CN112052912A - Intelligent flame combustion state identification method for fire-fighting robot - Google Patents

Intelligent flame combustion state identification method for fire-fighting robot Download PDF

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CN112052912A
CN112052912A CN202011010733.5A CN202011010733A CN112052912A CN 112052912 A CN112052912 A CN 112052912A CN 202011010733 A CN202011010733 A CN 202011010733A CN 112052912 A CN112052912 A CN 112052912A
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杨濛
卞永明
季鹏成
刘广军
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Abstract

The invention relates to an intelligent flame combustion state identification method for a fire-fighting robot, which comprises the following steps: carrying out segmentation pretreatment on an original image; calculating radial Tchebichef moment rotation, translation and scaling invariants of the segmentation processing image and the edge image, and constructing a multi-feature composite vector; inputting the multi-feature composite vector into a pre-established and trained improved firefly algorithm-wavelet support vector machine model, and finally obtaining a classification recognition result of the flame combustion state; an improved step size factor and an improved attraction degree are introduced into a firefly algorithm by an improved firefly algorithm-wavelet support vector machine model, iterative search optimization is carried out on wavelet support vector machine kernel parameters, and optimal kernel parameters are obtained. The method is not influenced by the position and the direction of the flame in a visual field scene and the distance between the flame and the image equipment, has high algorithm optimization efficiency and strong recognition performance, can effectively recognize and classify the combustion state of the flame, and assists the fire-fighting robot to perform subsequent targeted fire-fighting operation.

Description

Intelligent flame combustion state identification method for fire-fighting robot
Technical Field
The invention relates to the field of fire-fighting robots, in particular to an intelligent flame identification method for a fire-fighting robot.
Background
With the rapid development of national economy and urbanization construction, various large-scale chemical product storage houses, high-rise building buildings and large-span commercial buildings are scaled. The buildings generally have the characteristics of large personnel mobility, more stored combustible substances, complicated internal channels and the like, and once a fire disaster happens, the buildings are very easy to cause national property loss and casualties. As an intelligent fire fighting means, the fire-fighting robot can replace a fireman to complete reconnaissance and information feedback of a fire scene environment when facing the unknown complex fire scene environment, and carry out fire extinguishing work at the first time. In the process of fire extinguishing work performed by the fire-fighting robot, accurate flame identification is the most critical.
Flame image recognition is a novel image recognition detection technology, intelligent analysis is carried out on images by adopting an advanced recognition algorithm, characteristics such as flame motion, edge contour, color, space difference and the like are extracted, and the flame is directly recognized in real time. For example, patent CN104504382B finds the highest point and the center of gravity of the flame through an internal and external flame extraction algorithm, records RGB values on coordinates of the two points, compares the RGB values with a standard flame RGB feature library to obtain a matching value, and finally determines whether the image is a flame image according to the size of the matching value. For another example, patent CN101826153A detects the motion profile of the current frame and extracts the motion region, and performs uniform color-time domain-space domain filtering on the pixels in the motion region to mark flame pixels, and determines whether to start fire alarm according to the number of flame pixels. In the patent CN104766094B, when a fire disaster does not occur, the numbers of R values and G values are extracted as the distinguishing features, and when the numbers of R values and G values in the monitored image to be detected are obviously abnormal, the fire condition is judged according to the abnormal condition.
However, as with the above methods, most of the existing methods can only identify whether one image contains flames, and cannot identify key fire information such as the combustion state of the flames, and cannot assist the fire-fighting robot to make a perfect fire-fighting scheme. Moreover, these flame recognition methods are very sensitive to rotation, translation and scaling of the image, are susceptible to the location, orientation and distance between the flame and the image device in the scene of the fire, and are not suitable for use in fire fighting robots that are often in motion.
Disclosure of Invention
The invention aims to provide an intelligent flame combustion state identification method for a fire-fighting robot, which overcomes the defects that the combustion state cannot be identified, the visual angle for processing images is single, and the processing capability of the image for sensitive problems such as image rotation, translation and scaling is lacking in the prior art.
The purpose of the invention can be realized by the following technical scheme, comprising the following steps:
image acquisition: acquiring a series of candidate images by using a camera to construct an image data set, and dividing the acquired image data set into a training sample set and a testing sample set;
segmentation pretreatment: processing original images of the training sample set and the test sample set by adopting a color criterion segmentation rule based on a YCbCr color space to obtain corresponding segmentation processing images, then carrying out edge detection processing on the segmentation processing images by using a Canny operator, and extracting the outline of a suspected flame area to obtain an edge image;
and (3) flame feature extraction: calculating rotation, translation and scaling invariants of radial Tchebichef moments of the segmentation processing image and the edge image, respectively obtaining an area moment invariant and a contour moment invariant, and combining the area moment invariants and the contour moment invariants to construct a multi-feature composite vector for describing flame features;
the improved firefly algorithm-wavelet support vector machine is constructed as follows: initializing a pre-established improved firefly algorithm based on the multi-feature composite vector of the training sample set, and performing iterative search optimization on the kernel parameters of the wavelet support vector machine in the firefly transfer process through the introduced improved step-size factor and the improved attraction degree until the preset maximum iteration times are reached to obtain the optimal kernel parameters, so as to construct an improved firefly algorithm-wavelet support vector machine model;
and (3) classifying and identifying the flame combustion state: and inputting the multi-feature composite vector of the test sample set into a pre-established and trained improved firefly algorithm-wavelet support vector machine model, predicting the classification label of the class to which the test sample belongs, and finally obtaining the classification recognition result of the flame combustion state.
Further, in the segmentation preprocessing process, the expression of the color criterion segmentation rule based on the YCbCr color space is adopted as follows:
Figure BDA0002697480630000021
Figure BDA0002697480630000022
in the formula, I (x, y) is a segmentation processing image, if a suspected flame pixel point exists at the original image coordinate (x, y), the gray value at the corresponding coordinate is 1, otherwise, the gray value is 0; y ismeanThe average value of Y channel components of a single original image is obtained; y (x, Y), Cb (x, Y) and Cr (x, Y) are respectively the gray value of the luminance component, the gray value of the blue chrominance component and the gray value of the red chrominance component of the pixel point at the coordinate (x, Y) of the original image; λ is an adaptation coefficient, λ > 1; s is the total number of all pixels in a single original image.
Further, in the flame feature extraction process, an expression of extracting flame features by using rotation, translation and scaling invariant of the radial Tchebichef moment is as follows:
Figure BDA0002697480630000031
Figure BDA0002697480630000032
Figure BDA0002697480630000033
Figure BDA0002697480630000034
in the formula (I), the compound is shown in the specification,
Figure BDA0002697480630000035
rotation, translation and scaling invariants for the radial Tchebichef moment; cpqA radial Tchebichef moment of order p + q; t is00A radial Tchebichef moment of zero order; n is the maximum resolution used in the angular direction; ρ (p, N) is the orthonormal squared norm;
Figure BDA0002697480630000036
a regularized Tchebichef polynomial of order p and length N; m is N/2, and N is the side length of the image; q is a non-negative integer, and q is not more than N-1; g (r, theta) is the input image after translation and mapping; theta is 2 pi l/n, l is more than or equal to 0 and less than or equal to n-1, and l is an integer; r varies from 0 to N/2 and theta varies from 0 to 2 pi.
Further, the kernel function of the wavelet support vector machine adopts a Morlet wavelet function.
Furthermore, in the iterative search optimization process, the improved step factor definition formula is adopted in the improved firefly algorithm to realize the self-adaptive dynamic adjustment of the search step. In the initial stage of iterative search of the algorithm, in order to increase the search space of the firefly individuals, improve the optimization efficiency of the algorithm, avoid local search near the self position in a random mode and increase the step factor value; in the later iteration stage of the algorithm, because random disturbance terms exist all the time, firefly individuals are difficult to coincide with optimal individuals, and in order to improve the convergence rate of the algorithm and prevent repeated oscillation near the optimal feasible solution, the step factor value is reduced at the moment. The improved step-size factor defines the expression of the formula as follows:
Figure BDA0002697480630000037
in the formula (I), the compound is shown in the specification,
Figure BDA0002697480630000038
the step size factor is the step size factor of the improved n +1 th iteration; n is the current iteration number;
Figure BDA0002697480630000039
is the final value of the step factor;
Figure BDA00026974806300000310
is the initial value of the step factor; n ismaxIs the maximum number of iterations.
Further, in the iterative search optimization process, the improved attraction degree is adopted to define a formula by the improved firefly algorithm. At the initial stage of the algorithm, the spatial distance r between some firefly individuals is caused by the over-dispersion of population distributionijTo reduce the influence of the spatial distance on the attraction level, the search space for individual fireflies in the initial stage of the algorithm operation is further increased, the global optimization capability and the optimization efficiency are again improved, and the basic attraction level other than zero is introduced as the minimum value of the attraction level. The expression of the improved attraction definition formula is as follows:
Figure BDA0002697480630000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002697480630000042
the method is to improve the attraction degree of the firefly i to the firefly j; rho0The maximum attraction of the firefly i to other individuals; alpha is light absorption coefficient, alpha belongs to [0.1,10 ]]；ρbIs basically attractive and is not influenced by medium absorption in the transmission process.
Further, in the iterative search optimization process of the improved firefly algorithm, the expression of the position update formula of the firefly i is as follows:
Figure BDA0002697480630000043
in the formula (I), the compound is shown in the specification,
Figure BDA0002697480630000044
the space position coordinate vector of the firefly i in the (n + 1) th iteration is obtained; rand is in [0,1 ]]Random numbers are uniformly distributed in the interval.
Compared with the prior art, the invention has the following advantages:
(1) the flame characteristics in the image are extracted by adopting the invariants of rotation, translation and scaling of the radial Tchebichef moment, so that the flame image can be accurately and stably identified, and the flame image is insensitive to the rotation, translation and scaling of the image; and a multi-feature composite vector containing area invariants and contour moment invariants is constructed, so that the global and local features of the flame can be fully utilized.
(2) The step factor and the attractiveness of the firefly algorithm are improved, the value of the step factor is increased at the initial stage of the firefly algorithm iteration, the search space of the firefly individual is enlarged, the optimization efficiency of the algorithm is improved, the value of the step factor is reduced at the later stage of the algorithm iteration, the convergence speed is improved, and repeated oscillation near the optimal feasible solution is effectively prevented; the improved attraction degree reduces the influence of the space distance on the attraction degree, and further improves the global optimization capability and optimization efficiency of the algorithm.
(3) In the classification and identification of the flame combustion state, the kernel parameters of the wavelet support vector machine are optimized by adopting an improved firefly algorithm, so that the method has stronger flame combustion state identification capability and faster convergence speed.
(4) The intelligent flame combustion state identification method for the fire-fighting robot extracts the global and local characteristics of flame by adopting the rotation, translation and scaling invariants of the radial Tchebichef moment, is not influenced by the position and direction of the flame in a visual field scene and the distance between the flame and image equipment, iteratively optimizes the nuclear parameters of a wavelet support vector machine based on an improved firefly algorithm, has high algorithm optimization efficiency and strong identification performance, can effectively identify and classify the combustion state of the flame, and assists the fire-fighting robot to perform subsequent targeted fire-fighting operation.
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FIG. 1 is a schematic flow chart of an intelligent flame combustion state identification method of a fire-fighting robot according to the present invention;
fig. 2 is a schematic flow chart of wavelet support vector machine kernel parameter optimization based on the improved firefly algorithm in this embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1
The embodiment provides a fire-fighting robot intelligent flame combustion state identification method, which comprises the following steps:
an image acquisition step: acquiring a series of candidate images by using a camera to construct an image data set, and obtaining a test sample set;
a segmentation pretreatment step: processing the original image of the test sample set by adopting a color criterion segmentation rule based on a YCbCr color space to obtain a corresponding segmentation processing image, then carrying out edge detection processing on the segmentation processing image by using a Canny operator, and extracting the outline of a suspected flame area to obtain an edge image;
a flame characteristic extraction step: calculating rotation, translation and scaling invariants of radial Tchebichef moments of the segmentation processing image and the edge image, respectively obtaining an area moment invariant and a contour moment invariant, and combining the area moment invariants and the contour moment invariants to construct a multi-feature composite vector for describing flame features;
and (3) flame combustion state classification and identification: and inputting the multi-feature composite vector of the test sample set into a pre-established and trained wavelet support vector machine model, predicting a classification label, and finally obtaining a classification recognition result of the flame combustion state.
As a preferred embodiment, in the segmentation preprocessing step, the expression of the color criterion segmentation rule based on the YCbCr color space is:
Figure BDA0002697480630000051
Figure BDA0002697480630000061
in the formula, I (x, y) is a segmentation processing image, if a suspected flame pixel point exists at the original image coordinate (x, y), the gray value at the corresponding coordinate is 1, otherwise, the gray value is 0; y ismeanThe average value of Y channel components of a single original image is obtained; y (x, Y) is the gray value of the brightness component of the pixel point at the coordinate (x, Y) of the original image, Cb (x, Y) is the gray value of the blue chroma component of the pixel point at the coordinate (x, Y) of the original image, and Cr (x, Y) is the gray value of the red chroma component of the pixel point at the coordinate (x, Y) of the original image; λ is an adaptation coefficient, λ > 1; s is the total number of all pixels in a single original image, (x)i,yi) Is the coordinate of the ith pixel in the original image.
As a preferred embodiment, in the flame feature extraction process, the expression of extracting flame features by using rotation, translation and scaling invariant of the radial Tchebichef moment is as follows:
Figure BDA0002697480630000062
Figure BDA0002697480630000063
Figure BDA0002697480630000064
Figure BDA0002697480630000065
in the formula (I), the compound is shown in the specification,
Figure BDA0002697480630000066
rotation, translation and scaling invariants for the radial Tchebichef moment; t ispqA radial Tchebichef moment of order p + q; t is00A radial Tchebichef moment of zero order; n is the maximum resolution used in the angular direction; ρ (p, N) is the orthonormal squared norm;
Figure BDA0002697480630000067
a regularized Tchebichef polynomial of order p and length N; m is N/2, and N is the side length of the image; q is a non-negative integer, and q is not more than N-1; g (r, theta) is the input image after translation and mapping; theta is 2 pi l/n, l is more than or equal to 0 and less than or equal to n-1, and l is an integer; r varies from 0 to N/2 and theta varies from 0 to 2 pi.
In a preferred embodiment, the wavelet support vector machine model is a modified firefly algorithm-wavelet support vector machine model, and the construction and training process of the modified firefly algorithm-wavelet support vector machine model is specifically,
acquiring a training sample set through the image acquisition step, and processing the training sample set through the segmentation preprocessing step and the flame feature extraction step in sequence to acquire a multi-feature composite vector of the training sample set;
initializing a pre-established firefly algorithm based on the multi-feature composite vector of the training sample set, and performing iterative search optimization on the kernel parameters of the wavelet support vector machine in the firefly transfer process by the firefly algorithm until the preset maximum iteration times are reached to obtain the optimal kernel parameters, thereby constructing an improved firefly algorithm-wavelet support vector machine model.
Further, as a preferred embodiment, the kernel function of the wavelet support vector machine adopts a Morlet wavelet function.
Further, as a preferred embodiment, the firefly algorithm adopts an improved firefly algorithm, and the improved firefly algorithm adopts an improved step factor definition formula in an iterative search optimization process to realize the self-adaptive dynamic adjustment of the search step;
the improved step-size factor definition formula enables the step-size factor to be in a variation trend of increasing first and then decreasing along with the iterative search process of the improved firefly algorithm.
Further, as a preferred embodiment, the modified step-size factor defines the expression of the formula:
Figure BDA0002697480630000071
in the formula (I), the compound is shown in the specification,
Figure BDA0002697480630000072
the step size factor is the step size factor of the improved n +1 th iteration; n is the current iteration number;
Figure BDA0002697480630000073
is the final value of the step factor;
Figure BDA0002697480630000074
is the initial value of the step factor; n ismaxIn order to be the maximum number of iterations,
Figure BDA0002697480630000075
is the step size factor at the nth iteration after improvement.
Further, as a preferred embodiment, in the iterative search optimization process, the improved attraction degree definition formula is adopted in the improved firefly algorithm to realize adaptive dynamic adjustment of the attraction degree;
the modified attraction degree definition formula introduces a basic attraction degree different from zero as a minimum value of the attraction degree.
Further, as a preferred embodiment, the modified attraction degree defines an expression of the formula:
Figure BDA0002697480630000076
in the formula (I), the compound is shown in the specification,
Figure BDA0002697480630000077
the method is to improve the attraction degree of the firefly i to the firefly j; rho0The maximum attraction of the firefly i to other individuals; alpha is light absorption coefficient, alpha belongs to [0.1,10 ]]；rijIs the Cartesian distance between firefly i and firefly j; rhobIs the basic attraction degree.
Further, as a preferred embodiment, in the iterative search optimization process, the expression of the location update formula of the firefly i is as follows:
Figure BDA0002697480630000078
in the formula (I), the compound is shown in the specification,
Figure BDA0002697480630000079
is the space position coordinate vector of the firefly i at the n +1 th iteration,
Figure BDA00026974806300000710
is the space position coordinate vector of firefly i at the nth iteration,
Figure BDA00026974806300000711
is the space position coordinate vector of firefly j at the nth iteration, rand is in [0,1 ]]Random numbers are uniformly distributed in the interval.
The embodiment also provides an optimal implementation method, which includes the following specific implementation processes:
the flow of the fire-fighting robot intelligent flame combustion state identification method based on the radial Tchebichef moment invariant and the improved firefly algorithm-wavelet support vector machine is shown in figure 1, and the method specifically comprises the following steps:
s1: image acquisition
The method comprises the steps of collecting a series of candidate images by using a camera to construct an image data set, and dividing the collected image data set into a training sample set and a testing sample set, wherein the training sample set is used for constructing an improved firefly algorithm-wavelet support vector machine model, and the testing sample set is used for evaluating the performance of the model for recognizing the flame combustion state.
S2: segmentation pre-processing
S21: processing the original images of the training sample set and the test sample set by using a color criterion segmentation rule based on a YCbCr color space according to the following formula to obtain a segmentation processed image:
Figure BDA0002697480630000081
Figure BDA0002697480630000082
s22: in order to fully utilize the local characteristics of the flame shape subsequently, the Canny operator is used for carrying out edge detection processing on the segmentation processing image, the overall outline of the suspected flame area is extracted, an edge image is obtained, and segmentation preprocessing of the flame original image is completed.
S3: flame feature extraction
S31: calculating the radial Tchebichef moment rotation, translation and scaling invariants of each segmentation processing image and each edge image according to the following formulas to obtain corresponding area moment invariants and contour moment invariants:
Figure BDA0002697480630000083
Figure BDA0002697480630000084
Figure BDA0002697480630000085
Figure BDA0002697480630000086
s32: selecting area distance invariant and edge moment invariant with more than second order, and constructing a multi-feature composite vector F fused with global and local features:
Figure BDA0002697480630000091
s4: improved firefly algorithm-wavelet support vector machine construction
The wavelet support vector machine kernel parameter optimization process based on the improved firefly algorithm is shown in fig. 2, and specifically comprises the following steps:
s41: initializing an improved firefly algorithm, and setting initial values of algorithm parameters including the total number K of fireflies, the light absorption coefficient alpha and the maximum attraction rho0Final value of the step factor
Figure BDA0002697480630000092
Initial value of step factor
Figure BDA0002697480630000093
Maximum number of iterations nmaxAnd basic attraction degree ρb；
S42: randomly generating space position coordinate vector x of all firefly individualsn(n ═ 1,2, …, K), each spatial location coordinate vector consisting of the kernel parameters of the wavelet support vector machine;
s43: training a wavelet support vector machine to obtain a fitness function calculation value corresponding to each firefly individual so as to determine or update the corresponding absolute fluorescence brightness;
s44: sequencing all the individuals in the firefly population according to the absolute fluorescence brightness of the individuals, and taking the firefly individual with the highest absolute fluorescence brightness as the current global optimal feasible solution;
s45: evaluating the identity of each individual fireflyAbsolute fluorescent lightness of the front, and comparing the absolute fluorescent lightness L (x) of any two fireflies i and j in orderi) And L (x)j). If L (x)i)＜L(xj) Firstly, the improved step-size factor is calculated according to the following formula
Figure BDA0002697480630000094
And improved attraction
Figure BDA0002697480630000095
Then, updating the position of the firefly i according to a formula:
Figure BDA0002697480630000096
Figure BDA0002697480630000097
Figure BDA0002697480630000098
s46: judging whether the maximum iteration number is reached: if yes, outputting the value of the optimal kernel parameter, and performing step S47; if not, the process returns to S43.
S47: and training the wavelet support vector machine again by using the obtained optimal kernel parameters to obtain an improved firefly algorithm-wavelet support vector machine model.
S5: flame combustion state classification recognition
Inputting the flame characteristic vector of the test sample set into an improved firefly algorithm-wavelet support vector machine model, predicting a classification label of the class to which the test sample belongs, and realizing the identification of the flame combustion state; and the identification performance of the method is evaluated and analyzed by using the running time and the correct identification rate as evaluation indexes.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. An intelligent flame combustion state identification method for a fire-fighting robot is characterized by comprising the following steps:
an image acquisition step: acquiring a series of candidate images by using a camera to construct an image data set, and obtaining a test sample set;
a segmentation pretreatment step: processing the original image of the test sample set by adopting a color criterion segmentation rule based on a YCbCr color space to obtain a corresponding segmentation processing image, then carrying out edge detection processing on the segmentation processing image by using a Canny operator, and extracting the outline of a suspected flame area to obtain an edge image;
a flame characteristic extraction step: calculating rotation, translation and scaling invariants of radial Tchebichef moments of the segmentation processing image and the edge image, respectively obtaining an area moment invariant and a contour moment invariant, and combining the area moment invariants and the contour moment invariants to construct a multi-feature composite vector for describing flame features;
and (3) flame combustion state classification and identification: and inputting the multi-feature composite vector of the test sample set into a pre-established and trained wavelet support vector machine model, predicting a classification label, and finally obtaining a classification recognition result of the flame combustion state.
2. The fire-fighting robot intelligent flame burning state identification method according to claim 1, characterized in that in the segmentation preprocessing step, the expressions of the color criterion segmentation rules based on the YCbCr color space are adopted as follows:
Figure FDA0002697480620000011
Figure FDA0002697480620000012
in the formula, I (x, y) is a segmentation processing image, if a suspected flame pixel point exists at the original image coordinate (x, y), the gray value at the corresponding coordinate is 1, otherwise, the gray value is 0; y ismeanThe average value of Y channel components of a single original image is obtained; y (x, Y) is the gray value of the brightness component of the pixel point at the coordinate (x, Y) of the original image, Cb (x, Y) is the gray value of the blue chroma component of the pixel point at the coordinate (x, Y) of the original image, and Cr (x, Y) is the gray value of the red chroma component of the pixel point at the coordinate (x, Y) of the original image; λ is an adaptation coefficient, λ > 1; s is the total number of all pixels in a single original image, (x)i,yi) Is the coordinate of the ith pixel in the original image.
3. The intelligent fire-fighting robot flame combustion state identification method according to claim 1, wherein in the flame feature extraction process, an expression for extracting flame features by using rotation, translation and scaling invariants of the radial Tchebichef moment is as follows:
Figure FDA0002697480620000021
Figure FDA0002697480620000022
Figure FDA0002697480620000023
Figure FDA0002697480620000024
in the formula (I), the compound is shown in the specification,
Figure FDA0002697480620000025
rotation, translation and scaling invariants for the radial Tchebichef moment; t ispqA radial Tchebichef moment of order p + q; t is00A radial Tchebichef moment of zero order; n is the maximum resolution used in the angular direction; ρ (p, N) is the orthonormal squared norm;
Figure FDA0002697480620000026
a regularized Tchebichef polynomial of order p and length N; m is N/2, and N is the side length of the image; q is a non-negative integer, and q is not more than N-1; g (r, theta) is the input image after translation and mapping; theta is 2 pi l/n, l is more than or equal to 0 and less than or equal to n-1, and l is an integer; r varies from 0 to N/2 and theta varies from 0 to 2 pi.
4. The fire-fighting robot intelligent flame burning state recognition method according to claim 1, wherein the wavelet support vector machine model is an improved firefly algorithm-wavelet support vector machine model, and the construction and training process of the improved firefly algorithm-wavelet support vector machine model is specifically,
acquiring a training sample set through the image acquisition step, and processing the training sample set through the segmentation preprocessing step and the flame feature extraction step in sequence to acquire a multi-feature composite vector of the training sample set;
initializing a pre-established firefly algorithm based on the multi-feature composite vector of the training sample set, and performing iterative search optimization on the kernel parameters of the wavelet support vector machine in the firefly transfer process by the firefly algorithm until the preset maximum iteration times are reached to obtain the optimal kernel parameters, thereby constructing an improved firefly algorithm-wavelet support vector machine model.
5. The intelligent fire-fighting robot flame burning state identification method according to claim 4, wherein the kernel function of the wavelet support vector machine adopts a Morlet wavelet function.
6. The intelligent fire-fighting robot flame combustion state identification method according to claim 4, characterized in that the firefly algorithm employs an improved firefly algorithm, which employs an improved step factor definition formula in an iterative search optimization process to achieve adaptive dynamic adjustment of search step;
the improved step-size factor definition formula enables the step-size factor to be in a variation trend of increasing first and then decreasing along with the iterative search process of the improved firefly algorithm.
7. The intelligent fire fighting robot flame burning state identification method according to claim 6, wherein the improved step-size factor defines an expression of a formula as follows:
Figure FDA0002697480620000031
in the formula (I), the compound is shown in the specification,
Figure FDA0002697480620000032
the step size factor is the step size factor of the improved n +1 th iteration; n is the current iteration number;
Figure FDA0002697480620000033
is the final value of the step factor;
Figure FDA0002697480620000034
is the initial value of the step factor; n ismaxIn order to be the maximum number of iterations,
Figure FDA0002697480620000035
is the step size factor at the nth iteration after improvement.
8. The intelligent flame combustion state recognition method for the fire-fighting robot as claimed in claim 7, wherein the improved firefly algorithm adopts an improved attraction degree definition formula in an iterative search optimization process to realize adaptive dynamic adjustment of the attraction degree;
the modified attraction degree definition formula introduces a basic attraction degree different from zero as a minimum value of the attraction degree.
9. The intelligent fire-fighting robot flame burning state identification method according to claim 8, wherein the improved attractiveness defines an expression of a formula as follows:
Figure FDA0002697480620000036
in the formula (I), the compound is shown in the specification,
Figure FDA0002697480620000037
the method is to improve the attraction degree of the firefly i to the firefly j; rho0The maximum attraction of the firefly i to other individuals; alpha is light absorption coefficient, alpha belongs to [0.1,10 ]]；rijIs the Cartesian distance between firefly i and firefly j; rhobIs the basic attraction degree.
10. The intelligent flame combustion state identification method for the fire-fighting robot as recited in claim 9, wherein in the iterative search optimization process of the improved firefly algorithm, the expression of the position update formula of firefly i is as follows:
Figure FDA0002697480620000038
in the formula (I), the compound is shown in the specification,
Figure FDA0002697480620000039
is the space position coordinate vector of the firefly i at the n +1 th iteration,
Figure FDA00026974806200000310
is the space position coordinate vector of firefly i at the nth iteration,
Figure FDA00026974806200000311
is the space position coordinate vector of firefly j at the nth iteration, rand is in [0,1 ]]Random numbers are uniformly distributed in the interval.
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