CN106228140A - The transmission line forest fire smog of a kind of combination weather environment sentences knowledge method - Google Patents
The transmission line forest fire smog of a kind of combination weather environment sentences knowledge method Download PDFInfo
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Abstract
The transmission line forest fire smog that the invention discloses a kind of combination weather environment sentences knowledge method, and the method comprises the steps: that (1) obtains video surveillance image sampling frame, and image is carried out noise suppression preprocessing；(2) smog movement provincial characteristics is extracted；(3) moving region speed and orientative feature are obtained；(4) mountain fire smog shape recognition.The method of the invention extracts movement velocity and the direction of motion of inter frame image in sequence image the most accurately, then in conjunction with mountain fire early-stage smog motion feature and shaft tower environment parament, mountain fire smog movement is judged, preferably eliminate the factor impacts such as flying bird, vehicle, pedestrian, forest zone mist, be effectively reduced the probability of miscarriage of justice to transmission line forest fire alarm.As the aid of transmission line forest fire monitoring system, the method, for improving mountain fire monitoring accuracy, reduces mountain fire tripping operation significant.
Description
Technical Field
The invention belongs to the technical field of electric power engineering, and particularly relates to a power transmission line forest fire smoke identification method combined with a meteorological environment.
Background
The mountain fire of the power transmission line is mainly caused by the fact that the fire breaks out of combustible materials (including trees, thatch, structures, flammable and explosive materials and the like) existing below the line and in a protection area, and damage or faults are caused to the line. The main forms include mountain fire, house fire, deposits (coal, wood, plastic, etc.) fire, etc. The existing power transmission line forest fire monitoring method mainly depends on an infrared monitoring technology, and if a satellite-based infrared monitoring device, a ground infrared monitoring device, an airborne infrared forest fire monitoring device and a microwave radiation monitoring device are developed, the method is popularized to a certain extent in the aspect of large-area forest fire monitoring. However, in the monitoring of the forest fire of the power transmission line, the forest fire is difficult to be found in time at the initial stage only through infrared monitoring, and the condition that the forest fire cannot be identified due to the fact that flame is shielded easily occurs. Video monitoring based on smoke in the prior art has important significance for finding fire early, but at present, mountain fire identification based on smoke is interfered by factors such as atmosphere cloud and fog, tree swing and the like, and the false alarm rate is high.
Disclosure of Invention
Aiming at the defects of low mountain fire monitoring and identifying rate and high mountain fire alarming and false alarming rate of the power transmission line, the invention provides the mountain fire smoke identifying method of the power transmission line combined with the meteorological environment by researching the image characteristics of the smoke of the power transmission line and combining the meteorological environment data measured by the meteorological sensor on the mountain fire monitoring device to eliminate the influence of interference factors such as forest cloud and fog, pedestrian and vehicle, tree swing and the like.
A meteorological environment-related electric transmission line forest fire smoke identification method comprises the following steps:
the method comprises the following steps of (1) acquiring a video monitoring image of the forest fire of the power transmission line, and preprocessing the monitoring image;
step (2), according to the preprocessed image obtained in the step (1), setting a characteristic extraction time interval, and extracting mountain fire smoke motion areas in adjacent frame images within the interval time;
step (3), extracting the characteristics of the movement direction and the movement speed of the mountain fire smoke within the characteristic extraction time interval from the mountain fire smoke movement area obtained in the step (2);
and (4) identifying the mountain fire smoke by using the mountain fire smoke motion direction and motion speed characteristics extracted in the step (3) and combining wind speed and wind direction in a meteorological environment, so as to realize accurate monitoring of the mountain fire.
The preprocessing of the monitoring image in the step (1) refers to denoising the monitoring image by adopting adaptive wavelet filtering.
The method adopts an interframe difference method to extract the mountain fire smoke motion area, and comprises the following specific processes:
step 2.1: acquiring a differential image;
extracting the interframe sequence images according to the set characteristic extraction time interval to obtain the kth (k) of the sequence images>1) Frame image andthe 1 st frame image corresponding pixel point gray value is subjected to inter-frame differential subtraction to obtain fk(x, y) is the gray value at the (x, y) point after the difference between the k frame image and the 1 st frame image, and the calculation formula is as follows:
fk(x,y)＝|μk(x,y)-μ1(x,y)|
in the formula, muk(x, y) is the gray value of the point with the coordinate of (x, y) in the k frame image, fk(x, y) is the gray value of the point (x, y) after the difference between the k frame image and the 1 st frame image;
step 2.2: and (4) carrying out binary processing on the difference image, and acquiring a mountain fire smoke motion area from the difference image.
The threshold used when the difference image is binarized in the step 2.2 is obtained by the following steps:
step 2.2.1: finding each frame differential image fkTaking the average value of the minimum gray value and the maximum gray value of the pixels in (x, y) as an initial threshold value NiAnd let i equal to 1;
step 2.2.2: dividing each frame of differential image into a background area and a motion area according to the threshold value obtained in the last stage, respectively calculating the average gray value of the two parts, and respectively recording the average gray value as G0And G1；
Step 2.2.2: taking the average value of the average gray values of the background area and the motion area as an updating threshold value Ni+1：
If | Ni+1-Ni|<1, then the optimal threshold value is output to be Ni+1(ii) a Otherwise, the current threshold value is converted into the step 2.2.2, and the iteration is continued.
And performing further enhancement processing on the differential image, including background separation and denoising processing. Firstly, the area with the pixel value less than the set threshold is taken as the background area, and the gray value of the pixel pointIs set to 0, andand taking the area larger than the threshold as a motion area, and setting the gray value of the pixel point to be 255.
And (3) carrying out self-adaptive wavelet filtering denoising treatment on the mountain fire smoke motion area obtained in the step 2.2.
The extraction steps of the mountain fire smoke motion direction and motion speed characteristics are as follows:
(a) randomly selecting 1 pixel point from each frame of motion area image as the initial central point of each frame of motion area, and sequentially marking as o1(x,y),o2(x,y),…ok(x,y)；
(b) Calculating the distance from each pixel point in each frame of motion region image to the corresponding central point:
wherein,representing the distance between the ith pixel point and the central point in the k frame motion area image;
(c) circularly updating the coordinates of the central point to obtain the optimal central point of each motion area, and marking as sk(x, y) wherein sk(x, y) satisfiesn represents the number of pixel points;
the final central point meets the minimum sum of the distances from each pixel point to the central point;
(d) calculating the motion speed between the center points of the adjacent frames:
wherein, Δ t is the time interval of adjacent frames;
(e) calculating the motion orientation between the center points of the adjacent frames:
wherein each pixel point in the k frame motion region is expressed asAndare the abscissa and ordinate of the center point in the k-th frame motion region.
The mountain fire smoke identification by utilizing the mountain fire smoke motion direction and motion speed characteristics extracted in the step (3) and combining wind speed and wind direction in meteorological environment is as follows:
firstly, extracting the movement speed and the movement direction range of the mountain fire smoke;
selecting the moving speed v of the moving area image in the set characteristic extraction time intervalkMaximum value v ofk,maxAnd a minimum value vk,minAnd orientation of motion thetakMaximum value of (theta)k,maxAnd minimum value thetak,min；
Then, whether the wind speed and the wind direction in the meteorological environment obtained by the mountain fire monitoring device fall in the corresponding mountain fire smoke movement speed and movement direction range or not is judged, if yes, the existence of mountain fire smoke is indicated, otherwise, the existence of mountain fire smoke is indicated:
the wind speed measured by the mountain fire monitoring device in the corresponding characteristic extraction time interval falls within [ v [ ]k,min,vk,max]；
Wind direction data measured by the mountain fire monitoring device in the corresponding characteristic extraction time interval falls within [ theta ]k,min,θk,max]。
Advantageous effects
The invention provides a power transmission line forest fire smoke identification method combined with a meteorological environment, which comprises the following steps: (1) acquiring a video monitoring image sampling frame, and carrying out denoising pretreatment on the image; (2) extracting the characteristics of the smoke motion area; (3) acquiring the speed and orientation characteristics of the motion area; (4) and identifying the shape of mountain fire smoke. The method firstly accurately extracts the movement speed and the movement direction of the interframe images in the sequence images, and then judges the forest fire smoke movement by combining the forest fire early-stage smoke movement characteristics and tower meteorological environment parameters, thereby better eliminating the influence of factors such as flying birds, vehicles, pedestrians, forest region fog and the like and effectively reducing the false judgment probability of the forest fire alarm of the power transmission line. As an auxiliary tool of the power transmission line forest fire monitoring system, the method has important significance for improving forest fire monitoring precision and reducing forest fire tripping.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to fig. 1.
The invention relates to a power transmission line forest fire smoke identification method combined with meteorological environment, which comprises the following specific steps:
the method comprises the following steps: and acquiring a video monitoring image sampling frame, and denoising the acquired image by using a self-adaptive wavelet filtering method.
And extracting at the sampling frequency of one frame every 0.5s, and extracting image features every 3s, namely extracting 6 frames of images every time and analyzing.
Selecting different threshold values on different scales, setting the wavelet coefficient smaller than the threshold value to zero, and keeping the wavelet coefficient larger than the threshold value, thereby effectively inhibiting the noise in the signal, and finally obtaining the filtered reconstruction signal through wavelet inverse transformation.
Step two: and (4) carrying out feature extraction on the smoke motion area by using a difference method.
The denoised image sequence can be represented as: mu.s1,μ2,…μ6And performing interframe difference subtraction on gray pixel values of corresponding pixels of the kth frame image and the (k-1) th frame image in the 6 frames of images, wherein a calculation formula is as follows:
fk(x,y)＝|μk(x,y)-μk-1(x,y)|
in the formula ofk(x, y) is the gray value of the point with the coordinate of (x, y) in the k frame image, fk(x, y) is the gray scale value at the (x, y) point after the difference between the k frame image and the k-1 frame image.
And performing further enhancement processing on the differential image, including background separation and denoising processing.
(1) And separating the background. The background separation adopts a threshold value method, wherein a region with a pixel value smaller than a set threshold value N is used as a background region, the gray value of a pixel point is set to be 0, a region with a pixel value larger than the threshold value is used as a motion region, the gray value of the pixel point is set to be 255, and the calculation formula is as follows:
the value calculation process of the threshold value N in the formula is as follows:
(a) determining an image fkTaking the average value of the minimum gray value and the maximum gray value of the pixels in (x, y) as an initial threshold value NiLet i equal to 1;
(b) dividing the image into a background area and a motion area according to the threshold value obtained in the previous stage, and respectively obtaining the average gray value of the two parts, which is expressed as G0And G1；
(c) Taking the average value of the average gray values of the background area and the motion area to update the threshold value Ni+1：
If | Ni+1-Ni|<1, then the optimal threshold value is output to be Ni+1(ii) a Otherwise, the step (b) is carried out by the current threshold value, and the iteration is continued;
(2) and (5) denoising. And (4) eliminating isolated noise points in the motion area by means of a wavelet denoising method in the step (1).
Step three: and extracting the azimuth and speed characteristics of the motion areas by calculating the central point of each motion area. Expressing each pixel point in the acquired k frame motion region asThe specific implementation steps are as follows:
(a) randomly selecting k pixel points as initial central points of motion areas of each frame, and marking as o1(x,y),o2(x,y),…ok(x,y)；
(b) Calculating the distance from each pixel point in each frame image to the corresponding central point:
whereinAnd the distance between the ith pixel point and the central point in the k frame image is represented.
(c) Circularly updating the coordinates of the central point to obtain the optimal central point of each motion area, and marking as sk(x, y) wherein sk(x, y) satisfiesn represents the number of pixel points;
the final central point meets the minimum sum of the distances from each pixel point to the central point;
(d) calculating the motion speed between the center points of the adjacent frames:
wherein, the delta t is the time interval of adjacent frames and is taken as 0.5 s;
(e) calculating the motion orientation between the center points of the adjacent frames:
step four: performing mountain fire smoke shape auxiliary judgment based on meteorological environment data, and marking the motion area as smoke motion caused by mountain fire when the following two criteria are met:
(a) finding all motion region images v in a feature extraction intervalkMaximum value v ofk,maxAnd a minimum value vk,minAnd when the mountain fire monitoring device is provided with the meteorological sensor, the obtained wind speed data falls to [ v [ [ v ]k,min,vk,max]。
(b) Finding theta for all motion region images in a feature extraction intervalkMaximum value of (theta)k,maxAnd minimum value thetak,minAnd when the mountain fire monitoring device is provided with the meteorological sensor, the wind direction data obtained by the meteorological sensor falls on [ theta ]k,min,θk,max]。
The method for identifying the forest fire smoke of the transmission line related to the meteorological environment is adopted to monitor the forest fire of the extra-high voltage direct current transmission line in a certain area in Hunan province in real time. By carrying out smoke feature identification on the obtained video image, mountain fires of four times of plus or minus 800KV guest gold wire 1713#, guest gold wire 1210#, plus or minus 800KV chinsu wire 1723# and plus or minus 500KV Jiangcheng wire 511# are accurately monitored on 8 days of 2 months, and the mountain fires occur in the tower area after verification of on-site line operation and maintenance personnel.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A power transmission line forest fire smoke identification method combined with meteorological environment is characterized by comprising the following steps:
the method comprises the following steps of (1) acquiring a video monitoring image of the forest fire of the power transmission line, and preprocessing the monitoring image;
step (2), according to the preprocessed image obtained in the step (1), setting a characteristic extraction time interval, and extracting mountain fire smoke motion areas in adjacent frame images within the interval time;
step (3), extracting the characteristics of the movement direction and the movement speed of the mountain fire smoke within the characteristic extraction time interval from the mountain fire smoke movement area obtained in the step (2);
and (4) identifying the mountain fire smoke by using the mountain fire smoke motion direction and motion speed characteristics extracted in the step (3) and combining wind speed and wind direction in a meteorological environment, so as to realize accurate monitoring of the mountain fire.
2. The method according to claim 1, wherein the preprocessing of the monitoring image in step (1) is to perform denoising processing on the monitoring image by using adaptive wavelet filtering.
3. The method as claimed in claim 2, wherein the extraction of the mountain fire smoke motion area is performed by using an interframe difference method, which comprises the following steps:
step 2.1: acquiring a differential image;
extracting the interframe sequence images according to the set characteristic extraction time interval to obtain the kth (k) of the sequence images>1) The gray values of the corresponding pixel points of the frame image and the 1 st frame image are subjected to inter-frame differential subtraction to obtain fk(x, y) is the gray value at the (x, y) point after the difference between the k frame image and the 1 st frame image, and the calculation formula is as follows:
fk(x,y)＝|μk(x,y)-μ1(x,y)|
in the formula, muk(x, y) is the gray value of the point with the coordinate of (x, y) in the k frame image, fk(x, y) is the gray value of the point (x, y) after the difference between the k frame image and the 1 st frame image;
step 2.2: and (4) carrying out binarization processing on the difference image, and acquiring a mountain fire smoke motion area from the difference image.
4. A method according to claim 3, characterized in that the threshold used in the binarization processing of the difference image in step 2.2 is obtained by the following steps:
step 2.2.1: finding each frame differential image fkTaking the average value of the minimum gray value and the maximum gray value of the pixels in (x, y) as an initial threshold value NiAnd let i equal to 1;
step 2.2.2: dividing each frame of differential image into a background area and a motion area according to the threshold value obtained in the last stage, respectively calculating the average gray value of the two parts, and respectively recording the average gray value as G0And G1；
Step 2.2.2: taking the average value of the average gray values of the background area and the motion area as an updating threshold value Ni+1：
If | Ni+1-Ni|<1, then the optimal threshold value is output to be Ni+1(ii) a Otherwise, the current threshold value is converted into the step 2.2.2, and the iteration is continued.
5. The method according to claim 3 or 4, characterized in that the adaptive wavelet filtering denoising processing is carried out on the mountain fire smoke motion region obtained in the step 2.2.
6. The method as claimed in claim 5, wherein the extraction steps of the mountain fire smoke motion direction and motion speed features are as follows:
(a) randomly selecting 1 pixel point from each frame of motion area image as the initial central point of each frame of motion area, and sequentially marking as o1(x,y),o2(x,y),…ok(x,y)；
(b) Calculating the distance from each pixel point in each frame of motion region image to the corresponding central point:wherein,representing the distance between the ith pixel point and the central point in the k frame motion area image;
(c) circularly updating the coordinates of the central point to obtain the optimal central point of each motion area, and marking as sk(x, y) wherein sk(x, y) satisfiesn tableDisplaying the number of pixel points;
(d) calculating the motion speed between the center points of the adjacent frames:
wherein, Δ t is the time interval of adjacent frames;
(e) calculating the motion orientation between the center points of the adjacent frames:
wherein each pixel point in the k frame motion region is expressed as Andare the abscissa and ordinate of the center point in the k-th frame motion region.
7. The method as claimed in claim 6, wherein the mountain fire smoke identification by using the mountain fire smoke motion direction and motion speed features extracted in the step (3) and combining wind speed and wind direction in meteorological environment is as follows:
firstly, extracting the movement speed and the movement direction range of the mountain fire smoke;
selecting the moving speed v of the moving area image in the set characteristic extraction time intervalkMaximum value v ofk,maxAnd a minimum value vk,minAnd orientation of motion thetakMaximum value of (theta)k,maxAnd minimum value thetak,min；
Then, whether the wind speed and the wind direction in the meteorological environment obtained by the mountain fire monitoring device fall in the corresponding mountain fire smoke movement speed and movement direction range or not is judged, if yes, the existence of mountain fire smoke is indicated, otherwise, the existence of mountain fire smoke is indicated:
the wind speed measured by the mountain fire monitoring device in the corresponding characteristic extraction time interval falls within [ v [ ]k,min,vk,max]；
Wind direction data measured by the mountain fire monitoring device in the corresponding characteristic extraction time interval falls within [ theta ]k,min,θk,max]。
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