CN109377711B - Fire hazard classification method and device - Google Patents
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Abstract
The invention provides a fire hazard classification method and a device, wherein the method comprises the steps of acquiring a spectral image set of a target area, wherein the number of spectral images in the spectral image set is not less than 100; analyzing the fire occurrence position points of each spectral image, and extracting the fire occurrence position points in the spectral images; calculating the times of fire occurrence position points of the pixels; counting the times of the fire occurrence position points of the image element points in the analysis result of the spectral image set by taking the image element points as research units; and carrying out fire hazard classification on the target area according to the times of the position coincidence of each pixel point and the position of the fire occurrence position. The invention originally provides a fire occurrence position point extraction algorithm and a fire occurrence position point number statistical calculation method, and a fire hazard acquisition algorithm taking a pattern spot as a unit, thereby obtaining a more scientific evaluation result of the fire hazard risk of the pattern spot.
Description
Technical Field
The invention relates to the field of fire prevention and control, in particular to a fire hazard classification method and a fire hazard classification device.
Background
A fire refers to a catastrophic combustion event that loses control over time or space. Among the various disasters, fire is one of the main disasters that threaten public safety and social development most often and most generally. The human being can utilize and control the fire, which is an important mark of the civilization progress. Therefore, the history of using fire by human beings and the history of fighting against fire are concomitant, people continuously summarize the fire occurrence rule while using fire, and the fire and the harm to human beings are reduced as much as possible. People need to escape safely and quickly in case of fire.
For better fire prevention and control, it is necessary to perform an evaluation of the degree of risk of fire and the probability of occurrence of the risk.
Disclosure of Invention
In order to solve the above technical problems, the present invention provides a fire hazard classification method and apparatus.
The invention is realized by the following technical scheme:
a method of fire hazard classification, the method comprising:
acquiring a spectral image set of a target area, wherein the number of spectral images in the spectral image set is not less than 100;
analyzing the fire occurrence position points of each spectral image, and extracting the fire occurrence position points in the spectral images;
calculating the times of fire occurrence position points of the pixels; counting the times of the fire occurrence position points of the image element points in the analysis result of the spectral image set by taking the image element points as research units;
and carrying out fire hazard classification on the target area according to the times of the position coincidence of each pixel point and the position of the fire occurrence position.
Further, the plurality of spectral images have the same specification, that is, the same length, width and resolution, and all correspond to the same target region.
Further, spectral images were obtained using MODIS.
Further, a spectral image statistical result and a preset judgment condition set are obtained, and fire occurrence position point detection is carried out according to the relation between the statistical result and the judgment condition set.
A fire hazard classification apparatus comprising:
the spectral image set acquisition module is used for acquiring a spectral image set of a target area;
the fire occurrence position extraction module is used for analyzing the fire occurrence position of each spectral image and extracting the fire occurrence position in the spectral images;
the counting module is used for calculating the times of the fire occurrence position points of the pixels;
and the grading module is used for grading the fire hazard of the target area according to the times of the position coincidence of each pixel point and the fire occurrence position.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the orientation or positional relationship indicated in the drawings, which are merely for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be construed as limiting the invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the invention, the meaning of "a plurality" is two or more unless otherwise specified.
In the description of the invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted", "connected" and "connected" are to be construed broadly, e.g. as being fixed or detachable or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the creation of the present invention can be understood by those of ordinary skill in the art through specific situations.
The invention has the beneficial effects that:
the invention provides a fire hazard classification method and a fire hazard classification device, which can obtain a scientific evaluation result of the fire hazard of a pattern spot by originally proposing a fire hazard occurrence position point extraction algorithm, a fire hazard occurrence position point number statistical calculation method and a fire hazard acquisition algorithm taking the pattern spot as a unit.
Drawings
FIG. 1 is a flow chart of a fire risk classification method according to the present embodiment;
FIG. 2 is a flowchart of a method for extracting a fire occurrence location point in a spectral image according to the present embodiment;
fig. 3 is a flowchart of a method for extracting a fire occurrence location point according to the present embodiment;
FIG. 4 is a flowchart of a fire risk classification method for a target area according to the number of times each pixel coincides with a fire occurrence location according to the present embodiment;
FIG. 5 is a block diagram of a fire hazard classification apparatus according to the present embodiment;
fig. 6 is a block diagram of a fire occurrence location point extraction module provided in the present embodiment;
fig. 7 is a block diagram of a fire occurrence location point extraction unit provided in the present embodiment;
fig. 8 is a block diagram of a grading module provided in the present embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below.
An embodiment of the present invention provides a fire risk classification method, as shown in fig. 1, including:
s101, obtaining a spectral image set of a target area, wherein the spectral image set comprises a plurality of spectral images.
Specifically, the plurality of spectral images have the same specification, i.e., the length, the width and the resolution are the same, and all correspond to the same target region. The greater the number of spectral images, the more accurate the fire hazard classification. The number of the original spectrum images is not less than 100 in the embodiment of the invention.
And S102, carrying out fire occurrence position point analysis on each spectral image, and extracting fire occurrence position points in the spectral images.
And S103, calculating the times of the fire occurrence position points of the pixels.
And counting the number of times of the position coincidence of each pixel point and the fire position by taking the pixel point as a unit, namely the number of times of the fire position.
And counting the times of the pixel points belonging to the fire occurrence position points in the analysis result of the spectral image set by taking the pixel points as research units.
And S104, carrying out fire hazard classification on the target area according to the number of times that each pixel point is superposed with the position of the fire occurrence position.
Obviously, the more times, the greater the probability of fire at the position of the pixel point.
Specifically, in the embodiment of the present invention, the spectral image is obtained using MODIS. The full name of MODIS is medium resolution imaging spectrometer. An important sensor carried by MODIS on terra and aqua satellites is a satellite-borne instrument which only broadcasts real-time observation data to the whole world directly through an x wave band and can receive the data free and use the data free, and MODIS data is received and used by many countries and regions all over the world.
Specifically, the performing of the fire occurrence location point analysis on each spectral image and extracting the fire occurrence location point in the spectral image as shown in fig. 2 includes:
and S1021, extracting suspected fire occurrence position points.
Let T4And T11Luminance temperatures of 4 and 11 microns for the picture element, respectively, if the picture element satisfies the following condition:
in the daytime: (T)4φ315K AND T4-T11φ15K)
Or the like, or, alternatively,
at night: t is4φ305KAND T4-T11φ15K
It is determined as a suspected fire occurrence location point.
And S1022, extracting fire occurrence position points.
Specifically, the extracting of the fire occurrence location point, as shown in fig. 3, includes the steps of:
s10221, taking a suspected fire occurrence position as a center, establishing a square observation window, and performing statistical analysis based on temperature characteristics on pixels in the observation window.
The following conditions are simultaneously satisfied in the observation window:
day time T4φ330K AND T4-T11φ15K
Or the like, or, alternatively,
at night: t is4φ315KAND T4-T11φ15K
The pixel of (1) is marked as a pre-judging fire occurrence position point, and other pixels in the observation window are marked as pixels to be counted. Setting the brightness temperature of 4 microns as the first brightness temperature and the brightness temperature of 11 microns as the second brightness temperature, recording
δ4、
δ11、ΔT、δΔTRespectively as follows:
calculating a first brightness temperature mean value of the pixel to be counted;
the average absolute deviation of the first brightness temperature of the pixel to be counted;
calculating a second brightness temperature mean value of the pixel to be counted;
the average absolute deviation of the second brightness temperature of the pixel to be counted;
calculating the average value of the first brightness temperature and the second brightness temperature of the pixel;
calculating the average absolute deviation of the first brightness temperature and the second brightness temperature of the pixel to be counted;
note also:
S10222, acquiring a judgment condition set.
Specifically, the set of judgment conditions includes the following judgment conditions:
jug 1: in the daytime: t is4Phi 365K or, night: t is4φ330K
Jug2：
Jug3：
Jug4：
Jug5：
Jug6：δ4′φ5.5K
S10223, acquiring the statistical analysis result and the condition that the judgment condition is consistent with the centralized judgment condition, and identifying the fire occurrence position.
Specifically, the fire occurrence location point identification algorithm is as follows:
jug1OR ((Jug2 AND Jug3 AND Jug4) AND (Jug5 OR Jug6)) OR (Jug2 AND Jug3 AND Jug4) is true, the suspected fire occurrence location point is identified as the fire occurrence location point.
Further, the fire hazard classification of the target area according to the number of times that each pixel point coincides with the position of the fire occurrence position is shown in fig. 4, and includes:
s1041, acquiring an image map of the target area.
S1042, calculating the sum of the times of fire occurrence position points corresponding to the pixels included in each pattern spot in the image map.
S1043, calculating fire danger values of all the pattern spots in the image map.
The same pattern spots belong to the same ground object, and the fire in the ground object can spread to the whole ground object, so the greater the pattern spots are, the greater the fire risk is. Specifically, in the embodiment of the invention, the fire risk value of the pattern spot is equal to the sum of the times of fire occurrence position points multiplied by the area of the minimum circumscribed rectangle of the pattern spot.
S1044, obtaining the fire hazard grade of the pattern spots according to the fire hazard values of the pattern spots.
Specifically, a boundary threshold value of each fire risk level may be set, and the fire risk level of the pattern spot may be obtained according to a corresponding relationship between the boundary threshold value and the fire risk value. The boundary threshold may be set manually, and is not particularly limited in the embodiment of the present invention.
A fire hazard classification apparatus according to an embodiment of the present invention, as shown in fig. 5, includes:
the spectral image set acquisition module 201 is configured to acquire a spectral image set of a target area, where the spectral image set includes a plurality of spectral images;
a fire occurrence location point extraction module 202, configured to perform fire occurrence location point analysis on each spectral image, and extract a fire occurrence location point in the spectral image;
the counting module 203 is used for calculating the times of the fire occurrence position points of the pixels;
and the grading module 204 is used for grading the fire hazard of the target area according to the number of times that each pixel point is superposed with the position of the fire occurrence position.
Wherein the statistical module 203 and the classification module 204 constitute a fire risk classification module.
The fire occurrence location point extraction module 202, as shown in fig. 6, includes:
a suspected fire occurrence location point extracting unit 2021 for extracting a suspected fire occurrence location point.
A fire occurrence location point extracting unit 2022 for extracting a fire occurrence location point.
As shown in fig. 7, the fire occurrence location point extraction unit 2022 includes:
and the statistical subunit is used for establishing a square observation window by taking the suspected fire occurrence position point as the center, and performing statistical analysis based on temperature characteristics on the pixels in the observation window.
A condition acquisition unit for acquiring the set of judgment conditions.
And the identification unit is used for acquiring the coincidence condition of the result of the statistical analysis and the centralized judgment condition of the judgment condition so as to identify the fire occurrence position point.
The ranking module 204, as shown in FIG. 8, includes:
the influence map acquiring unit 2041 is configured to acquire an image map of the target area.
And a sum value obtaining unit 2042, configured to calculate a sum value of the number of times of fire occurrence location points corresponding to the pixels included in each of the patches in the image map.
And a risk value calculation unit 2043, configured to calculate fire risk values of each pattern spot in the image map.
And the grade judging unit 2044 is used for obtaining the fire danger grade of the pattern spots according to the fire danger values of the pattern spots.
The inventive device embodiment and the inventive method embodiment are based on the same inventive concept.
Embodiments of the present invention also provide a storage medium, which can be used to store program codes used in implementing the embodiments. Optionally, in this embodiment, the storage medium may be located in at least one network device of a plurality of network devices of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed terminal can be implemented in other manners. The above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
It should be noted that: the sequence of the above embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
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 (1)
1. A method of fire hazard classification, the method comprising:
acquiring a spectral image set of a target area; the spectral image set comprises a plurality of spectral images;
analyzing the fire occurrence position points of each spectral image, and extracting the fire occurrence position points in the spectral images;
calculating the times of fire occurrence position points of the pixels; counting the number of times that each pixel point is superposed with the position of the fire occurrence position by taking the pixel point as a unit, namely the number of times of the fire occurrence position; counting the times of the fire occurrence position points of the image element points in the analysis result of the spectral image set by taking the image element points as research units; carrying out fire hazard classification on the target area according to the number of times of position coincidence of each pixel point and a fire occurrence position; the more the times are, the greater the probability of fire at the position of the pixel point is;
acquiring an image map of a target area;
calculating the fire hazard value of each pattern spot in the image map;
obtaining the fire hazard grade of the pattern spots according to the fire hazard values of the pattern spots;
the calculating of the fire risk value of each pattern spot in the image map comprises:
calculating the sum value of the times of fire occurrence position points corresponding to pixels included in each pattern spot in the image map;
the fire risk value of the pattern spot is equal to the sum of the times of fire occurrence position points multiplied by the area of the minimum circumscribed rectangle of the pattern spot;
the analyzing of the fire occurrence location point for each spectral image, and the extracting of the fire occurrence location point in the spectral image includes:
extracting suspected fire occurrence position points;
extracting fire occurrence position points;
the method for extracting the fire occurrence location point comprises the following steps:
establishing a square observation window by taking a suspected fire occurrence position point as a center, and performing statistical analysis based on temperature characteristics on pixels in the observation window; the following conditions are simultaneously satisfied in the observation window:
in the daytime, T4 is more than 330K AND T4-T11 is more than 15K
Or the like, or, alternatively,
at night: t4 > 315KAND T4-T11 > 15K
The pixels are marked as the points for predicting the fire occurrence positions, and other pixels in the observation window are marked as pixels to be counted; note the book
、
、
、
、
、
Respectively as follows:
calculating a first brightness temperature mean value of the pixel to be counted;
the average absolute deviation of the first brightness temperature of the pixel to be counted;
calculating a second brightness temperature mean value of the pixel to be counted;
the average absolute deviation of the second brightness temperature of the pixel to be counted;
calculating the average value of the first brightness temperature and the second brightness temperature of the pixel;
calculating the average absolute deviation of the first brightness temperature and the second brightness temperature of the pixel to be counted;
note also:
acquiring a judgment condition set; the judgment condition set comprises the following judgment conditions:
jug 1: in the daytime: t4 > 365K or, night: t4 > 330K
Jug2：
Jug3：
Jug4：
Jug5：
Jug6：
Acquiring the coincidence condition of the result of the statistical analysis and the centralized judgment condition of the judgment condition, thereby identifying the position point of the fire; the fire occurrence position point identification algorithm comprises the following steps:
jug1OR ((Jug2 AND Jug3 AND Jug4) AND (Jug5 OR Jug6)) OR (Jug2 AND Jug3 AND Jug4) is true, the suspected fire occurrence location point is identified as a fire occurrence location point;
the multiple spectral images have the same specification, namely the length, the width and the resolution are the same and correspond to the same target area; spectral images were obtained using MODIS;
and acquiring a spectral image statistical result and a preset judgment condition set, and detecting a fire occurrence position according to the relation between the statistical result and the judgment condition set.
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| CN110459030A (en) * | 2019-09-06 | 2019-11-15 | 深圳市瑞讯云技术有限公司 | The detection method and device of fire |
| CN112991670A (en) * | 2021-02-04 | 2021-06-18 | 西安美格智联软件科技有限公司 | Fire-fighting dangerous area classification management and control method and system, storage medium and processing terminal |
| CN113033391B (en) * | 2021-03-24 | 2022-03-08 | 浙江中辰城市应急服务管理有限公司 | Fire risk early warning research and judgment method and system |
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| JP3067285B2 (en) * | 1991-07-12 | 2000-07-17 | ホーチキ株式会社 | Fire detection device using image processing |
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