Forest and grass fire simulation and fire fighting auxiliary method and system based on dynamic meteorological monitoring

Abstract

The present invention relates to the field of fire warning technology, and discloses a forest and grass fire simulation and fire fighting assistance method and system based on meteorological dynamic monitoring. The method determines the fire image and establishes a sample set by collecting meteorological dynamic monitoring images of the fire monitoring area and setting a temperature data threshold. At the same time, based on the fire image determined in the sample set, the fire point area corresponding to the fire image is calculated and the number of fire points is determined, and a fire intensity level model is established. At the same time, the fire monitoring area is divided into regions, a real scene map model is established, and the shortest path to each divided area is determined. The processed image data of the fire monitoring area collected is transmitted to the fire intensity level model and the real scene map model, and the fire intensity level is determined by the fire intensity level model. After the fire intensity level is determined, the fire fighting is dispatched through the calculated shortest path, thereby improving the efficiency of forest and grass fire simulation and fire fighting assistance.

Description

Technical Field

[0001] The present invention relates to the technical field of fire warning, and in particular to a forest and grass fire simulation and fire fighting auxiliary method and system based on dynamic meteorological monitoring. Background Art

[0002] Currently, forest and grass fire monitoring and fire point inspection are generally carried out through satellite monitoring, and the propagation of errors is difficult to control. Manual field verification has high accuracy but consumes a lot of manpower and material resources and is difficult to carry out on a large scale.

[0003] The existing Chinese patent application CN106485868A, the method reads the thermal image of the target area monitored at the current moment, calculates the suspicious fire source threshold according to the grayscale value of the thermal image and the monitoring data; and determines the existence of the suspicious fire source area in the target area through the calculated suspicious fire source threshold; at the same time, reads the thermal image and dynamic parameters of the target area monitored after the current moment, and obtains the fire information of the target area after judging whether the dynamic parameters meet the first preset condition; but due to the insufficient accuracy of thermal images alone, it has great limitations. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the existing technology, the present invention provides a forest and grass fire simulation and fire fighting auxiliary method and system based on dynamic meteorological monitoring, which has the advantages of high efficiency, accuracy and real-time, and solves the problem that the propagation of errors is difficult to control and difficult to carry out on a large scale.

[0006] (II) Technical solution

[0007] In order to solve the technical problem that the propagation of the above-mentioned error is difficult to control and difficult to carry out on a large scale, the present invention provides the following technical solutions:

[0008] This embodiment discloses a forest and grass fire simulation and fire fighting assistance method based on dynamic meteorological monitoring, which specifically includes the following steps:

[0009] S1. Collect meteorological dynamic monitoring images of the fire monitoring area, set a temperature data threshold of the meteorological dynamic monitoring images, determine the fire image based on the set temperature data threshold, and establish a sample set based on the determined fire image;

[0010] S2, based on the fire image determined in the sample set, calculate the fire point area corresponding to the fire image, determine the number of fire points and predict the direction of fire development;

[0011] S3, establishing a fire intensity level model based on the calculated fire point area corresponding to the fire image and the determined number of fire points;

[0012] S4, dividing the fire monitoring area into regions, establishing a real scene map model, and determining the shortest path to each divided area based on the established real scene map model;

[0013] S5. Collect image data of the fire monitoring area in real time, and process the collected image data of the fire monitoring area based on steps S2, S3 and S4, and transmit the calculated fire point area and the determined number of fire points corresponding to the image data of the fire monitoring area to the fire intensity level model and the real scene map model, and determine the fire intensity level through the fire intensity level model. After the fire intensity level is determined, the fire fighting is dispatched through the calculated shortest path to each divided area.

[0014] The present invention collects meteorological dynamic monitoring images of the fire monitoring area, sets a temperature data threshold of the meteorological dynamic monitoring image, determines the fire image based on the set temperature data threshold, and establishes a sample set. At the same time, based on the fire image determined in the sample set, the fire point area corresponding to the fire image is calculated and the number of fire points is determined, and a fire intensity level model is established. At the same time, the fire monitoring area is divided into regions, a real scene map model is established, and the shortest path to each divided area is determined. The processed and collected image data of the fire monitoring area is transmitted to the fire intensity level model and the real scene map model, and the fire intensity level is determined by the fire intensity level model. After the fire intensity level is determined, the fire fighting is dispatched by calculating the shortest path to each divided area, thereby improving the efficiency of forest and grass fire simulation and fire fighting assistance.

[0015] Preferably, the method of calculating the fire point area corresponding to the fire image, determining the number of fire points and predicting the fire development direction based on the fire image determined in the sample set includes the following steps:

[0016] S21, performing background separation on the fire image determined in the sample set, and determining the outline and number of the fire points;

[0017] S22, calculating the area of ​​the ignition point based on the determined ignition point contour;

[0018] S23, collecting wind speed data and wind direction data corresponding to the fire image, and predicting the direction of fire development according to the wind speed data and wind direction data;

[0019] The wind direction data corresponding to the fire image is set as the fire development direction, and the wind speed data corresponding to the fire image is set as the fire development speed.

[0020] Preferably, the process of performing background separation on the fire image determined in the sample set and determining the outline and number of fire points comprises the following steps:

[0021] S211, performing binarization processing on the determined fire image to obtain a binarized fire image;

[0022] Select the initial grayscale threshold Divide all pixels in the determined fire image into two categories and ;

[0023] set up Is less than or equal to the grayscale threshold Pixel category, is greater than the gray threshold Pixel category;

[0024] Set pixel class The grayscale mean is , pixel category The grayscale mean is , the global grayscale mean is ;

[0025] Set the pixels in the fire image to belong to the pixel category The probability of ; belongs to the pixel category The probability of ;

[0026] The binarization formula is as follows:

[0027] ;

[0028] ;

[0029] in, represents the binarization threshold;

[0030] The grayscale values ​​greater than the binarization threshold are set to 255, and the grayscale values ​​less than or equal to the binarization threshold are set to 0;

[0031] Summarize the pixels in the fire image after binary processing to obtain the fire image after binary processing;

[0032] S212, performing contour extraction on the fire image after the binarization process to obtain the contour of the fire image after background separation;

[0033] Create a one-dimensional array to record the grayscale values ​​of the eight neighborhoods around each pixel of the fire image after binary processing. When the grayscale values ​​of the eight neighborhoods around a pixel are the same as the grayscale value of the center point, the pixel is set to be inside the contour and deleted.

[0034] When the grayscale values ​​of the eight neighborhoods around a pixel are different from the grayscale value of the center point, the pixel is set at the edge of the contour and is retained;

[0035] Traverse and summarize each pixel point in the fire image after binary processing to obtain the outline of the fire image after background separation;

[0036] S213, setting a group of fire image contours after background separation as a group of fire point contours, and summarizing the fire point contours to determine the number of fire points.

[0037] Preferably, the step of calculating the ignition point area based on determining the ignition point contour comprises the following steps:

[0038] Suppose the longitude and latitude of two points on the earth are C(lat1,lng1) and D(lat2,lng2), and the radius of the earth is R , the distance between the two points is ;

[0039] ;

[0040] Based on the outline of the fire point, four points are selected to form a rectangle. The longitude and latitude coordinates of the four points are set to A (lat5, lng5), B (lat6, lng6), E (lat3, lng3), and F (lat4, lng4). According to the distance formula between two points, the distance between AB can be calculated as , the distance between EF is ;

[0041] The actual area of ​​the fire point ;

[0042] The size of the processed fire image is set to h×w, and the corresponding area size is ,

[0043] The actual size of each pixel on the processed fire image is ;

[0044] ;

[0045] Fire point area The number of image pixels multiplied by the actual size represented by each pixel is calculated as follows:

[0046] ;

[0047] in, It represents the number of pixels in the fire point contour of the processed fire image, and S represents the area of ​​the fire point.

[0048] The present invention performs binarization processing on the fire images determined in the sample set, completes the background separation of the processed fire images, further determines the outline and number of fire points, and simultaneously determines the area of ​​the fire points by calculation, thereby improving the accuracy of judging the area of ​​the fire points and the number of fire points corresponding to the fire images.

[0049] Preferably, the method of establishing a fire intensity level model based on the calculated fire point area corresponding to the fire image and the determined number of fire points comprises the following steps:

[0050] Level 1 fire intensity is defined as: the number of fire points is greater than or equal to 5, the area of ​​a single fire point is greater than or equal to 10 square meters, and the wind speed is greater than level 6;

[0051] Level 2 fire intensity is set as follows: the number of fire points is greater than or equal to 2 and less than 5, the area of ​​a single fire point is greater than or equal to 5 square meters and less than 10 square meters, and the wind speed is greater than level 3;

[0052] The fire intensity is set to level 3 as follows: the number of fire points is greater than or equal to 0 and less than 2, the area of ​​a single fire point is greater than or equal to 1 square meter and less than 5 square meters, and the wind speed is less than level 3.

[0053] The present invention establishes a fire intensity grade model by setting the number of fire points and the contour area of ​​a single fire point, completes the classification of fire intensity, and improves the reliability of forest and grass fire simulation and fire fighting assistance.

[0054] Preferably, the process of dividing the fire monitoring area into regions, establishing a real scene map model, and determining the shortest path to each divided area based on the established real scene map model comprises the following steps:

[0055] S41. Divide the fire monitoring area into regions and establish a real scene map model;

[0056] S42. Based on the established real-world scene map model, determine the shortest path to each divided area through a path planning algorithm.

[0057] Preferably, the fire monitoring area is divided into regions and a real scene map model is established, comprising the following steps:

[0058] Collect image information of the fire monitoring area, and divide the fire monitoring area into equal proportions based on the collected images;

[0059] The paths between the center points of each region after the division are called edges. The real-world map model consists of two sets: edges and center points of each region, represented by G(V,E); V represents the set of center points of each region, and E represents the set of edges.

[0060] Determine the path distance between the center points of each area in the real scene map model by calculating the Euclidean distance;

[0061] set up The coordinates of the point are , The coordinates of the point are ;

[0062] The Euclidean distance calculation formula is as follows:

[0063] ;

[0064] in, express Point and Path distance between points;

[0065] The calculated path distances between points in the real-scene map model are used as the weights of the edges in the real-scene map model, and the real-scene map model is determined based on two sets of edges and points in the real-scene map model and the weights of the edges.

[0066] Preferably, the method of determining the shortest path to each divided area by a path planning algorithm based on the established real scene map model comprises the following steps:

[0067] S421, setting two sets L and R, L is a set recording the center points of each region for which the shortest path has been found and the corresponding shortest path length, and R is a set recording the center points of each region for which the shortest path has not yet been found;

[0068] S422. Initially, set L to contain only the starting point , the set R includes all except the starting point The center points of each area outside the set R are recorded from the center point of each area to the starting point distance;

[0069] If the center point of the area is set to For edges that are not directly adjacent, set the center point and starting point of the area The distance between them is infinite;

[0070] S423. Find the center point of the area with the shortest path from the set R , and the center point of the region Add to the set L and remove the center point of the area from the array , update the center point of each region in the set R to the starting point distance;

[0071] S424, repeat step S423 until all vertices are traversed, and output the shortest path from the starting point to the center point of each area.

[0072] The present invention divides the fire monitoring area into regions and establishes a realistic scene map model. At the same time, by setting a set of center points of each region for which the shortest path has been calculated and a set of center points of each region for which the shortest path has not yet been calculated, the shortest path from the starting point to the center point of each region is determined by setting a starting point and continuously traversing, thereby improving the efficiency of forest and grass fire simulation and fire fighting assistance.

[0073] Preferably, the fire intensity level is determined by using a fire intensity level model, and after the fire intensity level is determined, the fire fighting is dispatched by calculating the shortest path to each divided area, which comprises the following steps:

[0074] When the fire intensity is set to level 1, it means it is extremely dangerous and materials need to be deployed immediately according to the calculated shortest path, and nearby firefighters need to be arranged to handle the fire based on the calculated shortest path;

[0075] When the fire intensity reaches level 2, it indicates a significant danger and needs to be reported. Firefighters nearby will be arranged to handle the fire based on the shortest path calculated.

[0076] When the fire intensity is set to level 3, indicating a slight danger, management personnel are arranged to handle it immediately based on the calculated shortest path.

[0077] The present invention collects image data of the fire monitoring area in real time, processes the real-time collected image data, and transmits the processed real-time image data to the fire intensity level model and the real-life scene map model. The fire intensity level is determined by the fire intensity level model. After the fire intensity level is determined, the fire fighting is scheduled by calculating the shortest path to each divided area, thereby improving the real-time performance of forest and grass fire simulation and fire fighting assistance.

[0078] This embodiment discloses a forest and grass fire simulation and fire fighting auxiliary system based on dynamic meteorological monitoring, including: a data acquisition module, a real scene map model module, a shortest path calculation module, a fire determination module, a fire intensity determination module and an alarm processing module;

[0079] The data acquisition module is used to collect fire images in the fire monitoring area;

[0080] The real scene map model module is used to divide the fire monitoring area into regions and establish a real scene map model;

[0081] The shortest path calculation module is used to find the shortest path through a path calculation method according to the established real scene map model;

[0082] The fire condition determination module is used to analyze the processed fire condition image of the fire monitoring area to determine the area of ​​the fire point;

[0083] The fire intensity determination module is used to determine the fire intensity according to the determined fire point area;

[0084] The alarm processing module is used to determine whether to issue an alarm according to the intensity of the fire.

[0085] (III) Beneficial effects

[0086] Compared with the prior art, the present invention provides a forest and grass fire simulation and fire fighting auxiliary method and system based on dynamic meteorological monitoring, which has the following beneficial effects:

[0087] 1. The invention collects meteorological dynamic monitoring images of the fire monitoring area, sets the temperature data threshold of the meteorological dynamic monitoring images, determines the fire image based on the set temperature data threshold, and establishes a sample set. At the same time, based on the fire image determined in the sample set, the fire point area corresponding to the fire image is calculated and the number of fire points is determined, and a fire intensity level model is established. At the same time, the fire monitoring area is divided into regions, a real scene map model is established, and the shortest path to each divided area is determined. The processed image data of the fire monitoring area is transmitted to the fire intensity level model and the real scene map model, and the fire intensity level is determined by the fire intensity level model. After the fire intensity level is determined, the fire fighting is dispatched by calculating the shortest path to each divided area, thereby improving the efficiency of forest and grass fire simulation and fire fighting assistance;

[0088] 2. The invention performs binarization processing on the fire images determined in the sample set, completes the background separation of the processed fire images, further determines the outline and number of fire points, and determines the area of ​​the fire points by calculation, thereby improving the accuracy of judging the area and number of fire points corresponding to the fire images;

[0089] 3. The invention establishes a fire intensity grade model by setting the number of fire points and the contour area of ​​a single fire point, completes the classification of fire intensity, and improves the reliability of forest and grass fire simulation and fire fighting assistance;

[0090] 4. The invention divides the fire monitoring area into regions and establishes a real scene map model. At the same time, by setting a set of center points of each region for which the shortest path has been found and a set of center points of each region for which the shortest path has not yet been found, the shortest path from the starting point to the center point of each region is determined by setting a starting point and continuously traversing, thereby improving the efficiency of forest and grass fire simulation and fire fighting assistance;

[0091] 5. The present invention collects image data of the fire monitoring area in real time, processes the real-time collected image data, and transmits the processed real-time image data to the fire intensity level model. The fire intensity level is determined by the fire intensity level model. After the fire intensity level is determined, the fire fighting is scheduled by calculating the shortest path to each divided area, thereby improving the real-time performance of forest and grass fire simulation and fire fighting assistance. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a schematic diagram of the structure of the forest and grass fire simulation and fire fighting auxiliary process of the present invention. DETAILED DESCRIPTION

[0093] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0094] Example 1 Please refer to Figure 1 This embodiment discloses a forest and grass fire simulation and fire fighting auxiliary method based on dynamic meteorological monitoring, which specifically includes the following steps:

[0095] S1. Collect meteorological dynamic monitoring images of the fire monitoring area, set a temperature data threshold of the meteorological dynamic monitoring images, determine the fire image based on the set temperature data threshold, and establish a sample set based on the determined fire image;

[0096] S2, based on the fire image determined in the sample set, calculate the fire point area corresponding to the fire image, determine the number of fire points and predict the direction of fire development;

[0097] Based on the fire image determined in the sample set, calculating the fire point area corresponding to the fire image, determining the number of fire points and predicting the direction of fire development includes the following steps:

[0098] S21, performing background separation on the fire image determined in the sample set, and determining the outline and number of the fire points;

[0099] S211, performing binarization processing on the determined fire image to obtain a binarized fire image;

[0100] Select the initial grayscale threshold Divide all pixels in the determined fire image into two categories and ;

[0101] set up Is less than or equal to the grayscale threshold Pixel category, is greater than the gray threshold Pixel category;

[0102] Set pixel class The grayscale mean is , pixel category The grayscale mean is , the global grayscale mean is ;

[0103] Set the pixels in the fire image to belong to the pixel category The probability of ; belongs to the pixel category The probability of ;

[0104] The binarization formula is as follows:

[0105] ;

[0106] ;

[0107] in, represents the binarization threshold;

[0108] The grayscale values ​​greater than the binarization threshold are set to 255, and the grayscale values ​​less than or equal to the binarization threshold are set to 0;

[0109] Summarize the pixels in the fire image after binary processing to obtain the fire image after binary processing;

[0110] S212, performing contour extraction on the fire image after the binarization process to obtain the contour of the fire image after background separation;

[0111] Create a one-dimensional array to record the grayscale values ​​of the eight neighborhoods around each pixel of the fire image after binary processing. When the grayscale values ​​of the eight neighborhoods around a pixel are the same as the grayscale value of the center point, the pixel is set to be inside the contour and deleted.

[0112] When the grayscale values ​​of the eight neighborhoods around a pixel are different from the grayscale value of the center point, the pixel is set at the edge of the contour and is retained;

[0113] Traverse and summarize each pixel point in the fire image after binary processing to obtain the outline of the fire image after background separation;

[0114] S213, setting a group of fire image contours after background separation as a group of fire point contours, and summarizing the fire point contours to determine the number of fire points;

[0115] S22, calculating the area of ​​the ignition point based on the determined ignition point contour;

[0116] Suppose the longitude and latitude of two points on the earth are C(lat1,lng1) and D(lat2,lng2), and the radius of the earth is R , the distance between the two points is ;

[0117] ;

[0118] Based on the outline of the fire point, four points are selected to form a rectangle. The longitude and latitude coordinates of the four points are set to A (lat5, lng5), B (lat6, lng6), E (lat3, lng3), and F (lat4, lng4). According to the distance formula between two points, the distance between AB can be calculated as , the distance between EF is ;

[0119] The actual area of ​​the fire point ;

[0120] The size of the processed fire image is set to h×w, and the corresponding area size is ,

[0121] The actual size of each pixel on the processed fire image is ;

[0122] ;

[0123] Fire point area The number of image pixels multiplied by the actual size represented by each pixel is calculated as follows:

[0124] ;

[0125] in, It represents the number of pixels in the fire point contour of the processed fire image, and S represents the area of ​​the fire point;

[0126] S23, collecting wind speed data and wind direction data corresponding to the fire image, and predicting the direction of fire development according to the wind speed data and wind direction data;

[0127] The wind direction data corresponding to the fire image is set as the fire development direction, and the wind speed data corresponding to the fire image is set as the fire development speed;

[0128] S3, establishing a fire intensity level model based on the calculated fire point area corresponding to the fire image and the determined number of fire points;

[0129] The fire intensity level model is established based on the calculated fire point area corresponding to the fire image and the determined number of fire points, including the following steps:

[0130] Level 1 fire intensity is defined as: the number of fire points is greater than or equal to 5, the area of ​​a single fire point is greater than or equal to 10 square meters, and the wind speed is greater than level 6;

[0131] Level 2 fire intensity is set as follows: the number of fire points is greater than or equal to 2 and less than 5, the area of ​​a single fire point is greater than or equal to 5 square meters and less than 10 square meters, and the wind speed is greater than level 3;

[0132] The fire intensity of level 3 is set as follows: the number of fire points is greater than or equal to 0 and less than 2, the area of ​​a single fire point is greater than or equal to 1 square meter and less than 5 square meters, and the wind speed is less than level 3;

[0133] S4, dividing the fire monitoring area into regions, establishing a real scene map model, and determining the shortest path to each divided area based on the established real scene map model;

[0134] Dividing the fire monitoring area into regions, establishing a real scene map model, and determining the shortest path to each divided area based on the established real scene map model includes the following steps:

[0135] S41. Divide the fire monitoring area into regions and establish a real scene map model;

[0136] Collect image information of the fire monitoring area, and divide the fire monitoring area into equal proportions based on the collected images;

[0137] The paths between the center points of each region after the division are called edges. The real-world map model consists of two sets: edges and center points of each region, represented by G(V,E); V represents the set of center points of each region, and E represents the set of edges.

[0138] Determine the path distance between the center points of each area in the real scene map model by calculating the Euclidean distance;

[0139] set up The coordinates of the point are , The coordinates of the point are ;

[0140] The Euclidean distance calculation formula is as follows:

[0141] ;

[0142] in, express Point and Path distance between points;

[0143] The calculated path distance between each point in the real scene map model is used as the weight of the edge in the real scene map model, and the real scene map model is determined based on the two sets of edges and points in the real scene map model and the weight of the edge;

[0144] S42, based on the established real-world scene map model, determining the shortest path to each divided area through a path planning algorithm;

[0145] Based on the established real-world scene map model, determining the shortest path to each divided area through a path planning algorithm includes the following steps:

[0146] S421, setting two sets L and R, L is a set recording the center points of each region for which the shortest path has been found and the corresponding shortest path length, and R is a set recording the center points of each region for which the shortest path has not yet been found;

[0147] S422. Initially, set L to contain only the starting point , the set R includes all except the starting point The center points of each area outside the set R are recorded from the center point of each area to the starting point distance;

[0148] If the center point of the area is set to For edges that are not directly adjacent, set the center point and starting point of the area The distance between them is infinite;

[0149] S423. Find the center point of the area with the shortest path from the set R , and the center point of the region Add to the set L and remove the center point of the area from the array , update the center point of each region in the set R to the starting point distance;

[0150] S424, repeat step S423 until all vertices are traversed, and the shortest path from the starting point to the center point of each area is output;

[0151] S5, collecting image data of the fire monitoring area in real time, and processing the collected image data of the fire monitoring area based on steps S2, S3 and S4, and transmitting the calculated fire point area and the determined number of fire points corresponding to the image data of the fire monitoring area to the fire intensity level model and the real scene map model, and determining the fire intensity level through the fire intensity level model. After the fire intensity level is determined, dispatching the fire fighting through the calculated shortest path to each divided area;

[0152] When the fire intensity is set to level 1, it means it is extremely dangerous and materials need to be deployed immediately according to the calculated shortest path, and nearby firefighters need to be arranged to handle the fire based on the calculated shortest path;

[0153] When the fire intensity reaches level 2, it indicates a significant danger and needs to be reported. Firefighters nearby will be arranged to handle the fire based on the shortest path calculated.

[0154] When the fire intensity is set to level 3, indicating a slight danger, management personnel are arranged to handle it immediately based on the calculated shortest path.

[0155] Embodiment 2 This embodiment discloses a forest and grass fire simulation and fire fighting auxiliary system based on dynamic meteorological monitoring, including: a data acquisition module, a real scene map model module, a shortest path calculation module, a fire determination module, a fire intensity determination module and an alarm processing module;

[0156] The data acquisition module is used to collect fire images in the fire monitoring area;

[0157] The real scene map model module is used to divide the fire monitoring area into regions and establish a real scene map model;

[0158] The shortest path calculation module is used to find the shortest path through a path calculation method according to the established real scene map model;

[0159] The fire condition determination module is used to analyze the processed fire condition image of the fire monitoring area to determine the area of ​​the fire point;

[0160] The fire intensity determination module is used to determine the fire intensity according to the determined fire point area;

[0161] The alarm processing module is used to determine whether to issue an alarm according to the intensity of the fire.

[0162] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for simulating forest and grass fires and assisting in fire fighting based on dynamic meteorological monitoring. It is characterized in that The following steps are involved: S1. Collect meteorological dynamic monitoring images of the fire monitoring area, set a temperature data threshold of the meteorological dynamic monitoring images, determine the fire image based on the set temperature data threshold, and establish a sample set based on the determined fire image; S2, based on the fire image determined in the sample set, calculate the fire point area corresponding to the fire image, determine the number of fire points and predict the direction of fire development; The method of calculating the fire point area corresponding to the fire image, determining the number of fire points and predicting the fire development direction based on the fire image determined in the sample set includes the following steps: S21, performing background separation on the fire image determined in the sample set, and determining the outline and number of the fire points; The method of performing background separation on the fire image determined in the sample set and determining the outline and number of fire points includes the following steps: S211, performing binarization processing on the determined fire image to obtain a binarized fire image; Select the initial grayscale threshold Divide all pixels in the determined fire image into two categories and ; set up is less than or equal to the grayscale threshold Pixel category, is greater than the gray threshold Pixel category; Set pixel class The grayscale mean is , pixel category The grayscale mean is , the global grayscale mean is ; Set the pixels in the fire image to belong to the pixel category The probability of , belongs to the pixel category The probability of ; The binarization formula is as follows: ； ； in, represents the binarization threshold; The grayscale values ​​greater than the binarization threshold are set to 255, and the grayscale values ​​less than or equal to the binarization threshold are set to 0; Summarize the pixels in the fire image after binary processing to obtain the fire image after binary processing; S212, performing contour extraction on the fire image after the binarization process to obtain the contour of the fire image after background separation; Create a one-dimensional array to record the grayscale values ​​of the eight neighborhoods around each pixel of the fire image after binary processing. When the grayscale values ​​of the eight neighborhoods around a pixel are the same as the grayscale value of the center point, the pixel is set to be inside the contour and deleted. When the grayscale values ​​of the eight neighborhoods around a pixel are different from the grayscale value of the center point, the pixel is set at the edge of the contour and is retained; Traverse and summarize each pixel point in the fire image after binary processing to obtain the outline of the fire image after background separation; S213, setting a group of fire image contours after background separation as a group of fire point contours, and summarizing the fire point contours to determine the number of fire points; S22, calculating the area of ​​the ignition point based on the determined ignition point contour; The calculation of the fire point area based on determining the fire point contour comprises the following steps: Suppose the longitude and latitude of two points on the earth are C(lat1,lng1) and D(lat2,lng2), the radius of the earth is R, and the distance between the two points is ; ； Based on the outline of the fire point, four points are selected to form a rectangle. The longitude and latitude coordinates of the four points are set to A (lat5, lng5), B (lat6, lng6), E (lat3, lng3), and F (lat4, lng4). According to the distance formula between two points, the distance between AB can be calculated as , the distance between EF is ; The actual area of ​​the fire point ; The size of the processed fire image is set to h×w, and the corresponding area size is , the actual size of each pixel on the processed fire image is ; ， ； Fire point area The number of image pixels multiplied by the actual size represented by each pixel is calculated as follows: ； in, It represents the number of pixels in the fire point contour of the processed fire image, and S represents the area of ​​the fire point; S23, collecting wind speed data and wind direction data corresponding to the fire image, and predicting the direction of fire development according to the wind speed data and wind direction data; The wind direction data corresponding to the fire image is set as the fire development direction, and the wind speed data corresponding to the fire image is set as the fire development speed; S3, establishing a fire intensity level model based on the calculated fire point area corresponding to the fire image and the determined number of fire points; S4, dividing the fire monitoring area into regions, establishing a real scene map model, and determining the shortest path to each divided area based on the established real scene map model; The method of dividing the fire monitoring area into regions, establishing a real scene map model, and determining the shortest path to each divided area based on the established real scene map model comprises the following steps: S41. Divide the fire monitoring area into regions and establish a real scene map model; The fire monitoring area is divided into regions and a real scene map model is established, which includes the following steps: Collect image information of the fire monitoring area, and divide the fire monitoring area into equal proportions based on the collected images; The paths between the center points of each region after the division are called edges. The real-world map model consists of two sets: edges and center points of each region, represented by G(V,E); V represents the set of center points of each region, and E represents the set of edges. Determine the path distance between the center points of each area in the real scene map model by calculating the Euclidean distance; set up The coordinates of the point are , The coordinates of the point are ; ； in, express Dot and Path distance between points; The calculated path distance between each point in the real scene map model is used as the weight of the edge in the real scene map model, and the real scene map model is determined based on the two sets of edges and points in the real scene map model and the weight of the edge; S42, based on the established real-world scene map model, determining the shortest path to each divided area through a path planning algorithm; S5. Collect image data of the fire monitoring area in real time, and process the collected image data of the fire monitoring area based on steps S2, S3 and S4, and transmit the calculated fire point area and the determined number of fire points corresponding to the image data of the fire monitoring area to the fire intensity level model and the real scene map model, and determine the fire intensity level through the fire intensity level model. After the fire intensity level is determined, the fire fighting is dispatched through the calculated shortest path to each divided area.

2. According to claim 1, a method for simulating forest and grass fire and assisting fire fighting based on dynamic meteorological monitoring, It is characterized in that The method of establishing a fire intensity level model based on the calculated fire point area corresponding to the fire image and the determined number of fire points comprises the following steps: Level 1 fire intensity is defined as: the number of fire points is greater than or equal to 5, the area of ​​a single fire point is greater than or equal to 10 square meters, and the wind speed is greater than level 6; Level 2 fire intensity is set as follows: the number of fire points is greater than or equal to 2 and less than 5, the area of ​​a single fire point is greater than or equal to 5 square meters and less than 10 square meters, and the wind speed is greater than level 3; The fire intensity is set to level 3 as follows: the number of fire points is greater than or equal to 0 and less than 2, the area of ​​a single fire point is greater than or equal to 1 square meter and less than 5 square meters, and the wind speed is less than level 3.

3. A method for simulating forest and grass fire and assisting fire fighting based on dynamic meteorological monitoring according to claim 2, It is characterized in that The method of determining the shortest path to each divided area by a path planning algorithm based on the established real scene map model comprises the following steps: S421, setting two sets L and R, L is a set recording the center points of each region for which the shortest path has been found and the corresponding shortest path length, and R is a set recording the center points of each region for which the shortest path has not yet been found; S422. Initially, set L to contain only the starting point , the set R includes all except the starting point The center points of each area outside the set R are recorded from the center point of each area to the starting point distance; If the center point of the area is set to For edges that are not directly adjacent, set the center point and starting point of the area The distance between them is infinite; S423. Find the center point of the area with the shortest path from the set R , and the center point of the region Add to the set L and remove the center point of the area from the array , update the center point of each region in the set R to the starting point distance; S434, repeat step S423 until all vertices are traversed, and output the shortest path from the starting point to the center point of each area.

4. A method for simulating forest and grass fire conditions and assisting in fire fighting based on dynamic meteorological monitoring according to claim 3, It is characterized in that The fire intensity level is determined by the fire intensity level model, and after the fire intensity level is determined, the fire fighting is dispatched by calculating the shortest path to each divided area, which includes the following steps: When the fire intensity is set to level 1, it means it is extremely dangerous and materials need to be deployed immediately according to the calculated shortest path, and nearby firefighters need to be arranged to handle the fire based on the calculated shortest path; When the fire intensity reaches level 2, it indicates a significant danger and needs to be reported. Firefighters nearby will be arranged to handle the fire based on the shortest path calculated. When the fire intensity is set to level 3, indicating a slight danger, management personnel are arranged to handle it immediately based on the calculated shortest path.

5. A system for implementing the forest and grass fire simulation and fire fighting assistance method based on dynamic meteorological monitoring as described in any one of claims 1 to 4, It is characterized in that include: Data acquisition module, real-world scene map model module, shortest path calculation module, fire situation determination module, fire intensity determination module, and alarm processing module; The data acquisition module is used to collect fire images in the fire monitoring area; The real scene map model module is used to divide the fire monitoring area into regions and establish a real scene map model; The shortest path calculation module is used to find the shortest path through a path calculation method according to the established real scene map model; The fire condition determination module is used to analyze the processed fire condition image of the fire monitoring area to determine the area of ​​the fire point; The fire intensity determination module is used to determine the fire intensity according to the determined fire point area; The alarm processing module is used to determine whether to issue an alarm according to the intensity of the fire.

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