Forest fire monitoring method based on particulate matter traceability technology

Abstract

The invention discloses a forest fire monitoring method based on a particulate matter traceability technology, and belongs to the technical field of forest fire monitoring and protection. The method comprises the following steps: collecting real-time particulate matter component monitoring data and meteorological data; based on the particulate matter component data, obtaining a receptor model method source analysis result of each monitoring station, and judging whether a main contribution emission source causing pollution is a forest fire or not; obtaining backward trajectory data; based on the backward trajectory data and the particulate matter concentration data, a high-precision particulate matter traceability calculation model is established, and position distribution of potential forest fire sources is obtained; according to the invention, characteristics of particulate matters generated by biomass combustion are fully utilized, real-time traceability positioning is carried out on an emission source based on a particulate matter traceability technology, real-time monitoring and protection of forest fires can be effectively realized, occurrence of the fires can be rapidly identified, the fire source can be positioned in time, and the forest fire can be protected. Valuable time is provided for a fire department to deal with and put out fire, and the efficiency and accuracy of forest fire prevention are remarkably improved.

Description

Technical Field

[0001] The invention relates to a forest fire monitoring method based on particle tracing technology, belonging to the technical field of forest fire monitoring and protection. Background Art

[0002] Forest fires are a serious natural disaster that can cause huge losses and damage to the environment, economy and society. Fires not only destroy large tracts of forests, leading to a reduction in biodiversity, but also release large amounts of inhalable particulate matter and carbon dioxide, causing serious air pollution and exacerbating global warming. In addition, forest fires have dealt a heavy blow to the local economy, affecting the forestry, tourism and quality of life of residents, and even threatening human life.

[0003] At present, most forest fire monitoring methods rely mainly on on-site observation and manual control. These traditional methods include manual patrols, drone aerial photography, and the use of fire observation towers and monitoring equipment. However, the limitations of these methods are obvious: manual patrols consume a lot of manpower and material resources and are difficult to cover vast forest areas; although fire observation towers and monitoring equipment can provide certain monitoring, they are limited by geographical conditions and the number of equipment, and cannot achieve all-round and all-weather monitoring. In addition, these methods lack real-time performance and can usually only be discovered and measures taken after the fire occurs, delaying the best time to extinguish the fire and increasing the risk of fire spread. Therefore, there is an urgent need for a method that can monitor forest fires in real time to improve the efficiency and accuracy of forest fire prevention. Summary of the invention

[0004] In order to improve the real-time and accuracy of forest fire monitoring and the efficiency of forest fire prevention, the present invention provides a forest fire monitoring method based on particle tracing technology.

[0005] The essence of forest fires is the burning of a large number of plant and animal carcasses, which will produce a large amount of particulate matter emissions. In the classification of particulate matter emission sources, this emission belongs to biomass combustion sources. The emitted particulate matter will spread rapidly under favorable meteorological conditions, and the diffusion speed is much faster than the spread speed of forest fires.

[0006] Therefore, if particulate emission monitoring stations are set up in forest areas and their surrounding areas, real-time monitoring of particulate emission data can be achieved. At the same time, using particulate source tracing technology to trace and locate emission sources in real time can effectively achieve real-time monitoring and protection of forest fires. This method can not only quickly identify the occurrence of fires, but also locate the source of fire in time, providing fire departments with valuable time to respond and extinguish fires, significantly improving the efficiency and accuracy of forest fire prevention.

[0007] The forest fire monitoring method based on particle tracing technology of the present invention comprises:

[0008] Step 1: Obtain particulate matter component monitoring data in real time through the particulate matter component monitoring station;

[0009] Step 2: Based on global meteorological reanalysis data and observation data from meteorological monitoring stations, an automated forecasting system for meteorological data above forest areas is constructed to obtain real-time meteorological data;

[0010] Step 3: Analyze the monitoring data of the particulate matter components to determine whether the main contributing emission source to the particulate matter pollution at the monitoring station is the biomass combustion source. If so, it indicates that a forest fire has occurred, and the next step of tracing the source is carried out;

[0011] Step 4: Based on the particulate matter component monitoring data obtained in step 1 and the meteorological data obtained in step 2, the backward trajectory of the pollution source is tracked using the HySPLIT software to obtain refined backward trajectory data;

[0012] Step 5: Based on the particulate matter component monitoring data obtained in step 1 and the refined backward trajectory data obtained in step 4, a variety of reverse calculation models are used to trace the particulate matter to obtain the location distribution data of potential forest fire sources; the reverse calculation models include: PSCF algorithm, CWT algorithm, CFA algorithm, RTWC algorithm, and SQTBA algorithm.

[0013] Optionally, in the multiple reverse calculation models, the empirical weight function W of the grid (i, j) i,j The value standards are:

[0014]

[0015] Among them, n ave is the average number of trajectory endpoints in the grid, n i,j is the number of all trajectory endpoints passing through the grid (i, j).

[0016] Optionally, step 2 includes:

[0017] Step 21: Collect publicly available global meteorological reanalysis data and observation data from meteorological monitoring stations;

[0018] Step 22: Use the WRF numerical forecast model, take the meteorological reanalysis data as the initial data, use the WRFDA data assimilation model, and adopt the 3DVAR assimilation method to assimilate the observation data of meteorological monitoring stations in real time, establish a high-precision meteorological data automatic forecast system covering the forest area, and collect high-precision real-time meteorological data over the forest area.

[0019] Optionally, step 3 includes:

[0020] Step 31: Obtain the particulate matter component monitoring data;

[0021] Step 32: performing source analysis on the particulate matter component monitoring data using a receptor model;

[0022] Step 33: Comprehensively analyze the source analysis results of each receptor model to determine whether the main emission source of particulate matter at the monitoring site is biomass combustion. If so, it indicates that a forest fire has occurred and further source tracing is required; if not, it indicates that no forest fire has occurred and no source tracing is required.

[0023] Optionally, step 4 includes:

[0024] Step 41: According to the latitude and longitude information of the particulate matter component monitoring station and the time period of particulate matter emission, the meteorological data automatic forecasting system established in step 2 is used to obtain high-precision real-time meteorological data for the required area and time period;

[0025] Step 42: Use the HySPLIT model to establish a high-precision backward trajectory tracking system, use the location of the particulate matter component monitoring station as the trajectory starting point, perform backward trajectory tracking, and obtain refined backward trajectory data of particulate matter emissions.

[0026] Optionally, the method further includes: utilizing drone aerial photography to make final confirmation of the location of the fire source.

[0027] Optionally, the receptor model includes: a chemical mass balance model and a positive definite matrix factorization model.

[0028] The present invention provides a forest fire monitoring system based on particle tracing technology, which is used to implement the forest fire monitoring method based on particle tracing technology as described in any one of the above items, and the system includes:

[0029] A particle component data acquisition module is configured to acquire particle component monitoring data in real time through a particle component monitoring station;

[0030] A meteorological data acquisition module is configured to construct an automatic forecasting system for meteorological data above the forest area based on global meteorological reanalysis data and observation data from meteorological monitoring stations to obtain real-time meteorological data;

[0031] The judgment module is configured to analyze the particulate matter component monitoring data to determine whether the main contributing emission source causing the particulate matter pollution of the monitoring station is the biomass combustion source. If so, it indicates that a forest fire has occurred, and the next step of tracing the source is performed;

[0032] A backward trajectory data calculation module is configured to perform backward trajectory tracking of pollution sources using HySPLIT software based on the particulate matter component monitoring data and meteorological data to obtain refined backward trajectory data;

[0033] The fire source location determination module is configured to establish a high-precision particulate matter tracing calculation model based on the particulate matter component monitoring data and the refined backward trajectory data, using the PSCF algorithm, CWT algorithm, CFA algorithm, RTWC algorithm, and SQTBA algorithm to obtain the location distribution data of potential forest fire sources.

[0034] The present invention provides a forest fire monitoring device based on particle tracing technology, including a memory and a processor;

[0035] The memory is used to store computer programs;

[0036] The processor is used to implement the forest fire monitoring method based on particle tracing technology as described in any of the above items when executing the computer program.

[0037] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the forest fire monitoring method based on the particle tracing technology as described in any one of the above items is implemented.

[0038] The beneficial effects of the present invention are:

[0039] (1) The present invention uses real-time particulate matter emission data and particulate matter source analysis technology, and can use the characteristic information in the particulate matter monitoring component data to quickly and accurately identify whether the particulate matter emission source is a biomass combustion source, thereby being able to make real-time judgments and trace the occurrence of forest fires, significantly improving the efficiency and accuracy of forest fire prevention.

[0040] (2) In order to solve the problem that a single tracing algorithm cannot fully consider the spatial non-uniformity and localized propagation characteristics of particle propagation, resulting in deviation and inaccuracy in tracing results, the present invention adopts five tracing algorithms, PSCF, CWT, CFA, RTWC and SQTBA, combining different spatial and temporal scales to more comprehensively reproduce the tracing path and propagation process. Through the comprehensive application of multiple algorithms, the deficiencies can be compensated for, the accuracy and stability of the tracing results can be improved, and the errors caused by a single algorithm can be reduced.

[0041] (3) In small-scale tracing, due to the high grid resolution, the average number of endpoints of the trajectories in the grid is small, which leads to a larger value of the grid empirical weight function. Therefore, in the present invention, the method for determining the value of the weight coefficient is improved to make it more suitable for small-scale tracing in forest areas, thereby improving the accuracy of the tracing results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 It is a flow chart of the forest fire monitoring method based on the particle tracing technology of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0045] Embodiment 1:

[0046] This embodiment provides a forest fire monitoring method based on particle tracing technology, characterized in that the method includes:

[0047] Step 1: Obtain particulate matter component monitoring data in real time through the particulate matter component monitoring station;

[0048] Step 2: Based on global meteorological reanalysis data and observation data from meteorological monitoring stations, an automated forecasting system for meteorological data above forest areas is constructed to obtain real-time meteorological data;

[0049] Step 3: Analyze the monitoring data of the particulate matter components to determine whether the main contributing emission source to the particulate matter pollution at the monitoring station is the biomass combustion source. If so, it indicates that a forest fire has occurred, and the next step of tracing the source is carried out;

[0050] Step 4: Based on the particulate matter component monitoring data obtained in step 1 and the meteorological data obtained in step 2, the backward trajectory of the pollution source is tracked using the HySPLIT software to obtain refined backward trajectory data;

[0051] Step 5: Based on the particulate matter component monitoring data obtained in step 1 and the refined backward trajectory data obtained in step 4, a variety of reverse calculation models are used to trace the particulate matter to obtain the location distribution data of potential forest fire sources; the reverse calculation models include: PSCF algorithm, CWT algorithm, CFA algorithm, RTWC algorithm, and SQTBA algorithm.

[0052] Embodiment 2:

[0053] This embodiment provides a forest fire monitoring method based on particle tracing technology. Figure 1 , specifically including the following steps:

[0054] S1: Set up a particulate matter composition monitoring station to collect real-time particulate matter composition monitoring data.

[0055] Establish particulate matter composition monitoring stations in forest areas and their surrounding areas to realize real-time monitoring of particulate matter emission data and provide reliable data support for fire monitoring and tracing.

[0056] When setting up particulate matter component monitoring stations, the specific terrain and climate of the forest area should be taken into consideration, representative locations covering the main wind directions and airflow channels should be selected, and areas with different vegetation types, such as coniferous forests and broad-leaved forests should be considered; particulate matter component monitoring instruments include PM2.5, PM10 and other equipment, as well as sensors for detecting components such as organic carbon and black carbon, and regular calibration of sensors should be ensured to ensure data accuracy; particulate matter concentration data should be collected in real time, and the content of different particulate matter components should be recorded, and the data should be transmitted to a central database for centralized processing and analysis using wireless networks or other transmission technologies; data should be stored in a cloud server or local database to ensure security and accessibility, and the data should be organized, classified and archived through a database management system for subsequent analysis and processing; monitoring equipment should be inspected and maintained regularly to ensure its normal operation and continuity of data collection, while keeping the environment around the monitoring site clean and tidy to prevent external factors from interfering with data accuracy.

[0057] S2: Establish a meteorological data forecast system to collect real-time meteorological data over the forest area, including the following steps:

[0058] S2.1: Data acquisition: Collect publicly available global meteorological reanalysis data and observation data from meteorological monitoring stations. Global meteorological reanalysis data is meteorological data obtained by integrating and reanalyzing a variety of observation data around the world. It integrates data from a variety of observation platforms, including meteorological satellites, ground-based meteorological stations, buoys, and aircraft soundings. It can more comprehensively reflect the meteorological conditions of the earth's atmosphere, oceans, and land, and provide detailed global meteorological field information. Observation data from meteorological monitoring stations refers to routine meteorological data near the ground collected by meteorological monitoring equipment deployed in forest areas and surrounding areas, including temperature, humidity, wind speed, wind direction, etc.

[0059] S2.2: Numerical simulation and data assimilation: Use the WRF numerical forecast model, take meteorological reanalysis data as initial data, use the WRFDA data assimilation model, 3DVAR method, assimilate the observation data of meteorological monitoring stations in real time, and the horizontal resolution of the forecast grid is less than 1km. Establish a regional high-precision meteorological data automatic forecast system to collect high-precision real-time meteorological data over forest areas.

[0060] The WRF model is a numerical weather forecast model used in meteorology and climate research. It has flexible horizontal and vertical grid settings that can adapt to the simulation needs of different geographical regions and meteorological events. WRFDA is the data assimilation system of the WRF model, which is used to fuse observational data with model output to improve the model's description of the atmospheric state. The 3DVAR assimilation method can effectively and significantly improve the model's forecasting capabilities.

[0061] S3: Use the receptor model source analysis calculation to determine whether it is a forest fire. If the result is true, proceed to the next step. Specifically, the following steps are included:

[0062] S3.1: Data access: Obtain the real-time particulate matter monitoring data of the site obtained in step S1.

[0063] S3.2: Receptor model source analysis: Use receptor models such as the chemical mass balance model (CMB) and the positive matrix factorization model (PMF) to perform source analysis calculations and obtain the receptor model source analysis results for each monitoring station. The receptor model method does not need to track the transmission process of particulate matter and does not rely on the emission conditions, topography, meteorology and other data of the emission source. In addition, the receptor model does not need to investigate the emission factors and activity levels of various pollution sources, and directly measures the ambient atmosphere.

[0064] The PMF model is a new analysis method developed on the basis of factor analysis. Through error estimation, it can more reasonably handle missing values ​​and outliers in the data, thereby maximizing the use of data. The CMB model is composed of a set of linear equations. The receptor concentration of each tracer species is equal to the linear sum of the mass fraction of the species in the source component spectrum and the source contribution concentration value. From the perspective of model calculation principle, the difference between the PMF model and the CMB model is that the CMB model requires the input of the chemical species concentration of the observed receptor and the mass fraction of the chemical species in the pollution source, while the PMF only requires the input of the chemical species concentration of the observed receptor. That is, the CMB model needs to input localized source spectrum information, while the PMF is not limited to this, but requires more receptor sample observation data. Therefore, combining the advantages of the two models can make the source analysis results of particulate matter pollution more accurate.

[0065] S3.3: Determine whether a forest fire has occurred: Comprehensively analyze the source analysis results of different receptor models to determine whether the main contributing source of particulate matter emissions at the monitoring site is biomass combustion. If the result is true, it indicates that a forest fire has occurred, and proceed to the next step for source analysis; if the result is no, it indicates that no forest fire has occurred, and exit the process.

[0066] This embodiment uses real-time particulate matter monitoring data combined with particulate matter source analysis technology, analyzes the characteristic information in the particulate matter monitoring components through receptor model source analysis, quickly and accurately identifies the biomass combustion source, and thus determines the occurrence of forest fires. This method can determine the occurrence of fires in real time, significantly improves the efficiency and accuracy of forest fire prevention, and provides an efficient and accurate means of fire warning and prevention and control.

[0067] Step S4: Establish a high-precision backward trajectory tracking system to obtain refined backward trajectory data. Specifically, the following steps are included:

[0068] Step S4.1: Data preparation: Based on the latitude and longitude information of the monitoring site in step S1, as well as the particulate matter emission data, the high-precision meteorological data automatic forecasting system established in step S2 is used to obtain high-precision meteorological data for the forest area during this period.

[0069] Step S4.2: Trajectory tracking: Based on the above data, the HySPLIT model is used to establish a high-precision backward trajectory tracking system. The location of the particulate matter component monitoring station is used as the trajectory starting point to perform backward trajectory tracking and obtain refined backward trajectory data of particulate matter emissions.

[0070] The HySPLIT model is a computational model used for atmospheric particle trajectory simulation and air quality research. The model is based on the Lagrangian method and combines the physical and chemical mechanisms of atmospheric transport and diffusion processes. It takes into account the influence of factors such as wind field, topography, atmospheric boundary layer and meteorological conditions on the movement of particle matter. It can simulate the diffusion, transport and deposition process of pollutants in the atmosphere and is widely used in research and application in the fields of environmental protection, meteorology, climatology, etc.

[0071] Step S5: Use PSCF, CWT, CFA, RTWC, and SQTBA algorithms to establish a high-precision source tracing calculation model to obtain the potential fire source location distribution. Specifically, the following steps are included:

[0072] Step S5.1: Data preparation: Obtain the monitoring site particulate matter emission data obtained in step S1 and the refined backward trajectory data calculated in step S4 to prepare for source tracing analysis.

[0073] Step S5.2: Source tracing analysis: Based on the above data, the PSCF, CWT, CFA, RTWC, and SQTBA algorithms are used to establish a high-precision particulate matter pollution source tracing calculation model to obtain the distribution data of the main potential sources contributing to particulate matter pollution.

[0074] The calculation formulas of each algorithm are as follows:

[0075] The calculation formula of the PSCF method is:

[0076]

[0077] Where n i,j is the number of all trajectory endpoints passing through the grid (i, j), m i,j is the number of endpoints of the particle emission trajectory passing through the grid (i, j), W i,j is the empirical weight function of the grid (i, j).

[0078] The calculation formula of the CWT method is:

[0079]

[0080] Where L is the total number of trajectories, C l is the concentration value observed by trajectory l at the target point, t i,j,l is the time it takes for the trajectory l to pass through the grid (i, j), W i,j is the empirical weight function of the grid (i, j).

[0081] The calculation formula of the CFA method is:

[0082]

[0083] Where L is the total number of trajectories, C l is the concentration value observed by trajectory l at the target point, t i,j,l is the time it takes for the trajectory l to pass through the grid (i, j), W i,j is the empirical weight function of the grid (i, j).

[0084] The calculation method of the RTWC method is:

[0085]

[0086] In the formula, C l,t is the redistributed concentration value of trajectory l in period t, c l is the monitored concentration value of trajectory l, N l is the total time experienced by the trajectory l (the total number of endpoints of the trajectory). When the trajectory l passes through the grid (i, j) in the t period:

[0087]

[0088] CF i,j The calculation method is as follows:

[0089]

[0090] In the formula, is the CF value of the grid (i, j) at the kth (k ≥ 1) iteration, L is the total number of trajectories, τ i,j,l,tis the number of endpoints of the grid (i, j) that the trajectory l passes through in the time period t. In the first iteration, that is, when k = 1, take When CF between two successful iterations i,j When the average deviation is less than 0.5%, the iteration ends.

[0091]

[0092] The final RTWC calculation formula is:

[0093]

[0094] Where W i,j is the empirical weight function of the grid (i, j).

[0095] The calculation method of SQTBA method is:

[0096]

[0097] Where Q(x,y,t|x′,y′,t′) is the transmission probability density function of the trajectory, that is, the probability that a point (x′,y′) on the trajectory at time t′ will appear at the position (x,y) at time t, where x,y are the coordinates of the grid center, x′,y′ are the coordinates of the corresponding point on the trajectory, and σ x and σ y is the standard deviation of the trajectory x and y in both directions (assuming it grows over time):

[0098] σ x (t′)=σ y (t′) = at′

[0099] Where a is the diffusion speed, which is 5.4 km / h.

[0100]

[0101] The above formula is the backward diffusion potential distribution function of trajectory l, which is obtained by backward integration of Q(x,y,t|x′,y′,t′) along the trajectory, and τ is the total duration of trajectory l.

[0102] The final SQTBA calculation formula is as follows:

[0103]

[0104] In the formula, c l is the monitored concentration value of trajectory l, L is the total number of trajectories, W i,j is the empirical weight function of the grid (i, j).

[0105] Furthermore, in the above algorithm, the empirical weight function W of the grid (i, j)i,j The value criteria are as follows:

[0106]

[0107] Where n ave is the average number of trajectory endpoints of the grid; n i,j Represents the number of trajectory endpoints within the grid (i, j).

[0108] At small scales, in order to improve n ave After hours i,j The value of is too large, and the method for determining the value of the weight coefficient is adjusted as follows: when 0.5n ave ≤2, take a=2; when n ave ≤3, take b=3; when 4n ave When ≤4, take c=4.

[0109] Aiming at the needs of small-scale traceability, the present invention optimizes the method for determining the value of the weight coefficient in the traceability algorithm. In small-scale traceability, in order to obtain high-precision results, it is usually necessary to use extremely high-resolution horizontal grids, and the grid spacing is usually less than 1 km. However, in this case, the number of grids may exceed the total number of trajectory endpoints, resulting in an average number of trajectory endpoints in each grid of less than 1. The traditional method for determining the weight coefficient of the traceability algorithm is prone to problems in small-scale traceability, resulting in excessively large weight coefficients, which in turn affects the credibility of the traceability results.

[0110] In order to solve this problem, the present invention optimizes the method for determining the value of the weight coefficient. Specifically, the restriction conditions of the weight coefficient under different thresholds are added to ensure that the value of the weight coefficient can remain within a reasonable range even when using a high-resolution horizontal grid under small-scale conditions. This improvement effectively improves the credibility of small-scale traceability results, thereby enhancing the reliability and accuracy of the traceability results.

[0111] Step S5.3: Data standardization: Use the Max-Min algorithm to standardize the potential fire source distribution data obtained by the five algorithms, and then average the five standardized potential fire source distribution data to finally obtain the comprehensive probability distribution data of the potential fire source location of the forest fire.

[0112] The Max-Min algorithm formula is:

[0113]

[0114] Where, X norm is the standardized data, X is the data to be processed, and X max and X min are the maximum and minimum values ​​of the data set, respectively.

[0115] According to previous studies, there are many challenges in tracing the source of particulate pollution within a small scale: for example, due to the influence of meteorological conditions and terrain, the diffusion of particulate matter is spatially non-uniform; the chemical composition of particulate matter is active and easily reacts with other components in the atmosphere to form secondary pollutants. It is difficult for a single algorithm to fully consider these factors, resulting in biased and inaccurate tracing results. The present invention combines five algorithms, PSCF, CWT, CFA, RTWC and SQTBA, and comprehensively utilizes different spatial and temporal scales to comprehensively describe the traceability path and propagation process of pollutants. Through the combination of multiple algorithms, the deficiencies are compensated for each other, the accuracy and stability of the traceability results are improved, the errors caused by a single algorithm are reduced, and the traceability results are more robust and reliable, which is suitable for complex and changeable atmospheric environments. The strategy of integrating the five algorithms improves the ability to accurately locate the source of particulate matter emissions and the accuracy of traceability, providing important technical support for accurately locating the source of forest fires.

[0116] S6: Send a drone to the fire source to take aerial photos and confirm the fire source and disaster situation of the forest fire. The specific steps include:

[0117] S6.1: UAV aerial photography: Based on the longitude and latitude of the potential location of the forest fire source obtained in step S5, mission planning is performed, including locating the fire source, designing the flight path to ensure coverage of the fire source area and avoid obstacles and dangerous areas, and checking the status of UAV equipment such as batteries, cameras, and sensors. Subsequently, the UAV is started to take off and fly to the potential location of the fire source according to the predetermined path. The ground control station monitors the flight status and shooting of the UAV in real time. High-altitude aerial photography is performed in the target area to obtain panoramic images. If conditions permit, the flight altitude is lowered to take detailed photos of key areas to obtain a clearer fire source location and fire situation.

[0118] S6.2: Confirmation of fire situation: The drone will transmit the captured images and video data back to the ground control station in real time, and the staff will conduct a preliminary analysis of the data to confirm the location and intensity of the fire. By comparing the images taken by the drone with the previous potential fire source location data, the exact location of the fire source is finally confirmed and marked on the map. Analyze the aerial images to assess the size of the fire, the direction of spread and the affected area. If conditions permit, send ground personnel to the fire source location for field investigation to obtain first-hand fire information. According to the results of aerial photography and on-site investigation, formulate and implement emergency dispatch and rescue plans, mobilize fire brigades, fire-fighting equipment and rescue personnel to the fire source location, and provide detailed fire information and suggestions to support the formulation and implementation of emergency decisions.

[0119] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0120] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A forest fire monitoring method based on particle tracing technology, characterized in that: The method comprises: Step 1: Obtain particulate matter component monitoring data in real time through the particulate matter component monitoring station; Step 2: Based on global meteorological reanalysis data and observation data from meteorological monitoring stations, an automated forecasting system for meteorological data above forest areas is constructed to obtain real-time meteorological data; Step 3: Analyze the monitoring data of the particulate matter components to determine whether the main contributing emission source to the particulate matter pollution at the monitoring station is the biomass combustion source. If so, it indicates that a forest fire has occurred, and the next step of tracing the source is carried out; Step 4: Based on the particulate matter component monitoring data obtained in step 1 and the meteorological data obtained in step 2, the backward trajectory of the pollution source is tracked using the HySPLIT software to obtain refined backward trajectory data; Step 5: Based on the particulate matter component monitoring data obtained in step 1 and the refined backward trajectory data obtained in step 4, a variety of reverse calculation models are used to trace the particulate matter to obtain the location distribution data of potential forest fire sources; the reverse calculation models include: PSCF algorithm, CWT algorithm, CFA algorithm, RTWC algorithm, and SQTBA algorithm.

2. The forest fire monitoring method based on particle tracing technology according to claim 1 is characterized in that: In the various reverse calculation models, the empirical weight function W of the grid (i, j) i,j The value standards are: Among them, n ave is the average number of trajectory endpoints in the grid, m i,j is the number of all trajectory endpoints passing through the grid (i, j).

3. The forest fire monitoring method based on particle tracing technology according to claim 1 is characterized in that: The step 2 comprises: Step 21: Collect publicly available global meteorological reanalysis data and observation data from meteorological monitoring stations; Step 22: Use the WRF numerical forecast model, take the meteorological reanalysis data as the initial data, use the WRFDA data assimilation model, and adopt the 3DVAR assimilation method to assimilate the observation data of meteorological monitoring stations in real time, establish a high-precision meteorological data automatic forecast system covering the forest area, and collect high-precision real-time meteorological data over the forest area.

4. The forest fire monitoring method based on particle tracing technology according to claim 1 is characterized in that: The step 3 comprises: Step 31: Obtain the particulate matter component monitoring data; Step 32: performing source analysis on the particulate matter component monitoring data using a receptor model; Step 33: Comprehensively analyze the source analysis results of each receptor model to determine whether the main emission source of particulate matter at the monitoring site is biomass combustion. If so, it indicates that a forest fire has occurred and further source tracing is required; if not, it indicates that no forest fire has occurred and no source tracing is required.

5. The forest fire monitoring method based on particle tracing technology according to claim 1 is characterized in that: The step 4 comprises: Step 41: According to the latitude and longitude information of the particulate matter component monitoring station and the time period of particulate matter emission, the meteorological data automatic forecasting system established in step 2 is used to obtain high-precision real-time meteorological data for the required area and time period; Step 42: Use the HySPLIT model to establish a high-precision backward trajectory tracking system, use the location of the particulate matter component monitoring station as the trajectory starting point, perform backward trajectory tracking, and obtain refined backward trajectory data of particulate matter emissions.

6. The forest fire monitoring method based on particle tracing technology according to claim 1 is characterized in that: The method also includes: using drones to take aerial photos to finally confirm the location of the fire source.

7. The forest fire monitoring method based on particle tracing technology according to claim 3 is characterized in that: The receptor model includes: a chemical mass balance model and a positive definite matrix factorization model.

8. A forest fire monitoring system based on particle tracing technology, characterized in that: The system is used to implement the forest fire monitoring method based on particle tracing technology according to any one of claims 1 to 7, and comprises: A particle component data acquisition module is configured to acquire particle component monitoring data in real time through a particle component monitoring station; A meteorological data acquisition module is configured to construct an automatic forecasting system for meteorological data above the forest area based on global meteorological reanalysis data and observation data from meteorological monitoring stations to obtain real-time meteorological data; The judgment module is configured to analyze the particulate matter component monitoring data to determine whether the main contributing emission source causing the particulate matter pollution of the monitoring station is the biomass combustion source. If so, it indicates that a forest fire has occurred, and the next step of tracing the source is performed; A backward trajectory data calculation module is configured to perform backward trajectory tracking of pollution sources using HySPLIT software based on the particulate matter component monitoring data and meteorological data to obtain refined backward trajectory data; The fire source location determination module is configured to trace the particulate matter based on the particulate matter component monitoring data and the refined backward trajectory data, and obtain the location distribution data of potential forest fire sources by using multiple reverse calculation models; the reverse calculation models include: PSCF algorithm, CWT algorithm, CFA algorithm, RTWC algorithm, and SQTBA algorithm.

9. A forest fire monitoring device based on particle tracing technology, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the forest fire monitoring method based on particle tracing technology as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: A computer program is stored on the storage medium. When the computer program is executed by the processor, the forest fire monitoring method based on particle tracing technology as described in any one of claims 1 to 7 is implemented.

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