, characterized in that, include: Obtain the ignition point monitoring data corresponding to the first biomass combustion; Based on the ignition monitoring data and pollutant diffusion model corresponding to the first biomass combustion, pollutant diffusion simulation data corresponding to the first biomass combustion is obtained; the pollutant diffusion simulation data includes the diffusion location of the pollutants and the diffusion time corresponding to each diffusion location; Based on the diffusion location, diffusion time, and pollutant prediction model, predict the pollutant parameter information corresponding to each diffusion location at any given time. The method further includes: Acquire training data, which includes pollutant parameter information for each moment corresponding to the second biomass combustion; The pollutant parameter information at each time point is aggregated and fitted to obtain the pollutant prediction model; The step of obtaining pollutant parameter information for each moment corresponding to the second biomass combustion in the training data includes: Acquire monitoring data of pollutants generated by the second biomass combustion and the forward trajectory of pollutant emissions corresponding to the second biomass combustion; The monitoring data of pollutants generated by the second biomass combustion are matched with the forward trajectory of the pollutant emissions to determine the matching result; Based on the matching results, obtain pollutant parameter information at each time point; The acquisition of monitoring data on pollutants generated from the second biomass combustion includes: Obtain pre-obtained monitoring data on pollutants; Based on the monitoring data of the pollutants, the optical thickness threshold of the pollutants, and the turbidity threshold of the pollutants, the monitoring data of the pollutants generated by the second biomass combustion is obtained; the monitoring data of the pollutants includes: the monitoring location of the pollutants and the monitoring time of the pollutants; Obtaining the forward trajectory of the pollutant emission includes: Obtain the ignition point monitoring data corresponding to the second biomass combustion; Based on the fire point monitoring data and pollutant transport model, the forward trajectory of pollutant emission is obtained; The step of obtaining the forward trajectory of pollutant emissions based on the fire point monitoring data and the pollutant transport model includes: When multiple pollutant emission forward trajectories are obtained based on multiple fire point monitoring data and there is an overlapping area among the pollutant emission forward trajectories, the fire point radiation energy value corresponding to each pollutant emission forward trajectory is determined. The pollutant emission forward trajectory corresponding to the largest fire point radiation energy value among all the fire point radiation energy values ​​is taken as the obtained pollutant emission forward trajectory. The pollutant parameter information includes at least one of the following: Pollutant optical thickness, Escull index, single scattering albedo, asymmetry factor, particle spectral distribution, complex refractive index, percentage of black carbon, radiative forcing, and radiative forcing efficiency.

2. The method for predicting atmospheric pollutant parameters from biomass combustion according to claim 1, characterized in that, The step of matching the monitoring data of pollutants generated by the second biomass combustion with the forward trajectory of pollutant emissions to obtain the matching result includes: If the time difference between the monitoring time of the pollutant and the target trajectory point in the pollutant emission forward trajectory is less than a first threshold, and the distance between the monitoring location of the pollutant and the target trajectory point is less than a second threshold, the matching result is determined to be a successful match; the target trajectory point is any trajectory point in the forward trajectory.

3. The method for predicting atmospheric pollutant parameters from biomass combustion according to claim 2, characterized in that, If the matching result is successful, the step of obtaining pollutant parameter information at each time point based on the matching result includes: If at least one target trajectory point in the pollutant monitoring data and the pollutant emission forward trajectory is successfully matched, the position of the target trajectory point closest to the fire point in the pollutant emission forward trajectory is determined as the target position, and the time corresponding to the target position is determined as the target time. Based on the target time and the monitoring data of the pollutants, obtain pollutant parameter information at each time point.

4. A device for predicting atmospheric pollutant parameters from biomass combustion, characterized in that, include: The acquisition module is used to acquire the ignition monitoring data corresponding to the first biomass combustion. The processing module is used to obtain pollutant diffusion simulation data corresponding to the first biomass combustion based on the ignition monitoring data and pollutant diffusion model corresponding to the first biomass combustion; the pollutant diffusion simulation data includes the diffusion location of the pollutants and the diffusion time corresponding to each diffusion location; The prediction module is used to predict pollutant parameter information corresponding to each diffusion location at any given time based on the diffusion location, diffusion time, and pollutant prediction model. The prediction module is also used to: acquire training data, which includes pollutant parameter information corresponding to each time of the second biomass combustion; aggregate and fit the pollutant parameter information at each time to obtain the pollutant prediction model. The step of obtaining pollutant parameter information for each moment corresponding to the second biomass combustion in the training data includes: obtaining monitoring data of pollutants generated by the second biomass combustion and the pollutant emission forward trajectory corresponding to the second biomass combustion; matching the monitoring data of pollutants generated by the second biomass combustion and the pollutant emission forward trajectory to determine the matching result; and obtaining pollutant parameter information for each moment based on the matching result. The acquisition of monitoring data on pollutants generated from the second biomass combustion includes: Acquire pre-obtained pollutant monitoring data; based on the pollutant monitoring data, pollutant optical thickness threshold, and pollutant turbidity threshold, acquire monitoring data of pollutants generated by the second biomass combustion; the pollutant monitoring data includes: the monitoring location of the pollutant and the monitoring time of the pollutant; The acquisition of the pollutant emission forward trajectory includes: Acquire ignition monitoring data corresponding to the second biomass combustion; based on the ignition monitoring data and the pollutant transport model, obtain the forward trajectory of the pollutant emission; The step of obtaining the pollutant emission forward trajectory based on the fire point monitoring data and the pollutant transport model includes: when multiple pollutant emission forward trajectories are obtained based on multiple fire point monitoring data and the various pollutant emission forward trajectories have overlapping areas, determining the fire point radiation energy value corresponding to each pollutant emission forward trajectory; and taking the pollutant emission forward trajectory corresponding to the fire point radiation energy value with the largest fire point radiation energy value as the obtained pollutant emission forward trajectory. The pollutant parameter information includes at least one of the following: Pollutant optical thickness, Escull index, single scattering albedo, asymmetry factor, particle spectral distribution, complex refractive index, percentage of black carbon, radiative forcing, and radiative forcing efficiency.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for predicting atmospheric pollutant parameter information from biomass combustion as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for predicting atmospheric pollutant parameter information from biomass combustion as described in any one of claims 1 to 3.

7. A computer program product having executable instructions stored thereon, characterized in that, When executed by the processor, this instruction enables the processor to implement the method for predicting atmospheric pollutant parameter information from biomass combustion as described in any one of claims 1 to 3.

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