Power overhead line corridor fire monitoring method and related equipment

Abstract

The invention discloses a power overhead line corridor fire monitoring method and related equipment, relates to the field of power grid operation and maintenance, and mainly aims to solve the problem that power overhead line corridor fire monitoring excessively depends on intelligent electronic equipment. The method comprises the steps that a fire danger grade index is determined based on a meteorological monitoring device, the fire danger grade index is used for determining an inspection tour index, and the inspection tour index comprises an inspection tour frequency and an inspection tour monitoring point interval; patrol is carried out based on the patrol indexes to obtain flame space information, and the flame space information is used for guiding start and stop of a wind measurement laser radar; frequency change of scattered light is obtained based on the wind measurement laser radar, and the frequency change of the scattered light is used for feeding back the fire spreading trend. The method is used for the fire monitoring process of the overhead power line corridor.

Description

Technical Field

[0001] The present invention relates to the field of power grid operation and maintenance, and in particular to a fire monitoring method for a power overhead line corridor and related equipment. Background Art

[0002] With the development of large and super-large cities, urban buildings, transportation, and power grids extend outward, and factory areas are gradually located in the urban-rural fringe areas of cities. The 110kV, 35kV, 10kV and other lines of enterprises often cross multiple farmlands, roads, construction sites, small and micro enterprises, and even cross multiple administrative divisions. With the rapid development of cities, the environment around factory areas is becoming increasingly complex, and the operating conditions of the company's power lines are becoming increasingly worse. External force damage and fires in the transmission corridor often affect the safe operation of the line. Among them, the proportion of tripping accidents caused by fires in the transmission line corridor area has increased year by year. At the same time, due to the fire, the insulation level of the line dropped sharply, the success rate of reclosing was low, and the power outage time was long, which seriously threatened the stable operation of the power grid. Therefore, early warning of fires in this situation is of great significance to scientific dispatching decisions.

[0003] However, the more widely used fire prevention measures nowadays include video surveillance systems, remote sensing satellites, infrared thermal imaging, etc. Relying on a single technology to detect fire information often results in missed reports and false reports, and the use of remote sensing patrols is expensive and has a long patrol cycle, and cannot cope with sudden fires. Summary of the invention

[0004] In view of the above problems, the present invention provides a method and related equipment for monitoring fire in a power overhead line corridor, the main purpose of which is to solve the problem that fire monitoring in a power overhead line corridor is overly dependent on intelligent electronic devices.

[0005] In order to solve at least one of the above technical problems, in a first aspect, the present invention provides a method for monitoring fire in a power overhead line corridor, the method comprising:

[0006] Determine a fire risk level index based on a meteorological monitoring device, wherein the fire risk level index is used to determine a patrol index, and the patrol index includes a patrol frequency and a patrol monitoring point interval;

[0007] Patrolling based on the patrol index to obtain flame space information, wherein the flame space information is used to guide the start and stop of the wind laser radar;

[0008] The frequency change of scattered light is obtained based on the wind measuring laser radar, wherein the frequency change of the scattered light is used to feedback the fire spreading trend.

[0009] Optionally, determining the fire risk level index based on the meteorological monitoring device includes:

[0010] Collect real-time meteorological data based on meteorological monitoring devices;

[0011] The fire risk index is determined by the real-time meteorological data based on the following formula:

[0012] I FFDI =U×C r ×C s

[0013] U=f(V)+f(T)+f(r RH )+f(M)

[0014] Among them, I FFDI is the fire risk index; Cr is the rainfall correction factor; Cs is the snow correction factor; V is the wind speed; T is the temperature, r RH is the relative humidity and M is the drought level.

[0015] Optionally, patrolling based on the patrol indicator to obtain flame space information includes:

[0016] Controlling the video monitoring device and the infrared thermal imaging device to patrol based on the patrol index to obtain visible light images and infrared thermal imaging images;

[0017] Flame space information is acquired based on the visible light image and the infrared thermal imaging image.

[0018] Optionally, the above method further includes:

[0019] The infrared thermal imaging image is trained based on the YOLOv5 deep learning model algorithm to obtain a fire detection model, wherein the fire detection model is used to determine the fire area in the infrared thermal imaging image;

[0020] The camera intrinsic parameters of the visible light image are calibrated based on the Zhang Zhengyou calibration method to establish an image coordinate system, wherein the image coordinate system is used to perform coordinate transformation on the fire detection result to determine the fire area in the visible light image.

[0021] Optionally, the obtaining of flame space information based on the visible light image and the infrared thermal imaging image includes:

[0022] Determine a comparison result between the fire area in the infrared thermal imaging image and the fire area in the visible light image based on a threshold segmentation method;

[0023] When the comparison results are consistent, the flame space information is determined.

[0024] Optionally, the above method further includes:

[0025] In the case where the flame space information indicates the presence of a fire, starting a wind measuring laser radar;

[0026] The fire spreading trend is determined based on the wind laser radar.

[0027] Optionally, the acquiring of frequency changes of scattered light based on the wind-measuring laser radar, wherein the frequency changes of the scattered light are used to feedback the fire spreading trend, includes:

[0028] Controlling the wind measuring laser radar to emit a laser beam;

[0029] When the laser beam forms scattered light, obtaining a frequency change of the scattered light;

[0030] determining wind speed based on the frequency change of the scattered light;

[0031] The fire spread tendency is determined based on wind speed.

[0032] In a second aspect, an embodiment of the present invention further provides a fire monitoring device for a power overhead line corridor, comprising:

[0033] A determination unit, configured to determine a fire risk level index based on a meteorological monitoring device, wherein the fire risk level index is used to determine a patrol index, and the patrol index includes a patrol frequency and a patrol monitoring point interval;

[0034] A first acquisition unit, configured to acquire flame space information by patrolling based on the patrol index, wherein the flame space information is used to guide the start and stop of the wind laser radar;

[0035] The second acquisition unit acquires the frequency change of scattered light based on the wind measuring laser radar, wherein the frequency change of scattered light is used to feedback the fire spreading trend.

[0036] In order to achieve the above-mentioned purpose, according to the third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the above-mentioned program is executed by a processor, the steps of the above-mentioned method for monitoring fire conditions in power overhead line corridors are implemented.

[0037] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present invention, there is provided an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the above-mentioned processor is used to call the program instructions in the above-mentioned memory to execute the steps of the above-mentioned method for monitoring fire conditions in the corridor of overhead power lines.

[0038] Through the above technical scheme, the fire monitoring method and related equipment for the power overhead line corridor provided by the present invention solve the problem that the fire monitoring of the power overhead line corridor is too dependent on intelligent electronic devices. The present invention determines the fire risk level index based on the meteorological monitoring device, wherein the fire risk level index is used to determine the patrol index, and the patrol index includes the patrol frequency and the patrol monitoring point interval; patrols based on the patrol index to obtain flame space information, wherein the flame space information is used to guide the start and stop of the wind laser radar; obtains the frequency change of scattered light based on the wind laser radar, wherein the frequency change of scattered light is used to feedback the fire spread trend. In the above scheme, by constructing a distributed multi-feature quantity large-scale steel joint enterprise power overhead line corridor fire monitoring and early warning system, the meteorological monitoring devices, video monitoring devices, infrared thermal imaging devices, Doppler wind laser radar devices, etc. distributed at various points are used to detect the feature quantity information of the fire source at different stages, and the early accurate warning of the fire and the spread trend judgment are realized by fusion analysis and diagnosis of multiple feature quantity data.

[0039] Correspondingly, the power overhead line corridor fire monitoring device, equipment and computer-readable storage medium provided in the embodiments of the present invention also have the above-mentioned technical effects.

[0040] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0042] Figure 1 A schematic flow chart of a method for monitoring fire conditions in a power overhead line corridor provided by an embodiment of the present invention is shown;

[0043] Figure 2 A schematic block diagram showing the composition of a fire monitoring device for a power overhead line corridor provided by an embodiment of the present invention is shown;

[0044] Figure 3 A schematic block diagram showing the composition of an electronic device for monitoring fire conditions in a corridor of an overhead power line provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0045] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0046] In order to solve the problem that fire monitoring in power overhead line corridors is overly dependent on intelligent electronic devices, an embodiment of the present invention provides a method for monitoring fire in power overhead line corridors, such as Figure 1 As shown, the method includes:

[0047] S101, determining a fire risk level index based on a meteorological monitoring device, wherein the fire risk level index is used to determine a patrol index, and the patrol index includes a patrol frequency and a patrol monitoring point interval;

[0048] The above step S101 further includes S1011:

[0049] S1011. Collecting real-time meteorological data based on a meteorological monitoring device;

[0050] The fire risk index is determined by the real-time meteorological data based on the following formula:

[0051] I FFDI =U×C r ×C s

[0052] U=f(V)+f(T)+f(r RH )+f(M)

[0053] Among them, I FFDI is the fire risk index; Cr is the rainfall correction factor; Cs is the snow correction factor; V is the wind speed; T is the temperature, r RH is the relative humidity and M is the drought level.

[0054] Specifically, the present application uses a meteorological monitoring device to collect data on meteorological elements such as temperature, humidity, wind speed, wind direction, and rainfall in real time, and the data is uploaded to an intelligent fusion device through a wireless WI FI network for fire risk level assessment. Meteorological drought comprehensive index MCI>-0.5, calculation method see Chapter 9 of GB / T20481-2017), the number of consecutive days without precipitation when the drought is mild or above (MCI<-0.5). The fire risk level is divided into three levels: high, medium, and low. According to different fire risk levels, the intelligent fusion device controls the video surveillance device and the infrared thermal imaging device to patrol the surrounding conditions of the monitoring point at different patrol frequencies and intervals.

[0055] Based on the above scheme, the relationship between meteorological factors such as temperature, humidity, wind speed, wind direction and fire risk level is analyzed by using statistical and machine learning methods through fire risk level warning based on meteorological factors. By constructing a mathematical model, the impact of these factors on the fire risk level is quantified, and the fire risk level under different meteorological conditions is predicted.

[0056] S102, patrolling based on the patrol index to obtain flame space information, wherein the flame space information is used to guide the start and stop of the wind laser radar;

[0057] The above step S102 also includes S1021, S1022, S1023 and S1024:

[0058] S1021. Control the video monitoring device and the infrared thermal imaging device to patrol based on the patrol index to obtain visible light images and infrared thermal imaging images; and obtain flame space information based on the visible light images and the infrared thermal imaging images.

[0059] Visible light images and infrared thermal imaging images are uploaded synchronously to the intelligent fusion device through the video monitoring device and the infrared thermal imaging device to conduct secondary detection of the flame and obtain the flame space information.

[0060] S1022, training the infrared thermal imaging image based on the YOLOv5 deep learning model algorithm to obtain a fire detection model, wherein the fire detection model is used to determine the fire area in the infrared thermal imaging image;

[0061] The camera intrinsic parameters of the visible light image are calibrated based on the Zhang Zhengyou calibration method to establish an image coordinate system, wherein the image coordinate system is used to perform coordinate transformation on the fire detection result to determine the fire area in the visible light image.

[0062] 1) Using the YOLOv5 deep learning model algorithm, the image data and annotation information are scaled and input into the network to train a wildfire disaster detection model.

[0063] (1) Collect pictures of wildfires around power transmission lines.

[0064] (2) Annotate the image and generate a corresponding XML file. The annotation information includes the image width, height, target name, and target bounding rectangle coordinates.

[0065] (3) Using the deep learning model yo l ov5 neural network model, the model input image size is set to 640×640, and the output is 3 layers of feature layers with different sizes. The labeled images are trained to obtain a wildfire disaster detection model.

[0066] 2) Use Zhang Zhengyou's calibration method to calibrate the intrinsic parameters of the visible light camera, establish the image coordinate system, and convert the coordinates of the flame detection results according to the camera's geometric imaging characteristics.

[0067] (1) Scale the image captured by the camera to obtain the model input image

[0068] (2) Input the image into the Yo L OV5 model trained in step 3 to obtain three 4-dimensional output feature layers:

[0069] output0:(1,20,20,21)

[0070] output1:(1,40,40,21)

[0071] output2:(1,80,80,21)

[0072] in:

[0073] The first dimension is the number of images in the group. Since the number of images detected is 1, the value is 1;

[0074] The second and third dimensions correspond to three preset anchor boxes of different sizes, which can detect targets of different sizes. The middle dimension nw,nh of output0 is 20, indicating a large anchor, and the middle dimension nw,nh of output2 is 80, indicating a small anchor.

[0075] The fourth dimension: Each anchor has 3 anchors with different aspect ratios, which can predict targets with different length and width ratios. Each anchor predicts 7 dimensions of information:

[0076] (t x ,t y ,t w ,t h ,conf,class_prob)

[0077] t x ,t y Is the offset of the center coordinate of the predicted box relative to the current anchor

[0078] t w ,t h It is the offset of the predicted box width and height relative to the current anchor width and height

[0079] conf indicates the probability that the current anchor is responsible for prediction

[0080] class_prob is the score of each category of the detected target. The number of categories detected in this scheme is 2. Each anchor predicts a 7-dimensional vector. Each anchor has 3 anchors, so each anchor outputs a 21-dimensional vector.

[0081] (3) Set the confidence threshold conf_threshold and filter out all detection results whose threshold conf is greater than conf_threshold.

[0082] (4) The predicted box coordinate offset is converted into the relative coordinates of the feature layer of the image:

[0083]

[0084] Where t x ,t y ,t w ,t h The predicted box center coordinates and width and height offsets for each anchor, c x ,c y ,p w ,p h The coordinates of the grid of each anchor's feature layer and the preset width and height of the anchor, b x ,b y ,b w ,b h The center coordinates, width and height of the predicted box relative to the current feature layer

[0085] (5) The relative coordinates of the center height and width of the predicted frame feature layer are converted to the relative coordinates of the center width and height of the original image

[0086]

[0087] (6) Where nw, nh are the width and height of the original image after data enhancement, grid w ,grid h is the number of grids in the current feature layer, r x ,r y ,r w ,r h The relative coordinates of the predicted box’s center width and height relative to the original image.

[0088] (7) The relative coordinates of the center width and height of the original image of the prediction box are converted to the real coordinates of the upper left corner and lower right corner

[0089]

[0090] Where w,h is the width and height of the original image, T xmin ,T ymin ,T xmax,T ymax are the true coordinates of the upper left and lower right corners of the prediction box.

[0091] (8) Find the maximum value of the class_prob score of each prediction box, indicating the highest probability that the box belongs to a certain category. Then classify the prediction box into this category. Then sort the scores of all the prediction boxes of each category. The higher the score at the top, the greater the probability that it belongs to this category. Finally, set the nms_threshold non-maximum suppression threshold. For all the prediction boxes of the same category after sorting, remove the prediction boxes with a high degree of overlap based on the condition that the intersection-union ratio iout is less than nms_threshold.

[0092] (9) After the above operations, we get the coordinates of the upper left corner and lower right corner of the prediction box, the category of the prediction box, and the score of the prediction box. Then, we mark this information on the original image to get the detected image and information.

[0093] S1023. Determine a comparison result of the fire area in the infrared thermal imaging image and the fire area in the visible light image based on a threshold segmentation method; if the comparison results are consistent, determine flame space information.

[0094] Based on the above scheme, threshold segmentation is used to detect high-temperature areas or flames in infrared thermal images, and the detection results are compared with those of flames in visible light images. If the results are consistent, they are uploaded to the data center.

[0095] In summary, based on visible light images and infrared thermal images, secondary detection of flames can be performed to improve the efficiency and accuracy of flame detection. At the same time, by obtaining the spatial position of the flame, the spatial positioning function of the fire is realized, reducing the false alarm rate of the flame.

[0096] S1024. When the flame space information indicates the existence of a fire, start a wind measuring laser radar; and determine a fire spreading trend based on the wind measuring laser radar.

[0097] S103. Acquire frequency changes of scattered light based on the wind measuring laser radar, wherein the frequency changes of the scattered light are used to feedback the fire spreading trend.

[0098] The above step S103 further includes S1031:

[0099] S1031. Control the wind measuring laser radar to emit a laser beam; when the laser beam forms scattered light, obtain a frequency change of the scattered light; determine the wind speed based on the frequency change of the scattered light; and determine the fire spread trend based on the wind speed.

[0100] Furthermore, after detecting a fire, the intelligent fusion device activates the wind-measuring laser radar. When the laser beam hits the aerosol particles or atmospheric molecules in the atmosphere at the fire point, these particles or molecules will scatter the laser beam to form scattered light. Due to the movement of particles or molecules, the frequency of the scattered light will change, and this frequency change is proportional to the wind speed. By measuring the frequency change of the scattered light, the wind speed can be calculated and the trend of fire spread can be determined.

[0101] Based on the above scheme, Doppler wind measurement lidar is used to obtain key information such as refined three-dimensional wind field, vertical airflow, turbulent dissipation rate, atmospheric boundary layer, etc. by emitting laser pulses and receiving their return signals, to assist in judging the wind conditions at the fire scene and the situation at key locations.

[0102] Furthermore, the present application uses an intelligent fusion device to collect data such as meteorology, visible light images, infrared images, wind fields and vertical airflows, and turbulent dissipation rates, and after analysis by a built-in algorithm, uploads the analysis results to a data monitoring center.

[0103] The above technical solution is composed of a meteorological monitoring device, a video monitoring device, an infrared thermal imaging device, a Doppler wind laser radar device and an intelligent fusion device. Each device in the system is installed at various monitoring points around the transmission line tower where the intelligent fusion device is located. The intelligent fusion device gathers data from each device, processes and analyzes the data through an algorithm model, and uploads the analysis results to the data monitoring center through a 4G wireless network.

[0104] Furthermore, as a response to the above Figure 1 In order to realize the method shown in the figure, the embodiment of the present invention also provides a fire monitoring device for a power overhead line corridor, which is used to monitor the fire in the above-mentioned Figure 1 The device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Figure 2 As shown, the device includes: a determination unit 21, a first acquisition unit 22 and a second acquisition unit 23, wherein

[0105] A determination unit 21, configured to determine a fire risk level index based on a meteorological monitoring device, wherein the fire risk level index is used to determine a patrol index, and the patrol index includes a patrol frequency and a patrol monitoring point interval;

[0106] A first acquisition unit 22 is used to obtain flame space information based on the patrol index, wherein the flame space information is used to guide the start and stop of the wind laser radar;

[0107] The second acquisition unit 23 acquires the frequency change of scattered light based on the wind measuring laser radar, wherein the frequency change of scattered light is used to feedback the fire spreading trend.

[0108] The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and a method for monitoring fire conditions in a power overhead line corridor is implemented by adjusting kernel parameters, which can solve the problem that fire conditions monitoring in a power overhead line corridor is overly dependent on intelligent electronic devices.

[0109] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, and when the program is executed by a processor, the method for monitoring fire conditions in a corridor of an overhead power line is implemented.

[0110] An embodiment of the present invention provides a processor, which is used to run a program, wherein the method for monitoring fire conditions in a corridor of an overhead power line is executed when the program is run.

[0111] An embodiment of the present invention provides an electronic device, the electronic device comprising at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the above-mentioned power overhead line corridor fire monitoring method

[0112] An embodiment of the present invention provides an electronic device 30, such as Figure 3 As shown, the electronic device includes at least one processor 301, and at least one memory 302 and a bus 303 connected to the processor; wherein the processor 301 and the memory 302 communicate with each other via the bus 303; the processor 301 is used to call the program instructions in the memory to execute the above-mentioned power overhead line corridor fire monitoring method.

[0113] The intelligent electronic devices in this article can be PCs, PADs, mobile phones, etc.

[0114] The present application also provides a computer program product, which, when executed on a process management electronic device, is suitable for executing a program that initializes the steps of the above-mentioned power overhead line corridor fire monitoring method.

[0115] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0116] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0117] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0120] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 This corresponds to the flow of control of the memory in the embodiment.

[0121] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0122] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0123] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0124] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0126] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0127] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring fire in a power overhead line corridor, characterized in that: include: Determine a fire risk level index based on a meteorological monitoring device, wherein the fire risk level index is used to determine a patrol index, and the patrol index includes a patrol frequency and a patrol monitoring point interval; Patrolling based on the patrol index to obtain flame space information, wherein the flame space information is used to guide the start and stop of the wind laser radar; The frequency change of scattered light is obtained based on the wind measuring laser radar, wherein the frequency change of the scattered light is used to feedback the fire spreading trend.

2. The method according to claim 1, characterized in that The method of determining the fire risk level index based on the meteorological monitoring device includes: Collect real-time meteorological data based on meteorological monitoring devices; The fire risk index is determined by the real-time meteorological data based on the following formula: I FFDI =U×C r ×C s U=f(V)+f(T)+f(r RH )+f(M) Among them, I FFDI is the fire risk index; Cr is the rainfall correction factor; Cs is the snow correction factor; V is the wind speed; T is the temperature, r RH is the relative humidity and M is the drought level.

3. The method according to claim 1, characterized in that The patrolling based on the patrol index to obtain flame space information includes: Controlling the video monitoring device and the infrared thermal imaging device to patrol based on the patrol index to obtain visible light images and infrared thermal imaging images; Flame space information is acquired based on the visible light image and the infrared thermal imaging image.

4. The method according to claim 3, characterized in that Also includes: The infrared thermal imaging image is trained based on the YOLOv5 deep learning model algorithm to obtain a fire detection model, wherein the fire detection model is used to determine the fire area in the infrared thermal imaging image; The camera intrinsic parameters of the visible light image are calibrated based on the Zhang Zhengyou calibration method to establish an image coordinate system, wherein the image coordinate system is used to perform coordinate transformation on the fire detection result to determine the fire area in the visible light image.

5. The method according to claim 4, characterized in that The method of acquiring flame space information based on the visible light image and the infrared thermal imaging image includes: Determine a comparison result between the fire area in the infrared thermal imaging image and the fire area in the visible light image based on a threshold segmentation method; When the comparison results are consistent, the flame space information is determined.

6. The method according to claim 1, characterized in that Also includes: In the case where the flame space information indicates the presence of a fire, starting a wind measuring laser radar; The fire spreading trend is determined based on the wind laser radar.

7. The method according to claim 6, characterized in that The frequency change of the scattered light is obtained based on the wind laser radar, wherein the frequency change of the scattered light is used to feedback the fire spreading trend, including: Controlling the wind measuring laser radar to emit a laser beam; When the laser beam forms scattered light, obtaining a frequency change of the scattered light; determining wind speed based on the frequency change of the scattered light; The fire spread tendency is determined based on wind speed.

8. A fire monitoring device for a power overhead line corridor, characterized in that: Also includes: A determination unit, configured to determine a fire risk level index based on a meteorological monitoring device, wherein the fire risk level index is used to determine a patrol index, and the patrol index includes a patrol frequency and a patrol monitoring point interval; A first acquisition unit, configured to acquire flame space information by patrolling based on the patrol index, wherein the flame space information is used to guide the start and stop of the wind laser radar; The second acquisition unit acquires the frequency change of scattered light based on the wind measuring laser radar, wherein the frequency change of scattered light is used to feedback the fire spreading trend.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the steps of the method for monitoring fire conditions in a power overhead line corridor according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the steps of the power overhead line corridor fire monitoring method as described in any one of claims 1 to claim 7.

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