Real-time combustible combustion monitoring method and system based on remote sensing technology

Abstract

The invention provides a combustible combustion real-time monitoring method and system based on a remote sensing technology. The method comprises the steps that electronic equipment obtains multi-temporal remote sensing data of a to-be-monitored area; obtaining region information with fire points and types of the fire points in the region information by using a pre-trained fire point identification model; predicting the smoke diffusion path by using a pre-trained hydrodynamic model in combination with an intelligent wind field simulation algorithm to obtain a dynamic smoke diffusion map including the predicted smoke diffusion path; and predicting the fire behavior development trend of the to-be-monitored area. The mode not only greatly expands the monitoring range, but also completely gets rid of dependence on human resources, and realizes automation, intelligence and real-time performance of combustible combustion real-time monitoring based on the remote sensing technology. The monitoring range and efficiency are greatly improved, and the labor cost is also reduced. Meanwhile, a dynamic smoke diffusion map and a fire trend prediction function can help related departments to respond quickly.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and particularly relates to a real-time monitoring method and system for combustible material combustion based on remote sensing technology. Background Art

[0002] Combustion of combustible materials such as straw is a common phenomenon in the agricultural production process. Due to improper treatment methods, straw burning will cause serious pollution to the atmospheric environment, release a large amount of harmful gases such as smoke, carbon dioxide, and carbon monoxide, leading to the aggravation of haze weather. At the same time, it may trigger fires, endangering ecological safety and personal and property safety. Therefore, how to conduct real-time monitoring and dynamic early warning of straw burning to reduce environmental pollution and potential safety hazards has become an urgent problem to be solved.

[0003] The existing real-time monitoring methods for combustible material combustion based on remote sensing technology mainly include ground inspections, video monitoring, remote sensing monitoring, etc. Among them, ground inspections rely on human resources and have problems such as limited monitoring range, low efficiency, and poor real-time performance. Summary of the Invention

[0004] In order to overcome the defects of the limited monitoring range, low efficiency, and poor real-time performance of the existing real-time monitoring of combustible material combustion based on remote sensing technology, the present invention provides a real-time monitoring method for combustible material combustion based on remote sensing technology, including:

[0005] Obtaining multi-temporal remote sensing data of the area to be monitored;

[0006] Based on the multi-temporal remote sensing data, using a pre-trained fire point recognition model for recognition to obtain the area information where fire points exist and the types of fire points existing in the area information;

[0007] Based on the multi-temporal remote sensing data, using a pre-trained hydrodynamic model combined with an intelligent wind field simulation algorithm to predict the smoke diffusion path and obtain a dynamic smoke diffusion map including the predicted smoke diffusion path;

[0008] According to the area information where fire points exist, the types of fire points existing in the area information, and the dynamic smoke diffusion map, predicting the development trend of the fire situation in the area to be monitored.

[0009] Further, the using a pre-trained fire point recognition model for recognition based on the multi-temporal remote sensing data to obtain the area information where fire points exist and the types of fire points existing in the area information includes:

[0010] Extracting the fire point features of each time phase in the multi-temporal remote sensing data;

[0011] According to the sequence of each time phase, splicing the fire point features of each time phase to obtain the spliced fire point features;

[0012] Based on the spliced fire point features and the multi-temporal remote sensing data, use the pre-trained fire point recognition model for recognition to obtain the area information where there are fire points and the types of fire points existing in the area information.

[0013] Further, the extraction of the fire point features of each time phase in the multi-temporal remote sensing data includes:

[0014] Based on each time phase remote sensing data in the multi-temporal remote sensing data, perform feature extraction on the remote sensing images in the time phase remote sensing data to obtain the fire point features of the time phase. The fire point features include the fire point features of heat source temperature, flame brightness, and smoke concentration.

[0015] Further, the fire point recognition model includes a location recognition sub-model and a type recognition sub-model;

[0016] The recognition based on the spliced fire point features of each time phase and the multi-temporal remote sensing data using the pre-trained fire point recognition model to obtain the area information where there are fire points and the types of fire points existing in the area information includes:

[0017] Based on the spliced fire point features of each time phase, the remote sensing images and local images in the multi-temporal remote sensing data, use the location recognition sub-model to obtain the area information where there are fire points;

[0018] Based on the spliced fire point features of each time phase, the area information where there are fire points, and the multi-temporal remote sensing data, use the type recognition sub-model to obtain the types of fire points existing in the area information.

[0019] Further, the prediction of the smoke diffusion path based on the multi-temporal remote sensing data using the pre-trained fluid dynamics model combined with the intelligent wind field simulation algorithm to obtain the dynamic smoke diffusion map including the predicted smoke diffusion path includes:

[0020] Obtain the ground meteorological data at the current time. The ground meteorological data includes wind speed, humidity, and air flow;

[0021] Based on the ground meteorological data and the multi-temporal remote sensing data, use the pre-trained fluid dynamics model to predict the wind field change to obtain the wind field change situation in the future preset time period at the current time;

[0022] Based on the wind field change situation in the future preset time period and the multi-temporal remote sensing data, use the intelligent wind field simulation algorithm to predict the smoke diffusion path to obtain the dynamic smoke diffusion map including the predicted smoke diffusion path.

[0023] Further, the types of the fire points include temporary fire points and / or persistent fire points;

[0024] Predicting the development trend of the fire situation in the area to be monitored according to the area information where the fire point exists, the type of the fire point existing in the area information, and the dynamic smoke diffusion map includes:

[0025] If the type of the fire point existing in the area information is a persistent fire point, then according to the area information, the dynamic smoke diffusion map, and the terrain and combustible distribution of the area to be monitored, predict the spread range and development direction of the fire situation in the area to be monitored.

[0026] Further, after predicting the development trend of the fire situation in the area to be monitored according to the area information where the fire point exists, the type of the fire point existing in the area information, and the dynamic smoke diffusion map, it further includes:

[0027] Based on the location information of the area to be monitored, determine the population density and environmentally sensitive areas corresponding to the location information;

[0028] Based on the population density, the risk weight of the population density, the environmentally sensitive area, and the risk weight of the environmentally sensitive area, perform weighted summation to determine the risk level corresponding to the development trend of the fire situation.

[0029] On the other hand, the present invention also provides a real-time monitoring system for combustible combustion based on remote sensing technology, including:

[0030] An acquisition module, configured to acquire multi-temporal remote sensing data of the area to be monitored;

[0031] A fire point identification module, configured to identify, based on the multi-temporal remote sensing data, using a pre-trained fire point identification model, to obtain the area information where the fire point exists, and the type of the fire point existing in the area information;

[0032] A smoke diffusion simulation module, configured to predict the smoke diffusion path based on the multi-temporal remote sensing data, using a pre-trained hydrodynamic model combined with an intelligent wind field simulation algorithm, to obtain a dynamic smoke diffusion map including the predicted smoke diffusion path;

[0033] A prediction module, configured to predict the development trend of the fire situation in the area to be monitored according to the area information where the fire point exists, the type of the fire point existing in the area information, and the dynamic smoke diffusion map.

[0034] On the other hand, the present invention also provides a computer device, characterized in that it includes: one or more processors;

[0035] The processor is configured to store one or more programs;

[0036] When the one or more programs are executed by the one or more processors, the real-time monitoring method for combustible burning based on remote sensing technology described in any one of the above is implemented.

[0037] On the other hand, the present invention also provides a computer-readable storage medium, characterized in that a computer program is stored thereon, and when the computer program is executed, the real-time monitoring method for combustible burning based on remote sensing technology described in any one of the above is implemented.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] The present invention provides a real-time monitoring method and system for combustible burning based on remote sensing technology. The method includes: an electronic device acquires multi-temporal remote sensing data of a to-be-monitored area; based on the multi-temporal remote sensing data, a pre-trained fire point recognition model is used for recognition to obtain area information where fire points exist and the types of fire points existing in the area information; based on the multi-temporal remote sensing data, a pre-trained fluid dynamics model is combined with an intelligent wind field simulation algorithm to predict the smoke diffusion path and obtain a dynamic smoke diffusion map including the predicted smoke diffusion path; according to the area information where fire points exist, the types of fire points existing in the area information, and the dynamic smoke diffusion map, the development trend of the fire situation in the to-be-monitored area is predicted. In the embodiments of the present invention, the area information where fire points exist and the types of fire points existing in the area information are obtained by using the fire point recognition model, and the fluid dynamics model is combined with the intelligent wind field simulation algorithm to predict the smoke diffusion path and obtain a dynamic smoke diffusion map, and then the fire development area is predicted. This way not only greatly expands the monitoring range, but also completely gets rid of the dependence on human resources, and can also track the development trend of the fire situation in real time. It not only greatly improves the monitoring range and efficiency, but also reduces the labor cost. At the same time, the dynamic smoke diffusion map and the fire situation trend prediction function can help relevant departments respond quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic flowchart of the real-time monitoring method for combustible burning based on remote sensing technology of the present invention;

[0041] Figure 2 It is a detailed process schematic for determining the fire point recognition result provided by an embodiment of the present invention;

[0042] Figure 3 It is a schematic structural diagram of the real-time monitoring system for combustible burning based on remote sensing technology of the present invention;

[0043] Figure 4 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following further elaborates on the specific implementation manners of the present invention with reference to the accompanying drawings.

[0045] Embodiment 1:

[0046] A real-time monitoring method for combustible material combustion based on remote sensing technology provided by the present invention has a process schematic diagram as Figure 1 shown, including:

[0047] Step 101: Obtain multi-temporal remote sensing data of the area to be monitored;

[0048] Step 102: Based on the multi-temporal remote sensing data, use a pre-trained fire point recognition model for recognition to obtain the area information where there are fire points and the types of fire points existing in the area information;

[0049] Step 103: Based on the multi-temporal remote sensing data, use a pre-trained fluid dynamics model combined with an intelligent wind field simulation algorithm to predict the smoke diffusion path and obtain a dynamic smoke diffusion map including the predicted smoke diffusion path;

[0050] Step 104: Predict the development trend of the fire situation according to the area information where there are fire points, the types of fire points existing in the area information, and the dynamic smoke diffusion map.

[0051] A real-time monitoring method for combustible material combustion based on remote sensing technology provided by an embodiment of the present invention is applied to an electronic device, and the electronic device can be an intelligent device such as a personal computer (PC), a server, etc.

[0052] In order to accurately and effectively conduct real-time monitoring of combustible material combustion based on remote sensing technology, in an embodiment of the present invention, a fire point recognition model is used to obtain the area information where there are fire points and the types of fire points existing in the area information, and a fluid dynamics model combined with an intelligent wind field simulation algorithm is also used to predict the smoke diffusion path and obtain a dynamic smoke diffusion map, and then predict the fire development area. This method not only greatly expands the monitoring range, but also completely gets rid of the dependence on human resources, and can also track the development trend of the fire situation in real time.

[0053] In order to achieve precise and efficient monitoring of the area to be monitored, the present invention proposes a collaborative perception method combining high-resolution remote sensing images, thermal infrared monitoring and ground observations to obtain multi-temporal remote sensing data.

[0054] Specifically, high-resolution remote sensing images can be obtained through high-resolution optical satellites, unmanned aerial vehicles equipped with high-resolution cameras or sensors, etc.; thermal infrared data can be obtained through thermal infrared remote sensing technology; combined with ground observation devices such as intelligent cameras and meteorological monitoring stations, more detailed ground images or thermal infrared data can be further obtained.

[0055] To accurately and effectively monitor the area to be monitored, the electronic device can obtain multi-temporal remote sensing data of the area to be monitored. Herein, the multi-temporal remote sensing data refers to a set of remote sensing data obtained at different time points in the same geographical area (i.e., the area to be monitored).

[0056] The electronic device locally stores a pre-trained fire point recognition model. The electronic device can input the multi-temporal remote sensing data into the fire point recognition model to obtain the output of the fire point recognition model. The output includes the area information where fire points exist. The area information of the fire points includes the specific location, range, and spatial distribution of the fire points, and the output also includes the type of the fire points existing in the area information.

[0057] It can be understood that the scenario to which the embodiments of the present application are applied can be the monitoring of straw burning, and of course, it can also be the monitoring of the burning of other combustibles. If the present invention is to monitor straw burning, the fire point recognition model identifies each area of the fire points corresponding to the straw. If it is to monitor other combustibles, the fire point recognition model identifies each area of the fire points corresponding to the other combustibles.

[0058] In the embodiments of the present invention, based on the multi-temporal remote sensing data, the electronic device can accurately predict the diffusion path of the smoke and generate a dynamic smoke diffusion map by combining a pre-trained fluid dynamics model and an intelligent wind field simulation algorithm. This process can first use the multi-temporal remote sensing data to obtain information such as the burning intensity of the fire point, and at the same time combine terrain data (such as elevation, slope, surface type) to construct a high-precision environmental model. The fluid dynamics model simulates the diffusion behavior of the smoke by solving the motion equation of the smoke in the air, considering the influence of factors such as the density, temperature, and wind speed of the smoke on the diffusion path. The intelligent wind field simulation algorithm is based on meteorological and terrain data and uses machine learning technology to optimize the accuracy of the wind field simulation, so as to more accurately reflect the diffusion trend of the smoke. Finally, information such as the predicted smoke diffusion path is obtained. In one example, the smoke diffusion path and concentration distribution can be obtained to generate a dynamic smoke diffusion map.

[0059] The diffusion range of the smoke in the smoke diffusion map is represented by areas with different colors or transparencies. In one example, the smoke concentration distribution is distinguished by the depth of color or contour lines. High-concentration areas are usually represented by dark colors, and low-concentration areas are represented by light colors. The diffusion path clearly shows the moving direction of the smoke through arrows or streamlines. The smoke diffusion map can also include a time dimension, and display the dynamic change process of the smoke diffusion in the form of an animation or a time slider to help understand the evolution trend of the smoke over time.

[0060] Based on the regional information of fire points, the type of fire points, and the dynamic smoke diffusion map, the electronic device can comprehensively predict the development trend of the fire situation in the area to be monitored. In one example, by analyzing the regional information of each fire point, including the location, combustion range, intensity, etc. of the fire point, and combining the type of fire point, the severity and potential risks of the fire can be initially judged. Temporary fire points are usually caused by short-term factors and have a lower risk of spreading, while continuous fire points may be accompanied by large-scale combustion and spread, posing a higher danger. By combining the dynamic smoke diffusion map, the electronic device can evaluate the diffusion path, concentration distribution of the smoke, and its impact on the surrounding environment. The electronic device can also simulate the spreading direction and speed of the fire by integrating meteorological data and terrain data. For example, strong winds and dry conditions may accelerate the spread of the fire, while complex terrain may affect the diffusion path of the fire.

[0061] By comprehensively analyzing the fire point information, the dynamic smoke diffusion, and environmental factors, a prediction report on the development trend of the fire situation can be generated, providing a scientific basis for disaster management, emergency response, and resource allocation. Based on the Artificial Intelligence (AI) algorithm, the electronic device can quickly locate the burning area, generate a dynamic warning map, and push it to the relevant management departments. The present invention improves the real-time performance and accuracy of burning monitoring and provides reliable technical support for environmental pollution prevention and control.

[0062] In order to obtain the fire point recognition result and then realize the prediction of the development trend of the fire situation, based on the above embodiments, in the embodiments of the present invention, based on multi-temporal remote sensing data, the pre-trained fire point recognition model is used for recognition to obtain the regional information of the area where there are fire points, and the types of fire points existing in the regional information include:

[0063] Extract the fire point features of each time phase from the multi-temporal remote sensing data;

[0064] According to the sequence of each time phase, splice the fire point features of each time phase to obtain the spliced fire point features;

[0065] Based on the spliced fire point features and the multi-temporal remote sensing data, use the pre-trained fire point recognition model for recognition to obtain the regional information of the area where there are fire points, and the types of fire points existing in the regional information.

[0066] In the multi-temporal remote sensing data, the electronic device can first extract the fire point features of each time phase. In one example, the fire point features may include key information such as the combustion intensity and temperature anomaly of the fire point. Subsequently, the electronic device can splice the extracted fire point features in a time series according to the sequence of each time phase to form a continuous fire point feature sequence. This process can retain the spatial distribution information of the fire points and also obtain the dynamic change law of the fire points in the time dimension.

[0067] Based on the spliced fire point features and multi-temporal remote sensing data, use the pre-trained fire point recognition model for analysis. This fire point recognition model can identify the regional information where there are fire points through deep learning or machine learning algorithms, and can further determine the types of fire points corresponding to the regional information.

[0068] Figure 2 This is a schematic diagram of the detailed process for determining the fire point recognition result provided by the embodiment of the present invention. This process includes the following steps:

[0069] S201: Obtain multi-temporal remote sensing data of the area to be monitored.

[0070] S202: Extract the fire point features of each time phase from the multi-temporal remote sensing data.

[0071] S203: Splice the fire point features of each time phase according to the sequence of each time phase.

[0072] S204: Based on the spliced fire point features of each time phase and the multi-temporal remote sensing data, use the pre-trained fire point recognition model for recognition to obtain the regional information where there are fire points and the types of fire points existing in the regional information.

[0073] In order to extract the fire point features, based on the above embodiments, in the embodiment of the present application, extracting the fire point features of each time phase from the multi-temporal remote sensing data includes:

[0074] Based on each time phase remote sensing data in the multi-temporal remote sensing data, perform feature extraction on the remote sensing images in the time phase remote sensing data to obtain the fire point features of the time phase. The fire point features include the fire point features of heat source temperature, flame brightness, and smoke concentration.

[0075] The electronic device can process the remote sensing images in each time phase remote sensing data to obtain the fire point features of the time phase. The fire point features include heat source temperature, flame brightness, and smoke concentration, etc. The heat source temperature can be obtained through thermal infrared band data; the flame brightness is obtained based on visible light band data and is used to evaluate the brightness and darkness of the flame; the smoke concentration is calculated through multi-spectral or hyperspectral data in combination with the spectral characteristics of the smoke.

[0076] By extracting each fire point feature, the fire points can be identified more accurately, and it can also provide data support for subsequent prediction of the types of fire points, the development trend of the fire situation, etc.

[0077] In order to determine the fire point recognition result, based on the above embodiments, in the embodiment of the present invention, the fire point recognition model includes a position recognition sub-model and a type recognition sub-model;

[0078] Based on the fire point features of each phase after splicing and multi-temporal remote sensing data, use the pre-trained fire point recognition model for recognition to obtain the regional information where fire points exist, and the types of fire points existing in the regional information include:

[0079] Based on the fire point features of each phase after splicing, the remote sensing images and local images in the multi-temporal remote sensing data, use the position recognition sub-model in the fire point recognition model to obtain the regional information where fire points exist;

[0080] Based on the fire point features of each phase after splicing, the regional information where fire points exist, and the multi-temporal remote sensing data, use the type recognition sub-model in the fire point recognition model to obtain the types of fire points existing in the regional information.

[0081] In order to accurately and effectively obtain the fire point recognition results, the fire point recognition model includes a position recognition sub-model and a type recognition sub-model. Among them, the position recognition sub-model is used to recognize each area where fire points exist, and the type recognition sub-model is used to recognize the types of fire points existing in the regional information.

[0082] Specifically, the electronic device first uses the position recognition sub-model in the pre-trained fire point recognition model based on the fire point features of each phase after splicing and the remote sensing images and local images in the multi-temporal remote sensing data. The position recognition sub-model locates each area where fire points exist by analyzing the spatial distribution and time series changes of the fire point features, and combines the spectral, texture, and thermal radiation information of the remote sensing images and local images, and outputs the regional information of these areas. The regional information may include the geographical location of the fire point, the combustion range, and its spatial distribution characteristics, etc.

[0083] It can be understood that if the present invention is to monitor straw burning, the position recognition sub-model of the fire point recognition model recognizes each area of the fire points corresponding to the straw. If other combustibles are to be monitored, the position recognition sub-model of the fire point recognition model recognizes each area of the fire points corresponding to the other combustibles.

[0084] Based on the fire point features after splicing, the recognized fire point regional information, and the multi-temporal remote sensing data, use the type recognition sub-model in the fire point recognition model for analysis. The type recognition sub-model discriminates the types of fire points in each area by features such as the heat source temperature, flame brightness, and smoke concentration of the fire points, and combines the spatio-temporal dynamic change rules of the fire points. The fire point types mainly include temporary fire points and continuous fire points.

[0085] Through the position recognition sub-model and the type recognition sub-model, the fire point recognition model can accurately output the regional information of each fire point and the type of the fire point in the regional information, which helps to improve the accuracy and efficiency of fire monitoring.

[0086] In order to accurately obtain the dynamic smoke diffusion map, based on the above embodiments, in the embodiments of the present invention, based on multi-temporal remote sensing data, using a pre-trained hydrodynamic model combined with an intelligent wind field simulation algorithm, the smoke diffusion path is predicted, and the dynamic smoke diffusion map including the predicted smoke diffusion path is obtained, including:

[0087] Obtain the ground meteorological data at the current time, where the ground meteorological data includes wind speed, humidity, and air flow;

[0088] Based on the ground meteorological data and multi-temporal remote sensing data, use the pre-trained hydrodynamic model to predict the wind field change, and obtain the wind field change situation in the future preset time period at the current time;

[0089] Based on the wind field change situation in the future preset time period and multi-temporal remote sensing data, use the intelligent wind field simulation algorithm to predict the smoke diffusion path, and obtain the dynamic smoke diffusion map including the predicted smoke diffusion path.

[0090] In order to obtain the dynamic smoke diffusion map, the electronic device can first obtain the ground meteorological data at the current time. Among them, the ground meteorological data includes information such as wind speed, humidity, and air flow. Input the obtained ground meteorological data and multi-temporal remote sensing data into the pre-trained hydrodynamic model. Among them, the hydrodynamic model can be a Computational Fluid Dynamics Model (CFD) or a Weather Research and Forecasting Model (WRF). The hydrodynamic model predicts the wind field change and obtains and outputs the wind field change situation in the future preset time period at the current time.

[0091] After obtaining the wind field change situation, the electronic device can use the intelligent wind field simulation algorithm (such as a wind field correction model based on machine learning or a Lagrangian particle diffusion model) to predict the smoke diffusion path with the obtained wind field change situation and multi-temporal remote sensing data. The intelligent wind field simulation algorithm generates a dynamic smoke diffusion map including the predicted smoke diffusion path by integrating the wind field change situation of the time series, and intuitively displays the change of smoke over time.

[0092] In order to determine the development trend of the fire situation, based on the above embodiments, in the embodiments of the present application, the types of fire points include temporary fire points and / or continuous fire points;

[0093] According to the area information where the fire point exists, the type of the fire point existing in the area information, and the dynamic smoke diffusion map, predicting the development trend of the fire situation in the area to be monitored includes:

[0094] If the type of the fire point where the area information exists is a continuous fire point, then according to the area information, the dynamic smoke diffusion map, and the terrain and combustible distribution of the area to be monitored, predict the spread range and development direction of the fire in the area to be monitored.

[0095] To determine the development trend of the fire situation, the electronic device can determine whether there is a fire point where a certain area information exists and the type of the fire point is a continuous fire point. If there is a fire point where a certain area information exists and the type of the fire point is a continuous fire point, then according to the target area information corresponding to the continuous fire point, the obtained dynamic smoke diffusion map, and the terrain and combustible distribution data saved in advance, predict the spread range and development direction of the fire. Among them, the dynamic smoke diffusion map provides the smoke diffusion path and concentration distribution, which can assist in analyzing the fire spread trend. Combining the terrain (such as slope, elevation) and the type of combustibles (such as vegetation density, distribution of flammable materials), the potential path and speed of the fire spread can be evaluated.

[0096] To accurately and effectively monitor the fire situation, based on the above embodiments, in the embodiments of the present application, after predicting the development trend of the fire situation in the area to be monitored according to the area information where the fire point exists, the type of the fire point where the area information exists, and the dynamic smoke diffusion map, it further includes:

[0097] Based on the location information of the area to be monitored, determine the population density and environmentally sensitive areas corresponding to the location information;

[0098] Based on the population density, the risk weight of the population density, the environmentally sensitive areas, and the risk weight of the environmentally sensitive areas, perform weighted summation to determine the risk level corresponding to the development trend of the fire situation.

[0099] Since in the actual scenario, when a fire occurs in different areas, the corresponding risk levels are different. For example, when a fire occurs in an area with a large population, the danger level is higher, and when a fire occurs in an area with a small population, the danger level will be relatively lower. Based on this, the electronic device can determine the risk level corresponding to the development trend of the fire situation according to the population density and environmentally sensitive areas corresponding to the location information of the area to be monitored.

[0100] Specifically, the electronic device can first obtain the location information of the area to be monitored, and the location information can include geographical coordinates and boundary ranges. Based on this location information, further analyze the population density and environmentally sensitive areas in the area to be monitored. Among them, the environmentally sensitive areas can be nature reserves, water sources, residential areas, etc. Based on the population density, the risk weight of the population density, the environmentally sensitive areas, and the risk weight of the environmentally sensitive areas, perform weighted summation to obtain the corresponding value, and determine the danger score corresponding to the development trend of the fire situation, and determine the product of the value and the danger score as the risk level value.

[0101] The electronic device can determine the risk level value through the following formula:

[0102] R = (P × W P + E × W E ) × S where R is the risk level value, P is the value corresponding to the population density, W P is the risk weight of the population density, E is the standardized value or score corresponding to the environmental sensitive period, W E is the risk weight of the environmentally sensitive area, and S is the danger score corresponding to the fire development trend.

[0103] Among them, the faster the speed of fire spread, the higher the danger score, and the larger the area or scale of the fire, the higher the danger score. Based on this, the electronic device can determine the danger score corresponding to the fire development trend according to the speed of fire spread and the area or scale of the fire.

[0104] According to each pre - saved risk range, determine the target risk range to which the risk level value belongs, and determine the risk level corresponding to the target risk range as the risk level corresponding to the fire development trend.

[0105] In one example, the combustion behavior tracking module of the electronic device can continuously track the combustion behavior by combining time - series image analysis, identify high - frequency fire points, and infer their sources, including farmland, abandoned land, industrial land, etc. Use intelligent target recognition technology to analyze the ground images in multi - temporal remote sensing data, identify vehicle and personnel activities during the burning process, and support law enforcement evidence collection. Based on farmland management and historical fire point data, generate a portrait of farmers' burning habits and provide intelligent suggestions for the treatment of combustibles such as straw, such as promoting straw recycling and demarcating no - burning areas. The governance effect evaluation module of the electronic device can, after the implementation of governance measures, track the change in the number of burning fire points through high - resolution remote sensing images combined with AI analysis, and conduct a governance effect evaluation. Combine the data of the air quality monitoring station to analyze the change trend of pollutant (PM2.5, CO, SO2) concentrations, and quantify the impact of the burning of combustibles such as straw on air quality. Use machine learning algorithms, combined with historical fire point distribution, policy implementation, and meteorological factors, to construct a governance effect prediction model to optimize policy formulation.

[0106] Embodiment 2:

[0107] Based on the same inventive concept, the present invention also provides a real - time monitoring system for combustible combustion based on remote sensing technology, the structural schematic diagram is as Figure 3 shown, including:

[0108] An acquisition module 301, configured to acquire multi - temporal remote sensing data of the area to be monitored;

[0109] The fire point recognition module 302 is used to identify based on the multi-temporal remote sensing data by using a pre-trained fire point recognition model, so as to obtain the area information where there are fire points and the types of fire points existing in the area information;

[0110] The smoke diffusion simulation module 303 is used to predict the smoke diffusion path based on the multi-temporal remote sensing data by using a pre-trained hydrodynamic model combined with an intelligent wind field simulation algorithm, so as to obtain a dynamic smoke diffusion map including the predicted smoke diffusion path;

[0111] The prediction module 304 is used to predict the development trend of the fire situation in the area to be monitored according to the area information where there are fire points, the types of fire points existing in the area information, and the dynamic smoke diffusion map.

[0112] In a specific implementation manner, the above-mentioned fire point recognition module 302 is specifically used to extract the fire point features of each time phase from the multi-temporal remote sensing data;

[0113] According to the sequence of each time phase, splice the fire point features of each time phase to obtain the spliced fire point features;

[0114] Based on the spliced fire point features and the multi-temporal remote sensing data, use a pre-trained fire point recognition model to identify, so as to obtain the area information where there are fire points and the types of fire points existing in the area information.

[0115] In a specific implementation manner, the above-mentioned fire point recognition module 302 is specifically used to perform feature extraction on the remote sensing images in the time-phase remote sensing data based on each time-phase remote sensing data in the multi-temporal remote sensing data, so as to obtain the fire point features of the time phase, and the fire point features include the fire point features of heat source temperature, flame brightness, and smoke concentration.

[0116] In a specific implementation manner, the above-mentioned fire point recognition module 302 is specifically used to obtain the area information where there are fire points by using the position recognition sub-model of a pre-trained fire point recognition model based on the spliced fire point features of each time phase, the remote sensing images in the multi-temporal remote sensing data, and the local images;

[0117] Based on the spliced fire point features of each time phase, the area information where there are fire points, and the multi-temporal remote sensing data, use the type recognition sub-model of a pre-trained fire point recognition model to obtain the types of fire points existing in the area information.

[0118] In a specific implementation manner, the above-mentioned smoke diffusion simulation module 303 is specifically used to obtain the ground meteorological data at the current time, and the ground meteorological data includes wind speed, humidity, and air flow;

[0119] Based on the ground meteorological data and the multi-temporal remote sensing data, a pre-trained hydrodynamic model is used to predict the wind field changes, and the wind field changes in a preset future period at the current time are obtained;

[0120] Based on the wind field changes in the preset future period and the multi-temporal remote sensing data, an intelligent wind field simulation algorithm is used to predict the smoke diffusion path, and a dynamic smoke diffusion map including the predicted smoke diffusion path is obtained.

[0121] In a specific implementation manner, the above prediction module 304 is specifically configured to, if the type of the fire point existing in the area information is a persistent fire point, predict the spread range and development direction of the fire situation in the area to be monitored according to the area information, the dynamic smoke diffusion map, and the terrain and combustible distribution of the area to be monitored.

[0122] In a specific implementation manner, the above prediction module 304 is further configured to determine the population density and environmentally sensitive areas corresponding to the location information based on the location information of the area to be monitored;

[0123] Based on the population density, the risk weight of the population density, the environmentally sensitive area, and the risk weight of the environmentally sensitive area, a weighted sum is performed to determine the risk level corresponding to the development trend of the fire situation.

[0124] Embodiment 3:

[0125] As Figure 4 shown, the present invention further provides an electronic device, which may be a computer device, a single-chip microcomputer device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected by a bus; the memory can be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, and the data can be called and / or modified when the instructions are executed.

[0126] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a real-time monitoring method for combustible material combustion based on remote sensing technology in the above embodiments.

[0127] Embodiment 4:

[0128] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). The electronic device-readable storage medium is a memory device in the electronic device, used to store programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device, and of course can also include the extended storage medium supported by the electronic device. The storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory. By loading and executing one or more instructions stored in the storage medium by the processor, the steps of a real-time monitoring method for combustible material combustion based on remote sensing technology in the above embodiments can be implemented.

[0129] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0130] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more flows and / or blocks. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0131] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one or more flows and / or blocks. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or blocks. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[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 its protection scope. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present invention, various changes, modifications, or equivalent replacements can still be made to the specific implementation manners of the application. However, these changes, modifications, or equivalent replacements are all within the protection scope of the pending claims of the application.

Claims

1. A real-time monitoring method for combustible combustion based on remote sensing technology, characterized in that, The method includes: Obtaining multi-temporal remote sensing data of the area to be monitored; Based on the multi-temporal remote sensing data, using a pre-trained fire point recognition model to identify, obtaining the area information where there are fire points and the types of fire points existing in the area information; Based on the multi-temporal remote sensing data, using a pre-trained hydrodynamic model combined with an intelligent wind field simulation algorithm to predict the smoke diffusion path, obtaining a dynamic smoke diffusion map including the predicted smoke diffusion path; According to the area information where there are fire points, the types of fire points existing in the area information, and the dynamic smoke diffusion map, predicting the fire development trend of the area to be monitored.

2. The method according to claim 1, wherein The using a pre-trained fire point recognition model to identify based on the multi-temporal remote sensing data, obtaining the area information where there are fire points and the types of fire points existing in the area information includes: Extracting the fire point features of each phase from the multi-temporal remote sensing data; According to the sequence of each phase, splicing the fire point features of each phase to obtain the spliced fire point features; Based on the spliced fire point features and the multi-temporal remote sensing data, using a pre-trained fire point recognition model to identify, obtaining the area information where there are fire points and the types of fire points existing in the area information.

3. The method according to claim 2, wherein The extracting the fire point features of each phase from the multi-temporal remote sensing data includes: Based on each phase of remote sensing data in the multi-temporal remote sensing data, performing feature extraction on the remote sensing image in the phase of remote sensing data to obtain the fire point features of the phase, and the fire point features include the fire point features of heat source temperature, flame brightness, and smoke concentration.

4. The method according to claim 2, wherein The fire point recognition model includes a position recognition sub-model and a type recognition sub-model; The using a pre-trained fire point recognition model to identify based on the spliced fire point features of each phase and the multi-temporal remote sensing data, obtaining the area information where there are fire points and the types of fire points existing in the area information includes: Based on the spliced fire point features of each phase, the remote sensing image and the local image in the multi-temporal remote sensing data, using the position recognition sub-model to obtain the area information where there are fire points; Based on the spliced fire point features of each phase, the area information where there are fire points, and the multi-temporal remote sensing data, using the type recognition sub-model to obtain the types of fire points existing in the area information.

5. The method according to claim 1, characterized in that, The using a pre-trained hydrodynamic model combined with an intelligent wind field simulation algorithm to predict the smoke diffusion path based on the multi-temporal remote sensing data, obtaining a dynamic smoke diffusion map including the predicted smoke diffusion path includes: Obtaining the ground meteorological data at the current time, and the ground meteorological data includes wind speed, humidity, and air flow; Based on the ground meteorological data and the multi-temporal remote sensing data, using a pre-trained hydrodynamic model to predict the wind field change, obtaining the wind field change situation in a future preset time period at the current time; Based on the wind field change situation in the preset future time period and the multi-temporal remote sensing data, use the intelligent wind field simulation algorithm to predict the smoke diffusion path, and obtain a dynamic smoke diffusion map including the predicted smoke diffusion path.

6. The method according to claim 1, wherein The types of the fire points include temporary fire points and / or continuous fire points; The predicting the fire development trend of the area to be monitored according to the area information where the fire points exist, the types of the fire points existing in the area information, and the dynamic smoke diffusion map includes: If the type of the fire points existing in the area information is continuous fire points, then according to the area information, the dynamic smoke diffusion map, and the terrain and combustible distribution conditions of the area to be monitored, predict the spreading range and development direction of the fire in the area to be monitored.

7. The method according to claim 1, wherein After predicting the fire development trend of the area to be monitored according to the area information where the fire points exist, the types of the fire points existing in the area information, and the dynamic smoke diffusion map, it further includes: Based on the location information of the area to be monitored, determine the population density and environmentally sensitive areas corresponding to the location information; Based on the population density, the risk weight of the population density, the environmentally sensitive areas, and the risk weight of the environmentally sensitive areas, perform weighted summation to determine the risk level corresponding to the fire development trend.

8. A real-time monitoring system for combustible material combustion based on remote sensing technology, characterized in that, It includes: An acquisition module, configured to acquire multi-temporal remote sensing data of the area to be monitored; A fire point identification module, configured to identify, based on the multi-temporal remote sensing data, using a pre-trained fire point identification model, to obtain the area information where the fire points exist, and the types of the fire points existing in the area information; A smoke diffusion simulation module, configured to predict the smoke diffusion path based on the multi-temporal remote sensing data, using a pre-trained hydrodynamic model combined with an intelligent wind field simulation algorithm, to obtain a dynamic smoke diffusion map including the predicted smoke diffusion path; A prediction module, configured to predict the fire development trend of the area to be monitored according to the area information where the fire points exist, the types of the fire points existing in the area information, and the dynamic smoke diffusion map.

9. An electronic device, characterized in that, It includes: At least one processor and a memory; The memory and the processor are connected by a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, implement the real-time monitoring method for combustible combustion based on remote sensing technology according to any one of claims 1-7.

10. A readable storage medium, characterized in that, There is an execution program stored thereon, and when the execution program is executed, implement the real-time monitoring method for combustible combustion based on remote sensing technology according to any one of claims 1-7.

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