Fire identification and early warning method based on weather radar

Abstract

The invention discloses a fire identification and early warning method based on a weather radar, and relates to the technical field of fire identification and early warning, and the method comprises the following steps: S1, inputting data in a weather radar body scanning mode into a system; s2, data quality control: filtering ground feature echoes by using a fuzzy logic method; step S3, echo image processing: smoke dust echoes are extracted from all echoes; carrying out expansion corrosion treatment on the smoke echo, and then carrying out communication block extraction; s4, preliminarily identifying the image; s5, performing depth identification based on the physical quantity; and step S6, final identification. The fire detection method is reasonable in design, can realize large-range detection, is all-weather, and is not liable to be interfered by external factors such as temperature, light and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of fire recognition and early warning, and more specifically to a method for fire recognition and early warning based on weather radar. Background Art

[0002] Fire is a disaster with strong suddenness and great destructiveness, posing a serious threat to people's lives and property safety. However, the existing fire detection technologies and devices currently, such as laser smoke detectors, temperature sensors, light-sensitive detectors, etc., still have many disadvantages and limitations in practical applications.

[0003] The laser smoke detector has a small detection range. For large-scale fire scenarios such as forest fires and large urban fires, it is often difficult to detect in time, resulting in a lag in early warning and missing the best fire extinguishing opportunity. In forest fires, the smoke spreads widely and is greatly affected by terrain and wind direction. The laser smoke detector can only cover a limited area, making it easy for the fire to spread and become a disaster before being discovered. In large urban fires, with high-rise buildings standing in great numbers and narrow streets, the installation position of the smoke detector is restricted, there are detection blind spots, and it is difficult to comprehensively monitor the smoke situation in the initial stage of the fire.

[0004] The temperature sensor is affected by temperature fluctuations, and the accuracy of its readings is greatly reduced. In a cold environment, low temperature may cause the sensor sensitivity to decline and it is not sensitive to the temperature change in the initial stage of the fire; while in a high-temperature environment, the sensor may generate errors due to overheating and misjudge normal temperature changes as fire signals, resulting in false alarms. In addition, the temperature sensor usually needs the fire to develop to a certain stage and the temperature to rise significantly to trigger an alarm, and it is difficult to respond in time at the budding stage of the fire.

[0005] The light-sensitive detector is extremely sensitive to light changes. With the slightest light brightness fluctuation, it is prone to misjudgment. Under strong light irradiation, such as direct sunlight in summer or large-scale lighting, the light-sensitive detector may misinterpret the light source change as a flame flicker, resulting in false alarms; while in a dim night or dark environment, its ability to identify weak flame signals is limited, and there may be missed alarms. This instability makes its reliability insufficient under different lighting conditions and it is difficult to accurately and stably detect fires.

[0006] In view of the above technical bottlenecks of these sensors or detectors, the existing equipment is stretched in practical applications. Therefore, a fire detection method that can achieve large-scale detection, all-weather operation, and is not easily interfered by external factors such as temperature and light is needed. Summary of the Invention

[0007] The purpose of the present invention is: to solve the above technical problems, the present invention provides a method for fire recognition and early warning based on weather radar.

[0008] The present invention specifically adopts the following technical solutions to achieve the above purpose: The present invention provides a method for fire recognition and early warning based on weather radar, comprising the following steps: Step S1: Input the data in the volume scan mode of the weather radar into the system; Step S2: Data quality control: There are ground clutter echoes in the volume scan data of the weather radar input in Step S1, and the ground clutter echoes will interfere with the subsequent processing of smoke echoes. The fuzzy logic method is used to filter out the ground clutter echoes; Step S3: Echo image processing: The data type in the volume scan mode of the weather radar is in the form of multi-elevation angles, multi-radials and multi-range bins; first, calculate the composite reflectivity based on the data in the volume scan mode of the weather radar, then perform echo image processing based on the composite reflectivity, eliminate the clutter with poor connectivity, and extract the smoke echoes from all the echoes; perform dilation and erosion processing on the smoke echoes, and then perform connected component extraction; Step S4: Preliminary image recognition: After extracting the connected components, when the smoke rises from the fire source, a "smoke plume" will be formed. The smoke plume is an upward turbulent flow formed by the buoyancy generated by combustion, and usually includes the lower combustion area; as the smoke rises, the smoke plume will become wider; and this characteristic of the smoke plume is reflected in the echo image as a narrow-band and long-strip smoke echo; For each connected component, two characteristic quantities, namely the aspect ratio and compactness, are proposed; Thresholds are respectively set for the aspect ratio and compactness. When the aspect ratio and compactness of a certain connected component simultaneously meet these two thresholds, it is marked as a type of target; If only based on the aspect ratio and compactness characteristics of the connected components, there will be more misjudgments; therefore, based on the image, the Hough line detection is used, and the line detection result is marked as a type of target; For each type of target, judge its intersection degree with the type of target, set an intersection threshold, and when the intersection degree between the two exceeds the set intersection threshold, the target is initially recognized as a preliminary fire target; Step S5: Depth recognition based on physical quantities: Among the preliminary fire targets, the preliminary positioning of the fire points has been realized; next, further screening of these fire points is required. The specific method is to calculate the change gradient of the echo area and the change of the echo top height of the preliminary fire targets, set the preset conditions corresponding to the echo area and the echo top height, and eliminate the fire points that do not meet the preset thresholds; Step S6: Final recognition: If the echo of a certain preliminary fire target simultaneously meets the preset conditions of the echo area and the two characteristic quantities, it is considered that the preliminary fire target is a fire target.

[0009] In one embodiment, in step S2, a method using fuzzy logic is used to filter the ground clutter echo. The characteristic quantities of the method using fuzzy logic include the vertical change of the echo intensity, the local change of the echo intensity, the radial change of the echo intensity, and the velocity value after median filtering.

[0010] Specifically, fuzzy logic refers to imitating the uncertain concept judgment and reasoning thinking mode of the human brain. For a description system that is unknown or cannot be determined for the model, as well as a control object with strong nonlinearity and large lag, fuzzy sets and fuzzy rules are applied for reasoning to express transitional boundaries or qualitative knowledge and experience, simulate the human brain mode, and implement fuzzy comprehensive judgment and reasoning to solve the rule-based fuzzy information problems that are difficult to handle by conventional methods. Fuzzy logic is good at expressing qualitative knowledge and experience with unclear boundaries. It uses the concept of membership function to distinguish fuzzy sets, process fuzzy relations, simulate the human brain to implement rule-based reasoning, and solve various uncertain problems caused by the logical break of the "law of excluded middle".

[0011] Fuzzy Logic is a mathematical method for processing uncertain and fuzzy information. It extends the scope of classical binary logic (true / false, 1 / 0) and allows variables to take any value in the interval [0,1], thus being closer to the fuzzy concepts in human thinking (such as "a bit hot", "relatively fast").

[0012] In one embodiment, in step S3, the specific method for processing the echo image is as follows: Step S31: After obtaining the composite reflectivity image, first perform dilation and erosion processing on the composite reflectivity image, and use a convolutional neural network with a convolution kernel of 3×3 during the dilation and erosion processing. The reason for this processing is that the soot echo is formed when the radar electromagnetic wave scatters when encountering soot particles in the air, and the radar receives the backscattering signals of these scattered waves. Therefore, the soot echo often shows discontinuous characteristics. After the erosion and dilation processing, the morphology of the soot echo will be better highlighted. Step S32: Extract connected components from the soot echo that has undergone the dilation and erosion processing in step S31, and remove the clutter with poor connectivity, thereby realizing echo partitioning.

[0013] Specifically, Connected Component Extraction is a basic technology in image processing used to identify and label the pixel regions (connected components) that are connected to each other in an image. It is most common in binary images and is widely used in fields such as object segmentation, morphological analysis, and OCR preprocessing. After steps S31 and S32, a large amount of clutter will be removed due to poor connectivity, which will be beneficial for identifying the soot echo.

[0014] In one embodiment, in step S4, for each connected component, the aspect ratio calculation formula is as follows: aspect_ratio = max(w, h) / min(w, h); In the formula, h is the length of the circumscribed rectangle of the connected component, w is the width of the circumscribed rectangle of the connected component, max(w, h) is the larger value of the length and the width, and min(w, h) is the smaller value of the length and the width; the greater the aspect ratio is than 1, the more slender the shape of the connected component is, that is, it is closer to a linear or strip shape; For each connected component, the compactness calculation formula is as follows: compactness = (4 × π × area) / (perimeter^2); In the formula, perimeter represents the contour perimeter of the connected component, area represents the area of the connected component, and π is the pi; the smaller the compactness is, the more slender the shape of the connected component is.

[0015] In one embodiment, in step S4, when the connected component is a type of target, the threshold values of the aspect ratio and the compactness are set to 3.47 and 0.26 respectively.

[0016] In one embodiment, in step S4, for each type of target, judge its intersection degree with the second type of target. The specific method is as follows: for each type of target (i.e., each connected component), calculate the ratio of the length that each second type of target (i.e., each straight line) passes through it to the diagonal length of the connected component. If the ratio exceeds 0.8, then initially determine that the target is a fire target.

[0017] In one embodiment, in step S5, the specific calculation method of the echo area is as follows: For each connected component, count the number of grid points with a combined reflectivity greater than 5 dbz within its consecutive time moments, and the result is used as the echo area within the connected component; if there is a trend that the echo area continuously increases for 5 consecutive time moments and the increment exceeds 5000, then it is considered that the connected component meets the echo area gradient change condition.

[0018] In one embodiment, in step S5, the specific calculation method of the echo top height is as follows: For each connected block, the echo top height at each moment is calculated, and then the difference between the two moments is taken to obtain the ET difference; during the development of the fire, the combustion particles are affected by the thermal uplift in the fire, and their movement trajectory presents an irregular up and down tumbling state. This phenomenon is reflected in the characteristics of the smoke echo as follows: the ET difference in meteorological echoes usually has a relatively stable vector displacement feature, because the movement of meteorological particles (such as raindrops, hail, etc.) is mainly affected by the atmospheric circulation, and their movement trajectory is relatively regular; while the ET difference of the fire smoke echo shows a large change and disorder in its vector displacement due to the complex movement of the combustion particles; this difference can be used as one of the important features to distinguish fire smoke echoes from meteorological echoes.

[0019] In one embodiment, a specific method for extracting the disorder feature of ET difference is as follows: after obtaining the ET difference at each moment, that is, a window with a length of 2 is used to slide to extract the ET difference, the two ET differences before and after the window sliding are used as A and B inputs, and the optical flow method is used to calculate the vector fields of A and B; after obtaining the vector field, a 15×15 grid matrix is ​​defined, and the vector field is gradually traversed from left to right and from top to bottom; for the vector field area selected by the grid matrix, the variance of the vector data in the vector field area is calculated separately ,like If it is greater than 50, the vector field region is judged to be an ET differential disorder region.

[0020] The beneficial effects of the present invention are as follows: The invention is reasonably designed and can realize a fire detection method with wide range detection, all-weather detection, and not easily disturbed by external factors such as temperature and light. The specific effects are as follows: In terms of large-scale detection: With the help of the volume scanning mode of the weather radar, this solution can scan and monitor large areas, which can effectively avoid the problem of delayed fire detection due to a small detection range. Whether it is a vast forest or a large urban area, the smoke echo generated by the fire can be captured in time at a long distance, achieving early warning of large-scale fire scenes, thereby effectively solving the problem of blind spots in the detection of traditional laser smoke detectors during large-scale fire monitoring, and providing strong support for fire prevention and control in large areas.

[0021] All-weather monitoring: The fire detection method based on weather radar is not restricted by light conditions. It can operate stably regardless of whether it is in direct sunlight during the day, in the dark of night, or in a complex environment with dim light. It can achieve continuous monitoring of fires and effectively make up for the lack of reliability of photosensitive detectors under different lighting conditions. It avoids missed reports or false alarms due to light changes, and ensures the stability and continuity of fire monitoring.

[0022] In terms of strong anti-interference ability: By using the method of fuzzy logic to filter out ground clutter echoes, and through image processing means such as dilation and erosion processing and connected component extraction, it can effectively remove clutter interference, making fire detection less susceptible to external factors such as temperature and light. This overcomes the problem that the temperature sensor has inaccurate readings due to temperature fluctuations, and the deficiency that the photosensitive detector is easily interfered by the fluctuations of light brightness and darkness, improves the accuracy of fire detection, reduces the occurrence of false alarms and missed alarms, and provides a more reliable basis for fire early warning.

[0023] In terms of accurately identifying fires: Through the calculation and analysis of feature quantities such as the aspect ratio and compactness of connected components, combined with means such as Hough line detection, it can preliminarily screen and determine suspected fire targets; further, in-depth identification is carried out based on physical quantities such as the change gradient of the echo area and the change of the echo top height, and finally the fire target is accurately determined. This effectively solves the problem that traditional fire detection equipment is difficult to respond in time at the budding stage of a fire, can accurately identify a fire in the initial stage of the fire, buys precious time for fire fighting operations, and reduces the losses caused by the fire. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0025] Figure 1 is a flowchart of a method for fire identification and early warning based on weather radar according to the present invention.

[0026] Figure 2 is a schematic diagram of the reflectivity before quality control.

[0027] Figure 3 is a schematic diagram of the reflectivity after quality control.

[0028] Figure 4 is a schematic diagram of the combined reflectivity.

[0029] Figure 5 is a schematic diagram of connected component extraction.

[0030] Figure 6 is a schematic diagram of the determination of a type of target.

[0031] Figure 7 is a schematic diagram of the determination of a type II target.

[0032] Figure 8 is a schematic diagram of initially identifying a fire target.

[0033] Figure 9It is a schematic diagram of the echo area change curve.

[0034] Figure 10 It is a schematic diagram of ET difference.

[0035] Figure 11 It is a schematic diagram of the optical flow vector field of meteorological echo ET difference.

[0036] Figure 12 It is a schematic diagram of the optical flow vector field of soot echo ET difference.

[0037] Figure 13 It is a schematic diagram of the final identification of fire targets. Detailed implementation manners

[0038] To make the technical problems, technical solutions and technical effects of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations.

[0039] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0040] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0041] Embodiment 1 As Figure 1 shown, this embodiment provides a fire recognition and early warning method based on weather radar, including the following steps: Step S1: Input the data in the weather radar volume scan mode into the system; Step S2: Data quality control: There are ground echoes in the weather radar volume scan data input in Step S1, and the ground echoes will interfere with the subsequent processing of soot echoes. The fuzzy logic method is used to filter out the ground echoes; the fuzzy logic method is used to filter the ground echoes. The characteristic quantities of the fuzzy logic method include the vertical change of echo intensity, the local change of echo intensity, the radial change of echo intensity, and the velocity value after median filtering, as Figure 2 and Figure 3as shown

[0042] Specifically, fuzzy logic refers to the way of imitating the uncertain concept judgment and reasoning thinking mode of the human brain. For a description system that is unknown or cannot be determined for the model, as well as a control object with strong nonlinearity and large lag, fuzzy sets and fuzzy rules are applied for reasoning, expressing transitional boundaries or qualitative knowledge and experience, simulating the human brain mode, implementing fuzzy comprehensive judgment, and reasoning to solve the rule-based fuzzy information problems that are difficult to handle by conventional methods. Fuzzy logic is good at expressing qualitative knowledge and experience with unclear boundaries. It uses the concept of membership function to distinguish fuzzy sets, process fuzzy relations, simulate the human brain to implement rule-based reasoning, and solve various uncertain problems caused by the logical break of the "law of excluded middle".

[0043] Fuzzy Logic is a mathematical method for dealing with uncertain and fuzzy information. It extends the scope of classical binary logic (true / false, 1 / 0), allowing variables to take any value in the interval [0,1], thus being closer to the fuzzy concepts in human thinking (such as "a bit hot", "relatively fast").

[0044] Step S3, echo image processing: The data type in the weather radar volume scan mode is in the form of multi-elevation, multi-radial, and multi-range bins; first, calculate the composite reflectivity based on the data in the weather radar volume scan mode, and then perform echo image processing based on the composite reflectivity, such as Figure 4 as shown, remove the clutter with poor connectivity, and extract the smoke and dust echo from all echoes; perform dilation and erosion processing on the smoke and dust echo, and then perform connected component extraction; the specific method of echo image processing is as follows: Step S31, after obtaining the composite reflectivity image, first perform dilation and erosion processing on the composite reflectivity image, and use a convolutional neural network with a convolution kernel of 3×3 during the dilation and erosion processing; the reason for this processing is that the smoke and dust echo is formed by the radar receiving the backscattered signals of the scattered waves when the radar electromagnetic wave encounters the smoke and dust particles in the air. Therefore, the smoke and dust echo often shows the characteristics of discontinuity; after the erosion and dilation processing, the morphology of the smoke and dust echo will be better highlighted; Step S32, perform connected component extraction on the smoke and dust echo after the dilation and erosion processing in Step S31, and remove the clutter with poor connectivity, so as to realize echo partitioning.

[0045] Specifically, Connected Component Extraction is a basic technology in image processing, used to identify and label the pixel regions (connected components) that are connected to each other in the image. It is most common in binary images and is widely used in fields such as object segmentation, morphological analysis, and OCR preprocessing. After Steps S31 and S32, a large amount of clutter will be removed due to poor connectivity, which will be beneficial to the identification of the smoke and dust echo, such asFigure 5 as shown

[0046] Step S4, preliminary image recognition: After extracting the connected components, when the flue gas rises from the fire source, a "smoke plume" will be formed. The smoke plume is an upward turbulent flow formed by the buoyancy generated by combustion and usually includes the lower combustion area; as the flue gas rises, the smoke plume will widen; and this characteristic of the smoke plume is reflected in the echo image as a narrow-band and long-strip soot echo; for each connected component, two characteristic quantities, aspect ratio and compactness, are proposed; For each connected component, the formula for calculating the aspect ratio is as follows: aspect_ratio = max(w, h) / min(w, h); In the formula, h is the length of the circumscribed rectangle of the connected component, w is the width of the circumscribed rectangle of the connected component, max(w, h) is the larger value of the length and width, and min(w, h) is the smaller value of the length and width; the larger the aspect ratio is greater than 1, the more slender the shape of the connected component is, that is, it is closer to a linear or strip shape; For each connected component, the formula for calculating the compactness is as follows: compactness = (4 × π × area) / (perimeter^2); In the formula, perimeter represents the contour perimeter of the connected component, area represents the area of the connected component, and π is the pi; the smaller the compactness, the more slender the shape of the connected component is.

[0047] Thresholds are set for the aspect ratio and compactness respectively. When the aspect ratio and compactness of a certain connected component simultaneously meet these two thresholds, it is marked as a type of target; when the connected component is a type of target, the thresholds of the aspect ratio and compactness are set to 3.47 and 0.26 respectively, as Figure 6 as shown

[0048] If only based on the aspect ratio and compactness characteristics of the connected components, there will be more misjudgments; therefore, based on the image, the Hough line detection is used, and the line detection results are marked as the second type of target, as Figure 7 as shown

[0049] For each type of target, judge its intersection degree with the second type of target, set an intersection threshold, and when the intersection degree of the two exceeds the set intersection threshold, then initially identify this target as a preliminary fire target. The specific method is as follows: for each type of target (i.e., each connected component), calculate the ratio of the length of each second type of target (i.e., each line) passing through it to the diagonal length of the connected component. If the ratio exceeds 0.8, then initially identify this target as a fire target, as Figure 8 as shown

[0050] Step S5, in-depth recognition based on physical quantities: In the preliminary fire targets, the preliminary positioning of the fire points has been achieved; next, further screening of these fire points is required. The specific method is to calculate the change gradient of the echo area and the change of the echo top height of the preliminary fire targets, set the corresponding preset conditions for the echo area and the echo top height, and eliminate the fire points that do not meet the preset thresholds; The specific calculation method of the echo area is as follows: The combined reflectivity refers to the maximum value of the reflectivity factor at all elevation angles at a specific position within the detection range of the radar. For each connected component, count the number of grid points with a combined reflectivity greater than 5 dbz within its consecutive time instants, and the result is used as the echo area within this connected component; if there is a trend that the echo area continuously increases for 5 consecutive time instants and the increment exceeds 5000, then it is considered that this connected component meets the echo area gradient change condition, as Figure 9 shown.

[0051] The specific calculation method of the echo top height is as follows: For each connected component, calculate the echo top height at each time instant, and then calculate the difference between two adjacent time instants to obtain the ET difference; during the fire development process, the combustion particles are affected by thermal uplift in the fire, and their movement trajectories show an irregular up-and-down tumbling state. The characteristics reflected on the smoke echo are as follows: The ET difference in the meteorological echo usually has a relatively stable vector displacement characteristic because the movement of meteorological particles (such as raindrops, hailstones, etc.) is mainly affected by the atmospheric circulation and their movement trajectories are relatively regular; while the ET difference of the fire smoke echo shows a large change and disorder in its vector displacement due to the complex movement of the combustion particles; this difference can be used as one of the important characteristics to distinguish the fire smoke echo from the meteorological echo, as Figure 10 shown.

[0052] The specific method for extracting the disorder characteristic of the ET difference is as follows: The ET differences between two adjacent time instants have been obtained before, that is, a window of length 2 is used to slide and extract the ET difference, and the ET differences of two adjacent time instants extracted by the window are used as the reference image A and the target image B respectively; Use the optical flow method to calculate the vector fields of the reference image A and the target image B; After obtaining the vector fields, define a 15×15 grid matrix and traverse the vector fields step by step from left to right and from top to bottom; For the vector field area selected by the grid matrix, calculate the variance of the vector data within the vector field area separately , if is greater than 50, then it is determined that this vector field area is an ET difference disorder area, as Figure 11 and Figure 12 .

[0053] Step S6, Final identification: If the echo of a certain preliminary fire target simultaneously meets the preset conditions of the echo area and the two characteristic quantities, then it is considered that this preliminary fire target is a fire target, asFigure 13 as shown

Claims

1. A fire recognition and early warning method based on weather radar, characterized in that, It includes the following steps: Step S1: Input the data in the weather radar volume scan mode into the system; Step S2: Data quality control: Use the fuzzy logic method to filter out ground clutter echoes; Step S3: Echo image processing: First calculate the composite reflectivity based on the data in the weather radar volume scan mode, then perform echo image processing based on the composite reflectivity, eliminate the clutter with poor connectivity, and extract the smoke and dust echoes from all echoes; Perform dilation and erosion processing on the smoke and dust echoes, and then perform connected component extraction; Step S4: Preliminary image recognition: After extracting the connected components, two feature quantities, namely the aspect ratio and compactness, are proposed for the connected components; Thresholds are set for the aspect ratio and compactness respectively. When the aspect ratio and compactness of a certain connected component simultaneously meet these two thresholds, it is marked as a type of target; If only based on the aspect ratio and compactness features of the connected components, there will be more misjudgments; Therefore, based on the image, Hough line detection is used, and the line detection result is marked as a type II target; For each type I target, judge its intersection degree with the type II target, set an intersection threshold. When the intersection degree between the two exceeds the set intersection threshold, the target is preliminarily identified as a preliminary fire target; Step S5: Depth recognition based on physical quantities: Among the preliminary fire targets, calculate the change gradient of the echo area and the change of the echo top height of the preliminary fire target, set the corresponding preset conditions for the echo area and the echo top height, and eliminate the fire points that do not meet the preset threshold; Step S6: Final recognition: If the echo of a certain preliminary fire target simultaneously meets the preset conditions of the echo area and the two feature quantities, then the preliminary fire target is considered a fire target.

2. The method for fire recognition and early warning based on weather radar according to claim 1, wherein In step S2, the fuzzy logic method is used to filter the ground clutter echoes. The feature quantities of the fuzzy logic method include the vertical change of the echo intensity, the local change of the echo intensity, the radial change of the echo intensity, and the velocity value after median filtering.

3. The method for fire recognition and early warning based on weather radar according to claim 1, characterized in that, In step S3, the specific method of echo image processing is as follows: Step S31: After obtaining the composite reflectivity image, first perform dilation and erosion processing on the composite reflectivity image. During the dilation and erosion processing, a convolutional neural network with a convolution kernel of 3×3 is used to highlight the morphology of the smoke and dust echoes; Step S32: Perform connected component extraction on the smoke and dust echoes processed by dilation and erosion in step S31, and eliminate the clutter with poor connectivity, so as to realize echo partitioning.

4. The method for fire recognition and early warning based on weather radar according to claim 3, characterized in that, In step S4, for each connected component, the aspect ratio calculation formula is as follows: aspect_ratio = max(w, h) / min(w, h); In the formula, h is the length of the circumscribed rectangle of the connected component, w is the width of the circumscribed rectangle of the connected component, max(w,h) is the larger value of the length and width, and min(w,h) is the smaller value of the length and width; The larger the aspect ratio, the more slender the shape of the connected component; For each connected component, the compactness calculation formula is as follows: compactness = (4 × π × area) / (perimeter^2); Wherein, perimeter represents the contour perimeter of the connected component, area represents the area of the connected component, and π is the ratio of the circumference of a circle to its diameter; the smaller the compactness, the more slender the shape of the connected component.

5. The method for fire recognition and early warning based on weather radar according to claim 4, characterized in that, In step S4, when the connected component is a type-I target, the threshold of the aspect ratio and the threshold of the compactness are set to 3.47 and 0.26 respectively.

6. The method for fire recognition and early warning based on weather radar according to claim 5, characterized in that, In step S4, for each type-I target, judge its intersection degree with the type-II target. The specific method is as follows: for each type-I target, calculate the ratio of the length that each type-II target passes through it to the diagonal length of the connected component. If the ratio exceeds 0.8, then initially determine that this target is a fire target.

7. The method for fire recognition and early warning based on weather radar according to claim 6, wherein, In step S5, the specific calculation method of the echo area is as follows: For each connected component, count the number of grid points with a combined reflectivity greater than 5 dbz within its consecutive time instants, and the result is used as the echo area within this connected component; if there exists an echo area that continuously shows an increasing trend for 5 consecutive time instants and the increment exceeds 5000, then it is considered that this connected component meets the echo area gradient change condition.

8. The method for fire recognition and early warning based on weather radar according to claim 6, characterized in that, In step S5, the specific calculation method of the echo top height is as follows: For each connected component, calculate the echo top height at each time instant, and then calculate the difference between two adjacent time instants to obtain the ET difference; during the fire development process, the combustion particles are affected by thermal lift in the fire, and their movement trajectories show an irregular up-and-down tumbling state. The characteristics reflected on the smoke echo are as follows: the ET difference in the meteorological echo usually has a relatively stable vector displacement characteristic because the movement of meteorological particles is mainly affected by the atmospheric circulation and its movement trajectory is relatively regular; while the ET difference of the fire smoke echo shows a large change and disorder in its vector displacement due to the complex movement of the combustion particles; this difference can be used as one of the important characteristics to distinguish the fire smoke echo from the meteorological echo.

9. A fire recognition and early warning method based on weather radar according to claim 8, characterized in that, The specific method for extracting the disorder characteristic of the ET difference is as follows: the ET differences between two adjacent time instants have been obtained before, that is, a window with a length of 2 is used to slide and extract the ET difference. The ET differences of two adjacent time instants extracted by the window are respectively used as the reference image A and the target image B; use the optical flow method to calculate the vector fields of the reference image A and the target image B; After obtaining the vector field, define a 15×15 grid matrix and gradually traverse the vector field from left to right and from top to bottom; For the vector field region selected by the grid matrix, calculate the variance of the vector data within the vector field region separately , if is greater than 50, then determine that the vector field region is an ET differential disorder region.

Images