CN112287849A - Fire early warning method and device for high-rise building - Google Patents

Fire early warning method and device for high-rise building Download PDF

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CN112287849A
CN112287849A CN202011197533.5A CN202011197533A CN112287849A CN 112287849 A CN112287849 A CN 112287849A CN 202011197533 A CN202011197533 A CN 202011197533A CN 112287849 A CN112287849 A CN 112287849A
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river
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谢筱依
王秀
高璐
闫全
程格尔
冯晓磊
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Wuhan Ligong Guangke Co Ltd
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Abstract

The invention discloses a fire early warning method and a device for a high-rise building, wherein the method comprises the following steps: acquiring infrared image data in a high-rise building, and preprocessing the acquired image; calculating an optimal image segmentation threshold value set by adopting an improved water circulation algorithm, and extracting a flame region according to the image segmentation threshold value set; extracting characteristic parameters of a flame area, identifying flame through multi-characteristic fusion, judging whether a fire exists, positioning the fire and starting fire alarm; predicting the fire spreading situation according to the fire location and the building structure; and sending the fire occurrence position and the fire spreading situation to a command center, and broadcasting the escape route guidance by voice. The invention adopts an improved water circulation algorithm to calculate the optimal image segmentation threshold, accurately extracts the flame area through multi-threshold segmentation, and accurately identifies and warns the fire at the initial stage of the fire.

Description

Fire early warning method and device for high-rise building
Technical Field
The invention belongs to the field of fire safety, and particularly relates to a fire early warning method and device for a high-rise building.
Background
With the deep advance of the urbanization process in China, high-rise buildings become the inevitable choice for urban development. Meanwhile, high-rise buildings have the characteristics of multiple use functions, complex building structures, multiple combustible decorations, large fire load, multiple electrical equipment, dense population and the like, so that the fire-causing factors of the buildings are multiple, once a fire disaster occurs, if the fire disaster is not effectively controlled in the early stage, the fire disaster can be rapidly spread, the fire fighting difficulty is high, and the serious casualties and property loss are easily caused. In recent years, a plurality of high-rise building fire accidents which have great influence occur in China successively, and great casualties, property loss and negative social influence are caused.
The high-rise building fire early-stage fire detection, the fire spreading pre-judgment and the internal fire threat reminding are important aspects of fire safety. At the present stage, image or video-based fire identification is effectively utilized, wherein fire image segmentation is an important premise for early fire identification, and the problems of low fire identification accuracy and the like caused by inaccurate flame segmentation still exist in the prior art.
Disclosure of Invention
In view of the above, the invention provides a high-rise building fire early warning method, which is used for solving the problem that a fire identification accuracy rate is not high due to the fact that a flame area cannot be accurately divided when a high-rise building fire occurs, so that accurate identification and positioning can be achieved at the initial stage of the fire.
The invention discloses a fire early warning method for a high-rise building, which comprises the following steps:
acquiring infrared image data in a high-rise building, and preprocessing the acquired image;
calculating an optimal image segmentation threshold value set by adopting an improved water circulation algorithm, and extracting a flame region according to the image segmentation threshold value set;
extracting characteristic parameters of a flame area, identifying flame through multi-characteristic fusion, judging whether a fire disaster exists, if the fire disaster occurs, positioning the fire disaster, and starting a fire disaster alarm; predicting the fire spreading situation according to the fire location and the building structure;
and sending the fire occurrence position and the fire spreading situation to a command center, and broadcasting the escape route guidance by voice.
Preferably, the step of calculating the optimal image segmentation threshold set by using the improved water circulation algorithm specifically comprises:
initialization: let N be the total number of populations, NstreamIs the number of streams, NriverThe number of rivers and oceans is 1, Nsr＝Nriver+1，Nstream＝N-NsrThe number of the image segmentation threshold values is D; the upper and lower boundary range of the image segmentation threshold is [ LB, UB]；
And (3) fitness calculation: calculating a fitness value through a fitness function, and screening out the current optimal ocean, river and stream according to the fitness value;
a confluence process: calculating the quantity of streams flowing to the ocean and the quantity of streams flowing to the corresponding river in the current population:
Figure BDA0002754441520000021
Nsrnfor the number of streams flowing into a river or ocean, round () is a round-down operation, fnIs the fitness value of the current individual, fmaxThe maximum value of the fitness in the current stream is obtained;
the stream flows to the river, the stream flows to the ocean, and the river flows to the ocean, and the position updating is respectively carried out:
Figure BDA0002754441520000022
Figure BDA0002754441520000023
the positions of the streams at the t-th iteration and the t + 1-th iteration are respectively,
Figure BDA0002754441520000024
the positions of the river at the t-th iteration and the t +1 th iteration respectively,
Figure BDA0002754441520000025
the positions of the ocean at the t th iteration and the t +1 th iteration are respectively, rand is a random number between (0,1), and C1, C2 and C3 are constants between (0, 2);
after the stream is updated every time, calculating a corresponding fitness value, if the current fitness value is superior to the fitness value of the river connected with the current fitness value, exchanging the position of the stream with the position of the river, and otherwise, keeping the position unchanged, and exchanging the river with the ocean and exchanging the stream with the ocean through the same rule;
evaporation and rainfall process: checking the relative positions of rivers and streams and oceans, and setting that if the judgment condition of the evaporation process is met, new streams or rivers are formed based on Laevice flight at different positions:
and storing the ocean position in the current population, recalculating the fitness value, and iteratively updating until the end condition is reached, wherein the obtained ocean position is the optimal position, namely the optimal image segmentation threshold value set.
Preferably, the conditions for determining the evaporation process are as follows:
Figure BDA0002754441520000031
dmaxis a constant close to 0;
the formula for forming a new stream or river based on the ravin flight at different positions is as follows:
Figure BDA0002754441520000032
alpha is the step size and is the length of the step,
Figure BDA0002754441520000039
for point-to-point multiplication, Levy (β) represents the lewy distribution with a parameter of β,
Figure BDA0002754441520000033
wherein the content of the first and second substances,
Figure BDA0002754441520000034
gamma is a standard gamma function, and u and v are both subjected to normal distribution; when in use
Figure BDA0002754441520000035
Or
Figure BDA0002754441520000036
The value of (b) exceeds the upper and lower boundary ranges [ LB, UB ] of the image segmentation threshold]In time, the stream or river is regenerated within the boundaries:
Figure BDA0002754441520000037
in the iterative process, dmaxIs updated by the formula
Figure BDA0002754441520000038
Preferably, the fitness function is the information entropy of the image, and the gray scale range of the infrared image is set as [0, K ]]And the image segmentation threshold value is set to be k1,k2,…，kDDividing pixel points in the image into a plurality of intervals, namely [0, k ], according to each image segmentation threshold value1-1]、[k1,k2-1]、…、[kD,K]Separately calculating the signal in each interval rangeEntropy and sum as fitness function:
Figure BDA0002754441520000041
wherein P isiIs the probability that a certain gray i appears in the image.
Preferably, the fire spreading situation prediction specifically includes:
extracting a spherical area which takes a fire position as a center and is within a set radius according to a building structure of a high-rise building, and extracting objects which cause a main spreading path of the fire of the high-rise building in the spherical area, wherein the main spreading path comprises a horizontal spreading path and a vertical spreading path, and the objects on the horizontal spreading path comprise an inner wall, a door and a window, a corridor, a suspended ceiling, a room partition wall and a horizontal dry type metal pipeline; objects on the vertical spreading path comprise staircases, vertical pipe shafts and cavities and gaps of floor slabs in the building;
identifying large-area inflammables in a main spreading path according to image data in a building, wherein the large-area inflammables comprise plastic wall cloth, wallpaper, curtains, large garbage cans and inflammable deposits;
and extracting a communication area consisting of objects and large-area inflammable matters on the horizontal spreading path and the vertical spreading path, and predicting the fire spreading situation according to the communication area.
In a second aspect of the present invention, a fire early warning device for high-rise buildings is disclosed, the device comprising:
a data acquisition module: the system is used for acquiring infrared image data in a high-rise building in real time, acquiring and storing a corresponding building structure;
an image segmentation module: the system comprises a water circulation module, a flame region extraction module and a flame region extraction module, wherein the water circulation module is used for carrying out pretreatment on an acquired image, calculating an optimal image segmentation threshold value set by adopting an improved water circulation algorithm, and extracting the flame region according to the image segmentation threshold value set;
flame discernment and orientation module: extracting characteristic parameters of a flame area, identifying flame through multi-characteristic fusion, judging whether a fire exists, and if the fire occurs, positioning the fire;
a fire spread prediction module: the system is used for predicting the fire spreading situation according to the fire location and the building structure;
fire early warning module: the system is used for starting fire alarm, sending the fire occurrence position and the fire spreading situation to the command center, and broadcasting escape route guidance by voice.
Compared with the prior art, the invention has the following beneficial effects:
1) an improved water circulation algorithm is adopted to calculate an optimal image segmentation threshold value set, and a new stream or river is formed at different positions based on Levy flight to simulate the rainfall process, so that the population diversity is increased, and the search efficiency is ensured; the flame area is subjected to multi-threshold segmentation according to the optimal image segmentation threshold value set, so that the flame segmentation accuracy is improved, accurate fire identification and positioning at the initial stage of a fire are realized, and fire early warning is timely carried out;
2) the method can effectively pre-judge the fire situation spreading in advance according to the fire location and the building structure, and provides a judgment basis for personnel escape and fire fighting.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a fire early warning method for high-rise buildings according to the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, the present invention provides a fire early warning method for a high-rise building, the method comprising:
s1, acquiring infrared image data in the high-rise building, and preprocessing the acquired image;
and acquiring infrared image data in the high-rise building by adopting an infrared camera, and performing gray level conversion, filtering and drying removal and other treatment on the acquired infrared image.
S2, calculating an optimal image segmentation threshold value set by adopting an improved water circulation algorithm, and extracting a flame region according to the image segmentation threshold value set;
an improved water circulation algorithm is adopted to calculate an optimal image segmentation threshold value set, and the method specifically comprises the following steps:
an initialization process: let N be the total number of populations, NstreamIs the number of streams, NriverThe number of rivers and oceans is 1, Nsr＝Nriver+1，Nstream＝N-NsrThe number of the image segmentation threshold values is D; the upper and lower boundary range of the image segmentation threshold is [ LB, UB]；
And (3) fitness calculation process: calculating a fitness value through a fitness function, and screening out the current optimal ocean, river and stream according to the fitness value;
the fitness function is the information entropy of the image, and the gray scale range of the infrared image is set as [0, K ]]And the image segmentation threshold value is set to be k1,k2,…，kDDividing pixel points in the image into a plurality of intervals, namely [0, k ], according to each image segmentation threshold value1-1]、[k1,k2-1]、…、[kD,K]And respectively calculating the information entropy in each interval range and summing the information entropy as a fitness function:
Figure BDA0002754441520000061
wherein P isiIs the probability that a certain gray i appears in the image.
The best fitness is ocean, then river, and the worse remaining fitness is stream.
A confluence process: calculating the quantity of streams flowing to the ocean and the quantity of streams flowing to the corresponding river in the current population:
Figure BDA0002754441520000062
Nsrnfor the number of streams flowing into a river or ocean, round () is a round-down operation, fnIs the fitness value of the current individual, fmaxThe maximum value of the fitness in the current stream is obtained;
the stream flows to the river, the stream flows to the ocean, and the river flows to the ocean, and the position updating is respectively carried out:
Figure BDA0002754441520000071
Figure BDA0002754441520000072
the positions of the streams at the t-th iteration and the t + 1-th iteration are respectively,
Figure BDA0002754441520000073
the positions of the river at the t-th iteration and the t +1 th iteration respectively,
Figure BDA0002754441520000074
the positions of the ocean at the t th iteration and the t +1 th iteration are respectively, rand is a random number between (0,1), and C1, C2 and C3 are constants between (0, 2);
after the stream is updated at each position, calculating a corresponding fitness value, if the current fitness value is superior to the fitness value of the river connected with the current fitness value, exchanging the position of the stream with the position of the river, otherwise, keeping the position unchanged, and exchanging the river with the ocean and exchanging the stream with the ocean through the same rule;
evaporation and rainfall process: checking the relative positions of rivers and streams and oceans, and setting that if the relative positions meet the judgment condition of an evaporation process, new streams or rivers are formed at different positions based on Laiwei flight;
taking a stream as an example, the judgment conditions of the evaporation process are as follows:
Figure BDA0002754441520000075
dmaxis a constant close to 0;
the formula for forming a new stream or river based on the ravin flight at different positions is as follows:
Figure BDA0002754441520000076
alpha is the step size and is the length of the step,
Figure BDA00027544415200000712
for point-to-point multiplication, Levy (β) represents the lewy distribution with a parameter of β,
Figure BDA0002754441520000077
wherein the content of the first and second substances,
Figure BDA0002754441520000078
gamma is a standard gamma function, and u and v are both subjected to normal distribution; when in use
Figure BDA0002754441520000079
Or
Figure BDA00027544415200000710
The value of (b) exceeds the upper and lower boundary ranges [ LB, UB ] of the image segmentation threshold]In time, the stream or river is regenerated within the boundaries:
Figure BDA00027544415200000711
in the iterative process, dmaxIs updated by the formula
Figure BDA0002754441520000081
And storing the ocean position in the current population, recalculating the fitness value, and iteratively updating until the end condition is reached, wherein the obtained ocean position is the optimal position, namely the optimal image segmentation threshold value set.
According to the invention, an optimal threshold segmentation set is searched through an improved water circulation algorithm, and a new stream or river is formed at different positions based on Levy flight by utilizing the evaporation and rainfall processes of the water circulation algorithm, so that the population diversity is increased, the search efficiency is ensured, and the flame area can be accurately segmented, thereby performing flame identification and ensuring the accuracy of flame identification.
S3, extracting characteristic parameters of a flame area, identifying flame through multi-characteristic fusion, judging whether a fire exists, if so, positioning the fire, and starting a fire alarm;
specifically, the characteristic parameters of the flame include circularity, area change rate, stroboscopic characteristics, layered characteristics, and the like.
S4, forecasting fire spreading situation according to the fire location and the building structure; the fire spreading situation prediction specifically comprises the following steps:
extracting a spherical area which takes a fire position as a center and is within a set radius according to a building structure of a high-rise building, and extracting objects which cause a main spreading path of the fire of the high-rise building in the spherical area, wherein the main spreading path comprises a horizontal spreading path and a vertical spreading path, and the objects on the horizontal spreading path comprise an inner wall, a door and a window, a corridor, a suspended ceiling, a room partition wall and a horizontal dry type metal pipeline; objects on the vertical spreading path comprise staircases, vertical pipe shafts and cavities and gaps of floor slabs in the building;
identifying large-area inflammables in a main spreading path according to image data in a building, wherein the large-area inflammables comprise plastic wall cloth, wallpaper, curtains, large garbage cans and inflammable deposits;
and extracting a communication area consisting of objects and large-area inflammable matters on the horizontal spreading path and the vertical spreading path, and predicting the fire spreading situation according to the communication area.
And S5, sending the fire occurrence position and the fire spreading situation to a command center, and broadcasting the escape route guidance by voice.
Based on infrared image data, the invention adopts an improved water circulation algorithm to search an optimal threshold segmentation set, can accurately segment a flame region for fire identification, accurately identify and position the fire at the initial stage of the fire, effectively pre-judge the fire situation spread in advance, pointedly remind and suppress the fire threat in the building, and reduce the casualties and property loss caused by the fire of the building.
Corresponding to the embodiment of the method, the invention also provides a high-rise building fire early warning device, which comprises:
a data acquisition module: the system is used for acquiring infrared image data in a high-rise building in real time, acquiring and storing a corresponding building structure;
an image segmentation module: the system comprises a water circulation module, a flame region extraction module and a flame region extraction module, wherein the water circulation module is used for carrying out pretreatment on an acquired image, calculating an optimal image segmentation threshold value set by adopting an improved water circulation algorithm, and extracting the flame region according to the image segmentation threshold value set;
flame discernment and orientation module: the system is used for extracting characteristic parameters of a flame area, identifying flame through multi-characteristic fusion, judging whether a fire exists and positioning the fire;
a fire spread prediction module: the system is used for predicting the fire spreading situation according to the fire location and the building structure;
fire early warning module: the system is used for starting fire alarm, sending the fire occurrence position and the fire spreading situation to the command center, and broadcasting escape route guidance by voice.
The above method embodiments and device embodiments are briefly described with reference to the method embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (6)

1. A fire early warning method for high-rise buildings is characterized by comprising the following steps:
acquiring infrared image data in a high-rise building, and preprocessing the acquired image;
calculating an optimal image segmentation threshold value set by adopting an improved water circulation algorithm, and extracting a flame region according to the image segmentation threshold value set;
extracting characteristic parameters of a flame area, identifying flame through multi-characteristic fusion, judging whether a fire disaster exists, if the fire disaster occurs, positioning the fire disaster, and starting a fire disaster alarm;
predicting the fire spreading situation according to the fire location and the building structure;
and sending the fire occurrence position and the fire spreading situation to a command center, and broadcasting the escape route guidance by voice.
2. The fire early warning method for the high-rise building according to claim 1, wherein the step of calculating the optimal image segmentation threshold set by adopting the improved water circulation algorithm specifically comprises the following steps:
initialization: let N be the total number of populations, NstreamIs the number of streams, NriverThe number of rivers and oceans is 1, Nsr＝Nriver+1，Nstream＝N-NsrThe number of the image segmentation threshold values is D; the upper and lower boundary range of the image segmentation threshold is [ LB, UB]；
And (3) fitness calculation: calculating a fitness value through a fitness function, and screening out the current optimal ocean, river and stream according to the fitness value;
a confluence process: calculating the quantity of streams flowing to the ocean and the quantity of streams flowing to the corresponding river in the current population:
Figure FDA0002754441510000011
Figure FDA0002754441510000012
for the number of streams flowing into a river or ocean, round () is a round-down operation, fnIs the fitness value of the current individual, fmaxThe maximum value of the fitness in the current stream is obtained;
the stream flows to the river, the stream flows to the ocean, and the river flows to the ocean, and the position updating is respectively carried out:
Figure FDA0002754441510000013
Figure FDA0002754441510000014
Figure FDA0002754441510000015
Figure FDA0002754441510000021
the positions of the streams at the t-th iteration and the t + 1-th iteration are respectively,
Figure FDA0002754441510000022
the positions of the river at the t-th iteration and the t +1 th iteration respectively,
Figure FDA0002754441510000023
the positions of the ocean at the t th iteration and the t +1 th iteration are respectively, rand is a random number between (0,1), and C1, C2 and C3 are constants between (0, 2);
after the stream is updated every time, calculating a corresponding fitness value, if the current fitness value is superior to the fitness value of the river connected with the current fitness value, exchanging the position of the stream with the position of the river, and otherwise, keeping the position unchanged, and exchanging the river with the ocean and exchanging the stream with the ocean through the same rule;
evaporation and rainfall process: checking the relative positions of rivers and streams and oceans, and setting that if the judgment condition of the evaporation process is met, new streams or rivers are formed based on Laevice flight at different positions:
and storing the ocean position in the current population, recalculating the fitness value, and iteratively updating until the end condition is reached, wherein the obtained ocean position is the optimal position, namely the optimal image segmentation threshold value set.
3. The fire early warning method for high-rise buildings according to claim 3, wherein the judgment conditions of the evaporation process are as follows:
Figure FDA0002754441510000024
dmaxis a constant close to 0;
the formula for forming a new stream or river based on the ravin flight at different positions is as follows:
Figure FDA0002754441510000025
alpha is the step size and is the length of the step,
Figure FDA0002754441510000026
for point-to-point multiplication, Levy (β) represents the lewy distribution with a parameter of β,
Figure FDA0002754441510000027
wherein the content of the first and second substances,
Figure FDA0002754441510000028
gamma is a standard gamma function, and u and v are both subjected to normal distribution; when in use
Figure FDA0002754441510000029
Or
Figure FDA00027544415100000210
The value of (b) exceeds the upper and lower boundary ranges [ LB, UB ] of the image segmentation threshold]In time, the stream or river is regenerated within the boundaries:
Figure FDA0002754441510000031
in the iterative process, dmaxIs updated by the formula
Figure FDA0002754441510000032
4. The fire early warning method for high-rise buildings according to claim 3, wherein the fitness function is the information entropy of the image, and the gray scale range of the infrared image is set as [0, K ]]And the image segmentation threshold value is set to be k1,k2,…，kDDividing pixel points in the image into a plurality of intervals, namely [0, k ], according to each image segmentation threshold value1-1]、[k1,k2-1]、…、[kD,K]And respectively calculating the information entropy in each interval range and summing the information entropy as a fitness function:
Figure FDA0002754441510000033
wherein P isiIs the probability that a certain gray i appears in the image.
5. The fire early warning method for high-rise buildings according to claim 2, wherein the fire spreading situation prediction is specifically:
extracting a spherical area which takes a fire position as a center and is within a set radius according to a building structure of a high-rise building, and extracting objects which cause a main spreading path of the fire of the high-rise building in the spherical area, wherein the main spreading path comprises a horizontal spreading path and a vertical spreading path, and the objects on the horizontal spreading path comprise an inner wall, a door and a window, a corridor, a suspended ceiling, a room partition wall and a horizontal dry type metal pipeline; objects on the vertical spreading path comprise staircases, vertical pipe shafts and cavities and gaps of floor slabs in the building;
identifying large-area inflammables in a main spreading path according to image data in a building, wherein the large-area inflammables comprise plastic wall cloth, wallpaper, curtains, large garbage cans and inflammable deposits;
and extracting a communication area consisting of objects and large-area inflammable matters on the horizontal spreading path and the vertical spreading path, and predicting the fire spreading situation according to the communication area.
6. A fire early warning apparatus for a high-rise building, the apparatus comprising:
a data acquisition module: the system is used for acquiring infrared image data in a high-rise building in real time, acquiring and storing a corresponding building structure;
an image segmentation module: the system comprises a water circulation module, a flame region extraction module and a flame region extraction module, wherein the water circulation module is used for carrying out pretreatment on an acquired image, calculating an optimal image segmentation threshold value set by adopting an improved water circulation algorithm, and extracting the flame region according to the image segmentation threshold value set;
flame discernment and orientation module: the system is used for extracting characteristic parameters of a flame area, identifying flame through multi-characteristic fusion, judging whether a fire exists and positioning the fire;
a fire spread prediction module: the system is used for predicting the fire spreading situation according to the fire location and the building structure;
fire early warning module: the system is used for starting fire alarm, sending the fire occurrence position and the fire spreading situation to the command center, and broadcasting escape route guidance by voice.
CN202011197533.5A 2020-10-30 2020-10-30 Fire early warning method and device for high-rise building Pending CN112287849A (en)

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