System and method for identifying and predicting forest fire around power transmission line

Abstract

The invention provides a power transmission line surrounding forest fire identification and prediction system and method, and the system comprises a front-end monitoring device which comprises a holder system, a monitoring and photographing device, a millimeter wave radar, and a meteorological sensor. The monitoring and shooting device is used for collecting pictures of preset key fireproof areas around the power transmission line, and identifying and extracting pictures of fire areas; meteorological parameters collected by a meteorological sensor are obtained, a cellular automaton is used for analyzing, studying and judging the fire area picture, and the mountain fire moving trend is marked on the fire area picture; and acquiring forest fire position information and forest fire concentration information acquired by the millimeter wave radar, determining a linear distance between the ignition point and the power transmission line based on the rotation angle of the holder system, determining an alarm level according to the linear distance, and generating corresponding alarm information. The method can achieve the precise recognition and positioning of the distance between the ignition region and the power transmission line body, and improves the fire monitoring precision to a great extent based on the prediction of the fire development trend based on the cellular automaton.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line safety monitoring, and more particularly to a system and method for identifying and predicting wildfires around power transmission lines. Background Art

[0002] In recent years, with the development of electric power resources, more and more power transmission lines pass through mountainous areas. The unique topography and climatic conditions in these areas are very likely to cause forest fires, which can cause power line tripping at the very least, and even burn out towers at the worst, causing long-term and irreversible major power accidents.

[0003] Since manual inspections are not suitable for forest fire inspections of power transmission lines in complex terrain and vast areas, the commonly used methods for detecting forest fires around power transmission lines include ground patrols, observation towers, aerial patrol monitoring, satellite remote sensing monitoring, radar positioning monitoring, etc. Among them, satellite remote sensing has a wide monitoring range, high spatial resolution and sensitivity, and has been widely used in surface fire monitoring of power grids in various places. At present, automatic monitoring technologies such as laser radar, satellite remote sensing, and image processing based on computer vision can realize the identification and monitoring of forest fires around power transmission lines.

[0004] Among them, satellite remote sensing has its own problems of large positioning errors and high costs; and the use of traditional video monitoring systems for safety inspections of power transmission lines requires the installation of fixed cameras to transmit images back to the background in real time, and to monitor abnormal conditions of the lines in combination with computers and manual instructions, but it takes up additional bandwidth and relies on high-configuration servers. If a front-end intelligent monitoring device based on artificial intelligence recognition technology is used, although the accuracy of wildfire monitoring is greatly improved, it is easily affected by sunlight, the distance from the ignition point, etc., and it is still impossible to accurately grasp the fire situation of the wildfire. It can be seen that the existing power transmission line anti-wildfire devices and systems can only make basic judgments on whether a wildfire has occurred, and cannot obtain accurate measurements from the ignition point to the transmission line body and the subsequent development trend, which is easy to cause a waste of operation and maintenance resources. Summary of the invention In view of the problems existing in the prior art, the purpose of the present invention is to provide a system and method for identifying and predicting wildfires around transmission lines, which can realize the accurate identification and positioning of the fire area from the transmission line body, and at the same time predict the development trend of the fire based on cellular automata, which greatly improves the accuracy of fire monitoring and development trends, provides reliable alarm information to operation and maintenance management personnel for their decision-making and analysis, and avoids the waste of operation and maintenance resources as much as possible.

[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions: A system for identifying and predicting wildfires around power transmission lines includes a front-end monitoring device arranged around the power transmission lines, the front-end monitoring device including a pan-tilt system, a monitoring device, a millimeter-wave radar, and a meteorological sensor; the monitoring device, the millimeter-wave radar, and the meteorological sensor are fixedly installed on the pan-tilt system, and the millimeter-wave radar and the meteorological sensor are respectively connected to the monitoring device for data; The monitoring device is used for: Collect pictures of preset key fire prevention areas around the transmission lines, perform image preprocessing on the acquired pictures, and identify and extract pictures of the fire areas; Obtain meteorological parameters collected by meteorological sensors, use cellular automata to analyze and judge the fire area pictures, and mark the fire movement trend on the fire area pictures as the fire spread range prediction result; The wildfire location information and wildfire concentration information collected by the millimeter wave radar are obtained, and based on the rotation angle of the pan-tilt system, the straight-line distance between the fire point and the transmission line is determined, and the alarm level is determined according to the straight-line distance and the corresponding alarm information is generated.

[0006] Furthermore, the system also includes a back-end platform; the back-end platform is connected to the monitoring device data via a wireless network, and is used to obtain in real time the fire area pictures, wildfire spread range prediction results and alarm information generated by the monitoring device, and display them.

[0007] Furthermore, the monitoring device includes: a visible light camera, an infrared thermal imager, a data analysis module and a wireless communication module; The data analysis module is connected to the visible light camera, infrared thermal imager, PTZ system, and wireless communication module respectively, and the data analysis module is connected to the back-end platform data through the wireless communication module; Visible light camera, used to collect visible light images of preset key fire prevention areas around the transmission line and send them to the data analysis module; Infrared thermal imager, used to collect infrared images of preset key fire prevention areas around the transmission line and send them to the data analysis module; The data analysis module is used to: Adopt the built-in recognition model based on color space, set the discrimination basis of each component of color space, segment the visible light image, and obtain the segmentation effect diagram of visible light flame based on the discrimination basis; Based on the infrared image corresponding to the segmentation effect diagram of the visible light flame, grayscale processing is performed to generate a grayscale image, and the drawing image is segmented using a preset grayscale threshold to generate a boundary diagram of the visible light flame; Mathematical morphology processing is performed on the segmentation effect map and boundary map of the visible light flame to extract the fire area image and mark the fire range of the scene in the field of view.

[0008] Furthermore, the data analysis module is also used for: After the image of the fire area is generated, the millimeter-wave radar is directed toward the fire area by controlling the pan-tilt system to rotate, and the millimeter-wave radar is controlled to obtain the wildfire location information and wildfire concentration information of the fire area by scattering millimeter-wave signals; the fire area location information includes the distance, angle and fire area from the fire area to the front-end monitoring device; The world coordinate system is established with the rotation center of the PTZ system as the coordinate origin. The distance from the fire point to the line body is calculated based on the rotation angle of the PTZ system and the distance and angle from the fire area to the front-end monitoring equipment. Based on the distance and a preset level determination threshold, the fire level is determined and corresponding alarm information is generated. Furthermore, the meteorological sensor adopts a multi-factor micro-meteorological sensor, which is used to collect wind speed, wind direction, air temperature, and air humidity in real time and send them to the data analysis module.

[0009] Furthermore, the data analysis module is also used for: Divide the fire area image into multiple combustion units; Using the preset cellular automaton, the combustion state value of each combustion unit is calculated according to the wind speed, wind direction, air temperature, air humidity, and the pre-configured slope information and combustible type information of the fire area; According to the combustion state value of each combustion unit, the movement trend of the wildfire is marked on the picture of the fire area as the prediction result of the wildfire spread range.

[0010] Furthermore, dividing the fire area image into a plurality of combustion units includes: According to the pixel value of the fire area image, the fire area image is divided into N combustion units, each combustion unit includes n×n pixels; The combustion state value of each combustion unit is set to range from 0 to 1; when the combustion state value is 0, each pixel of the combustion unit is not in the combustion state; when the combustion state value is greater than 0 and less than 1, some pixels of the combustion unit are in the combustion state; when the combustion state value is equal to 1, all pixels of the combustion unit are in the combustion state; The fire area image is divided into M recognition areas, M<N; each recognition area includes m×m combustion units; In each identification area, the burning unit at the center is taken as the central unit. When the burning state value of any burning unit is 1, the wildfire spreading direction of the burning unit points to the central unit. In each identified region, the burning cells that share an edge with the central cell are considered as adjacent cells, and the burning cells that share a corner with the central cell are considered as secondary adjacent cells.

[0011] Furthermore, the cellular automaton is used to: Calculate the initial speed of wildfire spread, combustible adjustment coefficient, and slope adjustment coefficient based on wind speed, wind direction, air temperature, air humidity, and pre-configured slope information and combustible type information of the fire area; The wind adjustment coefficient is calculated by the wind speed and the angle between the wildfire spreading direction of each adjacent unit and the actual wind direction, and then the wildfire spreading speed of each adjacent unit is obtained; Through the spatial fusion of millimeter-wave radar and visible light camera, the spread speed of wildfire in each adjacent unit in the fire area image is obtained; Set the propagation time and set the propagation speed unchanged within the propagation time, traverse all combustion units and calculate their state values.

[0012] Furthermore, the front-end monitoring equipment also includes a battery power supply system, which is connected to the monitoring device and is provided with a photovoltaic component.

[0013] Correspondingly, the present invention also discloses a method for identifying and predicting wildfires around power transmission lines, comprising: Collect pictures of preset key fire prevention areas around power transmission lines; Perform image preprocessing on the acquired images, identify and extract the images of the fire area; Obtaining the wildfire location information and wildfire concentration information collected by the millimeter wave radar, and determining the straight-line distance between the fire point and the transmission line based on the rotation angle of the pan-tilt system, determining the alarm level according to the straight-line distance and generating corresponding alarm information; Obtain meteorological parameters collected by meteorological sensors, use cellular automata to analyze and judge the fire area pictures, and mark the fire movement trend on the fire area pictures as the fire spread range prediction result; The fire area pictures, wildfire spread prediction results and alarm information are sent to the back-end platform and displayed.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a system and method for identifying and predicting wildfires around transmission lines, based on visible light-infrared thermal imaging technology, for identifying and judging fire conditions around transmission lines, and using millimeter wave radar and pan-tilt system to achieve accurate measurement of the distance from the fire area to the transmission line body; through external multi-element micro-meteorological sensors to collect various meteorological elements, based on the embedded cellular automaton to estimate the direction of the fire, annotate the expected spread range in the monitoring pictures, and transmit the corresponding level of alarm information to the monitoring platform for decision-making analysis. The present invention realizes the accurate identification and positioning of the distance between the fire area and the transmission line body, and at the same time, based on the embedded cellular automaton to predict the development trend of the fire, it greatly improves the accuracy of fire monitoring and development situation, provides reliable alarm information to operation and maintenance management personnel for their decision-making analysis, and can avoid the waste of operation and maintenance resources as much as possible.

[0015] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0017] Figure 1 It is a system structure diagram of a specific implementation mode of the present invention.

[0018] Figure 2 It is a method flow chart of a specific implementation mode of the present invention.

[0019] In the figure, 1. Front-end monitoring equipment; 2. Back-end platform; 3. PTZ system; 4. Monitoring device; 5. Millimeter-wave radar; 6. Meteorological sensor; 7. Battery power supply system; 41. Visible light camera; 42. Infrared thermal imager; 43. Data analysis module; 44. Wireless communication module. DETAILED DESCRIPTION

[0020] The specific implementation of the present invention is described below with reference to the accompanying drawings.

[0021] like Figure 1A system for identifying and predicting wildfires around power transmission lines is shown, comprising a front-end monitoring device 1 and a back-end platform 2 arranged around the power transmission lines, wherein the front-end monitoring device 1 comprises a pan-tilt system 3, a monitoring device 4, a millimeter-wave radar 5, a meteorological sensor 6 and a battery power supply system 7; the monitoring device 4, the millimeter-wave radar 5, the meteorological sensor 6 and the battery power supply system 7 are fixedly installed on the pan-tilt system 3, and the monitoring device 4 is respectively connected to the pan-tilt system 3, the millimeter-wave radar 5, the meteorological sensor 6 and the battery power supply system 7; the monitoring device 4 is data-connected to the back-end platform 2 via a wireless network.

[0022] The monitoring device 4 is used for: Collect pictures of preset key fire prevention areas around the transmission lines, perform image preprocessing on the acquired pictures, and identify and extract pictures of the fire areas.

[0023] The meteorological parameters collected by the meteorological sensor 6 are obtained, and the fire area picture is analyzed and judged using the cellular automation, and the wildfire movement trend is marked on the fire area picture as the wildfire spread range prediction result.

[0024] The wildfire location information and wildfire concentration information collected by the millimeter wave radar 5 are obtained, and based on the rotation angle of the pan-tilt system, the straight-line distance between the fire point and the transmission line is determined, and the alarm level is determined according to the straight-line distance and the corresponding alarm information is generated.

[0025] The back-end platform 2 is used to obtain in real time the fire area pictures, wildfire spread range prediction results and alarm information generated by the monitoring device, and display them.

[0026] The battery power supply system 7 is provided with a photovoltaic module. It is powered by a combination of solar energy and batteries and adopts a low-power operation strategy to meet the long-term stable operation of the system. The meteorological sensor 6 adopts a multi-element micro-meteorological sensor. The meteorological sensor 6 is used to collect wind speed, wind direction, air temperature, and air humidity in real time and send them to the monitoring device 4.

[0027] In a specific implementation, the monitoring device 4 includes: a visible light camera 41, an infrared thermal imager 42, a data analysis module 43 and a wireless communication module 44; the data analysis module 43 is respectively connected to the visible light camera 41, the infrared thermal imager 42, the pan-tilt system 3, and the wireless communication module 44, and the data analysis module 43 is connected to the back-end platform 2 through the wireless communication module 44.

[0028] The visible light camera 41 is used to collect visible light images of preset key fire prevention areas around the transmission line and send them to the data analysis module 43.

[0029] The infrared thermal imager 42 is used to collect infrared images of preset key fire prevention areas around the transmission line and send them to the data analysis module 43.

[0030] Based on the above system architecture, the data analysis module 43 can realize the fire area image recognition function, fire level classification function and fire prediction function according to the various types of data obtained. The implementation process of the above functions is as follows: 1. Image recognition of fire area: Firstly, the built-in recognition model based on color space is used to set the discrimination basis of each component of color space, segment the visible light image, and obtain the segmentation effect diagram of visible light flame based on the discrimination basis; Then, based on the infrared image corresponding to the segmentation effect map of the visible light flame, grayscale processing is performed to generate a grayscale image, and the drawing image is segmented using a preset grayscale threshold to generate a boundary map of the visible light flame; finally, mathematical morphology processing is performed on the segmentation effect map and boundary map of the visible light flame to extract the image of the fire area and mark the fire range of the scene in the field of view.

[0031] Therefore, when performing image recognition of the fire area, the data analysis module 43 collects key fire prevention areas around the transmission line through the visible light camera 41 and the infrared thermal imager 42, and performs image preprocessing on the acquired images to obtain the fire area. For the visible light image, a recognition model based on color space is adopted, and the judgment basis of each component of the color space is set to segment the image to obtain the segmentation effect diagram of the visible light flame. For the infrared image, grayscale processing is performed, and the judgment threshold is set to segment the image, and then the images after the segmentation are subjected to mathematical morphology processing to obtain a better wildfire target extraction effect. Finally, the processed image is analyzed and judged. After the fire is confirmed to exist, the fire range of the scene in the field of view is marked in the picture collected by the visible light camera 41.

[0032] 2. Fire level classification: When the fire area image is generated, it means that the fire has been identified. At this time, the identification and classification of the fire level can be started.

[0033] First, the pan / tilt system 3 is controlled to rotate so that the millimeter-wave radar 5 faces the fire area, and the millimeter-wave radar 5 is controlled to obtain the wildfire location information and wildfire concentration information of the fire area by scattering millimeter-wave signals; wherein the fire area location information includes the distance, angle and fire area from the fire area to the front-end monitoring equipment.

[0034] Then, a world coordinate system is established with the rotation center of the pan / tilt system 3 as the coordinate origin, and the distance from the fire point to the line body is calculated based on the rotation angle of the pan / tilt system 3 and the distance and angle from the fire area to the front-end monitoring device 1.

[0035] Finally, based on the distance and a preset level determination threshold, the fire level is determined and corresponding alarm information is generated.

[0036] Therefore, when classifying the fire level, the millimeter wave radar 5 is first used to detect the smoke generated by the wildfire, and the wildfire location information and wildfire concentration information around the transmission line are obtained by scattering millimeter wave signals. Because the data analysis module 43 has a pan-tilt control function, it can obtain the rotation angle (including horizontal rotation angle and pitch rotation angle) by itself. A world coordinate system is established with the pan-tilt rotation center as the coordinate origin. The distance from the ignition point to the line body can be calculated by combining the pan-tilt rotation angle and the distance and angle of the ignition point measured by the millimeter wave radar. When the fire range exceeds the protection warning range of the transmission line body, the alarm level can be determined according to the distance, and the corresponding level of alarm information can be sent to the back-end platform 2 in a timely manner.

[0037] 3. Fire prediction: First, the image of the fire area is divided into multiple combustion units. Then, the preset cellular automaton is used to calculate the combustion state value of each combustion unit based on wind speed, wind direction, air temperature, air humidity, and the pre-configured slope information and combustible type information of the fire area. Finally, according to the combustion state value of each combustion unit, the wildfire movement trend is marked on the image of the fire area as the prediction result of the wildfire spread range.

[0038] The division of the combustion units is achieved in the following ways: First, the image of the burning area is divided into N combustion units according to its pixel value, and each combustion unit includes n×n pixels; the combustion state value of each combustion unit is set to range from 0 to 1; when the combustion state value is 0, each pixel point of the combustion unit is not in a burning state; when the combustion state value is greater than 0 and less than 1, some pixel points of the combustion unit are in a burning state; when the combustion state value is equal to 1, all pixel points of the combustion unit are in a burning state.

[0039] Furthermore, the fire area image is divided into M recognition areas, M < N; each recognition area includes m×m combustion units; in each recognition area, the combustion unit at the center is regarded as the central unit, and when the combustion state value of any combustion unit is 1, the wildfire spread direction of the combustion unit points to the central unit. It should be noted that in each recognition area, the combustion unit that shares an edge with the central unit is regarded as an adjacent unit, and the combustion unit that shares an angle with the central unit is regarded as a secondary adjacent unit.

[0040] In this system, three situations are mainly considered: 1) 2 adjacent units are completely burned; 2) 1 adjacent unit and 1 sub-adjacent unit are completely burned; 3) 2 adjacent units and 1 sub-adjacent unit are completely burned.

[0041] In addition, the data analysis module 43 uses cellular automata as an embedded micro-model to calculate the combustion state value of each combustion unit to achieve fire prediction.

[0042] The cellular automaton can calculate the initial speed of wildfire spread, the combustible adjustment coefficient, and the slope adjustment coefficient through the collected meteorological parameters, slope + combustible type (to be configured), etc.; and calculate the wind adjustment coefficient through the wind speed and the angle between the spread direction of each adjacent unit and the actual wind direction, and then obtain the wildfire spread speed of each adjacent unit. At the same time, with the help of the spatial fusion of the millimeter wave radar 5 and the visible light camera 41, the spread and diffusion speed of the wildfire in each adjacent unit in the image can be obtained. Set the spread time, assume that the spread speed does not change during this time period, traverse all burning units, and calculate their state values. According to the state of each burning unit, the movement trend of the wildfire is marked based on the visible light image recognition range.

[0043] It can be seen that the present invention discloses a system for identifying and predicting wildfires around transmission lines, which can realize the accurate identification and positioning of the fire area from the transmission line body, and at the same time predict the development trend of the fire based on cellular automata, which greatly improves the accuracy of fire monitoring and development trends, provides reliable alarm information to operation and maintenance management personnel for their decision-making and analysis, and avoids the waste of operation and maintenance resources as much as possible.

[0044] Correspondingly, such as Figure 2 As shown, the present invention also discloses a method for identifying and predicting wildfires around power transmission lines, comprising the following steps: S1: Collect pictures of preset key fire prevention areas around the transmission lines.

[0045] S2: Perform image preprocessing on the acquired images respectively, identify and extract the fire area images.

[0046] S3: Obtain the wildfire location information and wildfire concentration information collected by the millimeter wave radar, and determine the straight-line distance between the fire point and the transmission line based on the rotation angle of the pan-tilt system, determine the alarm level according to the straight-line distance and generate corresponding alarm information.

[0047] S4: Obtain meteorological parameters collected by meteorological sensors, use cellular automata to analyze and judge the pictures of the fire area, and mark the movement trend of the wildfire on the pictures of the fire area as the prediction result of the wildfire spread range.

[0048] S5: Send the fire area picture, wildfire spread prediction results and alarm information to the back-end platform and display them.

[0049] The specific implementation of the method for identifying and predicting wildfires around power transmission lines of this embodiment is basically the same as the specific implementation of the system for identifying and predicting wildfires around power transmission lines described above, and will not be described in detail here.

[0050] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium such as a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, etc., which can store program codes, including several instructions to enable a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The same and similar parts between the various embodiments in this specification can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0051] The present invention will be further described with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art may make various changes or modifications to the present invention, and these equivalent forms also fall within the scope limited by the application.

Claims

1. A system for identifying and predicting wildfires around power transmission lines, characterized in that: It includes front-end monitoring equipment installed around the transmission line, which includes a pan-tilt system, a monitoring device, a millimeter-wave radar and a meteorological sensor; the monitoring device, the millimeter-wave radar and the meteorological sensor are fixedly installed on the pan-tilt system, and the millimeter-wave radar and the meteorological sensor are respectively connected to the monitoring device for data; The monitoring device is used for: Collect pictures of preset key fire prevention areas around the transmission lines, perform image preprocessing on the acquired pictures, and identify and extract pictures of the fire areas; Obtain meteorological parameters collected by meteorological sensors, use cellular automata to analyze and judge the fire area pictures, and mark the fire movement trend on the fire area pictures as the fire spread range prediction result; The wildfire location information and wildfire concentration information collected by the millimeter wave radar are obtained, and based on the rotation angle of the pan-tilt system, the straight-line distance between the fire point and the transmission line is determined, and the alarm level is determined according to the straight-line distance and the corresponding alarm information is generated.

2. The system for identifying and predicting wildfires around power transmission lines according to claim 1, characterized in that: The system also includes a backend platform; The back-end platform is connected to the monitoring device data via a wireless network, and is used to obtain real-time fire area images, wildfire spread prediction results and alarm information generated by the monitoring device, and display them.

3. The system for identifying and predicting wildfires around power transmission lines according to claim 2, characterized in that: The monitoring device includes: a visible light camera, an infrared thermal imager, a data analysis module and a wireless communication module; The data analysis module is connected to the visible light camera, infrared thermal imager, PTZ system, and wireless communication module respectively, and the data analysis module is connected to the back-end platform data through the wireless communication module; Visible light camera, used to collect visible light images of preset key fire prevention areas around the transmission line and send them to the data analysis module; Infrared thermal imager, used to collect infrared images of preset key fire prevention areas around the transmission line and send them to the data analysis module; The data analysis module is used to: Adopt the built-in recognition model based on color space, set the discrimination basis of each component of color space, segment the visible light image, and obtain the segmentation effect diagram of visible light flame based on the discrimination basis; Based on the infrared image corresponding to the segmentation effect diagram of the visible light flame, grayscale processing is performed to generate a grayscale image, and the drawing image is segmented using a preset grayscale threshold to generate a boundary diagram of the visible light flame; Mathematical morphology processing is performed on the segmentation effect map and boundary map of the visible light flame to extract the fire area image and mark the fire range of the scene in the field of view.

4. The system for identifying and predicting wildfires around power transmission lines according to claim 3, characterized in that: The data analysis module is also used for: After the image of the fire area is generated, the millimeter-wave radar is directed toward the fire area by controlling the pan-tilt system to rotate, and the millimeter-wave radar is controlled to obtain the wildfire location information and wildfire concentration information of the fire area by scattering millimeter-wave signals; the fire area location information includes the distance, angle and fire area from the fire area to the front-end monitoring device; The world coordinate system is established with the rotation center of the PTZ system as the coordinate origin. The distance from the fire point to the line body is calculated based on the rotation angle of the PTZ system and the distance and angle from the fire area to the front-end monitoring equipment. Based on the distance and a preset level determination threshold, the fire level is determined and corresponding alarm information is generated.

5. The system for identifying and predicting wildfires around power transmission lines according to claim 4, characterized in that: The meteorological sensor adopts a multi-factor micro-meteorological sensor, which is used to collect wind speed, wind direction, air temperature, and air humidity in real time and send them to the data analysis module.

6. The system for identifying and predicting wildfires around power transmission lines according to claim 5, characterized in that: The data analysis module is also used for: Divide the fire area image into multiple combustion units; Using the preset cellular automaton, the combustion state value of each combustion unit is calculated according to the wind speed, wind direction, air temperature, air humidity, and the pre-configured slope information and combustible type information of the fire area; According to the combustion state value of each combustion unit, the movement trend of the wildfire is marked on the picture of the fire area as the prediction result of the wildfire spread range.

7. The system for identifying and predicting wildfires around power transmission lines according to claim 6, characterized in that: The step of dividing the fire area image into a plurality of combustion units includes: According to the pixel value of the fire area image, the fire area image is divided into N combustion units, each combustion unit includes n×n pixels; The combustion state value of each combustion unit is set to range from 0 to 1; when the combustion state value is 0, each pixel of the combustion unit is not in the combustion state; when the combustion state value is greater than 0 and less than 1, some pixels of the combustion unit are in the combustion state; when the combustion state value is equal to 1, all pixels of the combustion unit are in the combustion state; The fire area image is divided into M recognition areas, M<N; each recognition area includes m×m combustion units; In each identification area, the burning unit at the center is taken as the central unit. When the burning state value of any burning unit is 1, the wildfire spreading direction of the burning unit points to the central unit. In each identified region, the burning cells that share an edge with the central cell are considered as adjacent cells, and the burning cells that share a corner with the central cell are considered as secondary adjacent cells.

8. The system for identifying and predicting wildfires around power transmission lines according to claim 7, characterized in that: The cellular automaton is used to: Calculate the initial speed of wildfire spread, combustible adjustment coefficient, and slope adjustment coefficient based on wind speed, wind direction, air temperature, air humidity, and pre-configured slope information and combustible type information of the fire area; The wind adjustment coefficient is calculated by the wind speed and the angle between the wildfire spreading direction of each adjacent unit and the actual wind direction, and then the wildfire spreading speed of each adjacent unit is obtained; Through the spatial fusion of millimeter-wave radar and visible light camera, the spread speed of wildfire in each adjacent unit in the fire area image is obtained; Set the propagation time and set the propagation speed unchanged within the propagation time, traverse all combustion units and calculate their state values.

9. The system for identifying and predicting wildfires around power transmission lines according to claim 8, characterized in that: The front-end monitoring equipment also includes a battery power supply system, which is connected to the monitoring device and is provided with a photovoltaic component.

10. A method for identifying and predicting wildfires around power transmission lines, characterized in that: The method adopts the system for identifying and predicting wildfires around power transmission lines according to any one of claims 1 to 9, and the method comprises: Collect pictures of preset key fire prevention areas around power transmission lines; Perform image preprocessing on the acquired images, identify and extract the images of the fire area; Obtaining the wildfire location information and wildfire concentration information collected by the millimeter wave radar, and determining the straight-line distance between the fire point and the transmission line based on the rotation angle of the pan-tilt system, determining the alarm level according to the straight-line distance and generating corresponding alarm information; Obtain meteorological parameters collected by meteorological sensors, use cellular automata to analyze and judge the fire area pictures, and mark the fire movement trend on the fire area pictures as the fire spread range prediction result; The fire area pictures, wildfire spread prediction results and alarm information are sent to the back-end platform and displayed.

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